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Has Laudan Killed the Demarcation Problem?

Kirsten Walsh Bachelor of Arts (Hons) (Melb)

Submitted in partial fulfilment of the requirements of the degree of Master of Arts (with Advanced Seminars component)

History and Philosophy of Science The University of Melbourne

Australia

October 2009

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Abstract

The „Demarcation Problem‟ is to mark the boundary between things that are scientific and

things that are not. Philosophers have worked on this problem for a long time, and yet

there is still no consensus solution. Should we continue to hope, or must we draw a more

sceptical conclusion? In his paper, „The Demise of the Demarcation Problem‟, Larry

Laudan (1983) does the latter. In this thesis, I address the three arguments he gives for this

conclusion.

The Pessimistic Induction: From the failure of many specific past attempts at demarcation,

Laudan infers that all future attempts at demarcation will fail. For his argument to be fully

convincing, Laudan needs to show that each attempt has been a complete failure, and that

these failures have never led to progress in the theory of demarcation. I argue that many

past attempts at demarcation have only resulted in partial failure, and many of these failures

have led to some cumulative progress. So I think we can draw a more optimistic

conclusion: future attempts at demarcation may be even more successful than past

attempts.

The Pseudo-Problem: Laudan argues that the demarcation problem presupposes an

„epistemic invariant‟: something common to all and only the sciences, which makes them

epistemically special. But, says Laudan, this presumption is false – so, by definition, the

issue is merely a pseudo-problem. I find Laudan‟s argument unconvincing. I present

reasons for thinking that the demarcation problem does not, in fact, presuppose an

extremely simple epistemic invariant. Furthermore, there may still be a satisfactory,

moderately complex epistemic invariant to be found. So I do not think any false

assumption is presupposed.

The New Problem: Laudan argues that we should replace the original demarcation

problem with a new demarcation problem. I take this to be the problem of demarcating

between well-confirmed and ill-confirmed theories. I argue that scientific status is relevant

to the confirmation of theories, so the two problems are closely related. I also argue that

science has other purposes; so scientific status indicates other virtues besides well-

confirmedness. Thus we do want to know which theories and activities are scientific,

because this will help us to decide which theories and activities to pursue. So this new

demarcation problem is not a suitable replacement for the original problem.

My central question is „Has Laudan killed the demarcation problem?‟, and my answer is

„No!‟.

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Declaration

This is to certify that

(i) The thesis comprises only my original work except where indicated in the Preface;

(ii) Due acknowledgement has been made in the text to all other material used;

(iii) The thesis is 20,000-22,000 words in length, inclusive of footnotes but exclusive of

tables, maps, appendices and bibliography.

Kirsten Walsh

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Acknowledgements

This thesis is the culmination of my academic journey thus far. Starting out as a vague

question, „Is the demarcation problem worth solving?‟, it evolved into its present form. I

would like to thank a number of people who have contributed to the final result in many

different ways:

First and foremost, I sincerely thank my long-suffering supervisor Howard Sankey,

who has ploughed through various drafts, making critical suggestions and posing

challenging questions. He has motivated and encouraged me every step of the way – never

reproaching me when the necessity of doing paid work got in the way of progress. For

this, I am extremely grateful.

Special thanks go to Neil Thomason for inspiring me in the early stages, encouraging

me to present my work at conferences, and for reading an entire draft of my thesis during a

flight to the US! I also thank Jason Grossman for his kind comments and helpful

criticisms of Chapter One.

This thesis evolved into its present form during a summer I spent at the University of

Auckland under the supervision of Robert Nola who went above and beyond the call of

duty. I thank him not only for his helpful discussions and feedback, but also for showing

me around Auckland and the surrounds. I also thank Jan Crosthwaite, Rosalind

Hursthouse, and the University of Auckland for providing me with the Summer Research

Scholarship that made this work possible.

For providing numerous distractions, as well as their unfailing support, I thank the

History and Philosophy of Science (HPS) and Philosophy postgrads with whom I have

shared office space, morning tea, and jugs of beer. In particular I‟d like to thank: Conrad

Asmus for teaching me formal logic and reading a draft of my thesis; Alex Murphy for

insisting that I use correct grammar and punctuation; Tama Coutts for being interested in

everything philosophical; David Condylis for insisting that I should never trust a

philosopher named „Larry‟; Ned Taylor for telling me what physicists do; Suzy Killmister

for securing office space; and Kristian Camilleri and Steph Lavau for proving that theses

can be finished. Special mention goes to the participants of „Help! My Progress has

Stalled!‟ and „Operation Endless Victory‟: Aaron Retz, Bryan Cooke, Chris Soeterboek,

Paul Carter, Sergio Mariscal, Steph Lavau, Steven Kambouris, and Vicki Macknight – I

hope we are all victorious in the end!

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A very special thank you to Erik Nyberg, my partner, colleague and friend. He always

supported and encouraged me, stayed interested in the details of my interminable project,

and made many helpful suggestions – especially by playing the devil‟s advocate during the

editing of my final draft.

Finally, I thank my parents, Adrian and Meredith Walsh, who have offered their

unconditional support and gentle counsel at every turn of the road. Their foresight and

values paved the way for my privileged education.

I dedicate this thesis to Larry Laudan, for all the time we‟ve spent together.1

1 I haven‟t met Professor Laudan yet, but I hope to meet him one day!

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Table of Contents

Abstract ............................................................................................................................................... 1

Declaration .......................................................................................................................................... 2

Acknowledgements ............................................................................................................................. 3

Table of Contents ................................................................................................................................ 5

Table of Figures .................................................................................................................................. 8

0 Introduction .................................................................................................................................. 9

0.1 What is Demarcation? .......................................................................................................... 9

0.2 Laudan’s Three Sceptical Arguments ................................................................................. 11

1 The Pessimistic Induction .......................................................................................................... 13

1.1 Laudan’s Pessimistic Induction .......................................................................................... 13

1.2 Inductive Inference ............................................................................................................. 16

1.3 The Resemblance Assumption should be Rejected .............................................................. 19

1.4 Progress in the Philosophy of Method ................................................................................ 20

1.5 Reply: Scientific Method Changes ...................................................................................... 23

1.6 Reply: The New Tradition is not Epistemic ........................................................................ 27

1.7 Rejoinder: Testability is Epistemic ..................................................................................... 28

1.8 Rejoinder: The New Tradition is Progressive .................................................................... 29

1.9 Conclusion .......................................................................................................................... 32

2 The Pseudo-Problem .................................................................................................................. 34

2.1 Laudan’s Requirements and Pseudo-Problem.................................................................... 34

2.2 Requirement One: Accuracy ............................................................................................... 36

2.2.1 Objection: Demarcations can be legislative .............................................................. 36

2.2.2 Sub-conclusion: Reasonable accuracy is sufficient ................................................... 37

2.3 Requirement Two: Precision .............................................................................................. 37

2.3.1 Objection: Precise enough for the specific purpose .................................................. 38

2.3.2 Objection: Ordinary vagueness could be replicated .................................................. 39

2.3.3 Sub-conclusion: Moderate precision is sufficient ..................................................... 40

2.4 Requirement Three: Epistemic Superiority ......................................................................... 40

2.4.1 Objection: Only epistemic significance .................................................................... 41

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2.4.2 Objection: Indirect epistemic virtues ........................................................................ 42

2.4.3 Reply: Too many virtues ........................................................................................... 43

2.4.4 Reply: Too unreliable ................................................................................................ 44

2.4.5 Sub-conclusion: Indirect epistemic virtues are sufficient ......................................... 45

2.5 Requirement Four: Invariance ........................................................................................... 45

2.5.1 Variance and complexity........................................................................................... 45

2.5.2 Objection: An extremely complex invariant ............................................................. 48

2.5.3 Reply: The demarcation criterion must be simple ..................................................... 49

2.5.4 Rejoinder: Demarcation needn’t be extremely simple .............................................. 50

2.5.5 Sub-conclusion: Moderate invariance is sufficient ................................................... 52

2.6 The Moderate Demarcation Criterion ................................................................................ 53

2.7 The Existence of the Epistemic Invariant ........................................................................... 53

2.7.1 Objection: Rules don’t vary that much! .................................................................... 56

2.7.2 Objection: Ultimate goals might not vary ................................................................. 57

2.7.3 Sub-conclusion: Deep epistemic homogeneity ......................................................... 58

2.8 Conclusion .......................................................................................................................... 58

3 The New Problem ....................................................................................................................... 61

3.1 Laudan’s New Problem ...................................................................................................... 61

3.2 Accounts of Confirmation ................................................................................................... 62

3.2.1 Laudan’s views ......................................................................................................... 62

3.2.2 A simple account ....................................................................................................... 63

3.2.3 Sophisticated accounts .............................................................................................. 64

3.2.4 Overcoming underdetermination............................................................................... 65

3.3 Objection: Science is Relevant to Confirmation ................................................................. 66

3.3.1 Science is more than confirmation ............................................................................ 66

3.3.2 Types of relevance .................................................................................................... 66

3.3.3 No exhaustive relevance ........................................................................................... 67

3.3.4 Causal relevance ....................................................................................................... 68

3.3.5 Statistical relevance ................................................................................................... 69

3.3.6 Logical relevance ...................................................................................................... 71

3.3.7 Sub-Conclusion: Scientific status is strongly relevant .............................................. 71

3.4 Objection: Science has Other Purposes and Other Virtues ................................................ 71

3.4.1 Improving our understanding requires novelty ......................................................... 72

3.4.2 Improving our living standards requires usefulness .................................................. 72

3.4.3 Achieving confirmation requires testability .............................................................. 72

3.4.4 Pre-selection before confirmation ............................................................................. 73

3.4.5 Other virtues can outweigh confirmation .................................................................. 74

3.4.6 Reply: Pursuit versus acceptance .............................................................................. 74

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3.4.7 Rejoinder: Demarcation is still useful for pursuit ..................................................... 75

3.4.8 Rejoinder: Other virtues may affect acceptance ........................................................ 76

3.4.9 Reply: Science is all about confirmation ................................................................... 76

3.4.10 Rejoinder: Then science is relevant to confirmation! ................................................ 77

3.4.11 Sub-conclusion: Being ‘scientific’ indicates other virtues ........................................ 77

3.5 Conclusion .......................................................................................................................... 78

4 Conclusion................................................................................................................................... 79

Appendix A Some Non-Ideal Definitions .................................................................................... 80

A.1 Ideal Definitions ................................................................................................................. 80

A.2 Non-Ideal Definitions ......................................................................................................... 81

A.3 Gold .................................................................................................................................... 82

A.4 Science ................................................................................................................................ 83

A.5 Diamonds ............................................................................................................................ 84

Appendix B Some Complex Demarcations ................................................................................ 86

B.1 Thagard .............................................................................................................................. 86

B.2 Lugg .................................................................................................................................... 87

B.3 Derksen ............................................................................................................................... 88

Appendix C Other Virtues versus Confirmation ....................................................................... 90

C.1 Wonten versus Newton ........................................................................................................ 90

Bibliography ...................................................................................................................................... 93

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Table of Figures

Figure 1.1: Three possible views of demarcation. .......................................................... 20

Figure 1.2: Candidate rules in plausible levels of generality. ......................................... 22

Figure 1.3: Two inadequacies of the „eschew‟ proposal. ............................................... 22

Figure 1.4: A hierarchy of goals and methods. ............................................................... 26

Figure 1.5: Laudan‟s Old and New Traditions of demarcation. .................................. 28

Figure 1.6: Some methodological progress since Popper. ............................................ 31

Figure 2.1: Two epistemic virtues in a positive feedback loop. ................................... 45

Figure 2.2: My alternative requirements for a demarcation criterion. ......................... 53

Figure 2.3: The implications of heterogeneity for the invariant. .................................. 56

Figure 2.4: Some accepted rules and their opposites. .................................................... 57

Figure 2.5: My alternative interpretation and claim. ...................................................... 59

Figure 2.6: Two interpretations and my corresponding truth values. ......................... 59

Figure B.1 Thagard‟s two conceptual profiles. ............................................................... 86

Figure C.1 Two Laws describing the behaviour of apples. .......................................... 90

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0 Introduction

In 1983, Larry Laudan published a paper that he called „The Demise of the Demarcation

Problem‟ (Laudan, 1983), in which he argued that the problem is unsolvable. This is my

reply to Laudan‟s paper.

0.1 What is Demarcation?

To demarcate is, literally, to mark a boundary (Sykes, 1989). So a demarcation tries to sort

things into two mutually exclusive groups: things inside the boundary, and things outside

the boundary. If the boundary is not perfectly precise, then there may also be some

„borderline‟ things that are not clearly „in‟ or „out‟.

In the philosophy of science, „the problem of demarcation‟ is to mark the boundary

between things that are scientific and things that are not.2 Several different terms are

commonly used to describe the contrasting things that are not scientific, with slightly

different connotations: „non-scientific‟, „pseudo-scientific‟, and „unscientific‟. I do not think

the differences here are important to Laudan‟s argument or my reply.

Science has many aspects, including (but not limited to): (a) basic elements such as

theories, predictions, experiments, and results; (b) technical refinements such as

mathematical models and formulae, measurement tools, and statistical analyses; (c) general

virtues of theories such as confirmation, novelty, simplicity, explanatory breadth and

usefulness; and (d) social arrangements such as qualified experts, published journals, peer

review, large institutions, and competition for funding. We can try to demarcate between

what is scientific and what is not with respect to areas of knowledge, or with respect to any

of these particular aspects. I take it that all these demarcations should be closely related,

e.g. once we have decided that an area of knowledge is a science, we would probably

describe most of its theories, methods, instruments, and experts as scientific.

2 It is possible to propose logically weaker demarcation criteria that are only one-way: they either include

things as scientific, or they exclude things as non-scientific, but not both. One might also define other

weaker types of criterion. But traditionally philosophers have been concerned with strong criteria that are

two-way, applicable in all areas, and so on. Laudan clearly states that he is concerned with strong criteria of

this kind (Laudan, 1983: 119), and strong criteria will also be my concern. Note that if a strong criterion were

discovered, then this could also function as a logically weaker criterion (e.g. exclude non-sciences). So if my

defence of the possibility of demarcation is successful, then it holds for strong and weak criteria alike.

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We can sort things adequately for a specific purpose and context, but this sorting may

not be adequate for another purpose or context. What purposes could a demarcation of

science serve? Let us suppose that the successful demarcation is based on fundamental

features that make science epistemically superior, which gives us both a deeper theoretical

understanding of science and the practical ability to tell whether something is scientific.

Then it could be useful in several ways. Firstly, it could be of theoretical interest to

philosophers, e.g. to help them explain why science is epistemically superior. Secondly, it

could be of practical interest to non-scientists, e.g. to help them decide what research to

fund or who to trust. Thirdly, it could even be of practical interest to scientists themselves,

e.g. to help them improve their practices.3

Demarcation is described as a „problem‟ because it has proved to be very difficult –

and perhaps impossible – to achieve. Philosophers of science have worked on it for many

years. While many solutions have been suggested, and many philosophers think they have

solved it, no solution has been accepted by all or most philosophers of science. Therefore,

I shall assume, as Laudan does, that the demarcation problem is „unsolved‟.

In claiming that the demarcation problem cannot be solved, Laudan is rejecting all

forms of the problem. He is not distinguishing between demarcations that contrast non-

scientific, pseudo-scientific, or unscientific. He is not distinguishing between demarcations

that apply specifically to areas, theories, methods, people, etc. He is not distinguishing

between demarcations for one purpose or another. He is asserting that the solutions to all

of these variations on the demarcation problem are either impossible or unimportant. Like

Laudan, I will not focus exclusively on any of these particular variations on the problem. I

take it that they are all related. However, this variety does make it harder for Laudan to

establish that no useful version of the problem can be solved.

Is there any difference between a demarcation and a definition? A definition of what is

scientific also tries to sort things into two groups. Things are scientific iff they fit the

definition; things are not scientific iff they do not fit the definition. Again, there may be

borderline cases. Definitions can apply to any aspect of science, and be designed for any

purpose, whether theoretical or practical. I will not assume that the projects of

„demarcating‟ and „defining‟ what is scientific are exactly the same, but they are clearly

similar – I will argue that similar issues can arise.

3 For more detailed discussion of the purposes of demarcation see for example (Cioffi, 1970),

(Gardner, 1957), (Kuhn, 1996), (Resnik, 2000), (Ruse, 1982), and even (Laudan, 1983: 111).

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0.2 Laudan’s Three Sceptical Arguments

There are two possible reasons for the failure to solve the demarcation problem:

1. There is a solution out there – the reason we haven‟t found it (or accepted it) yet is

because philosophers have not been imaginative (or perceptive) enough; or

2. There is no solution to the demarcation problem – that‟s the reason we haven‟t found

one.

Laudan favours the second diagnosis, arguing that the demarcation problem hasn‟t been

solved because it is unsolvable.

Laudan gives three main sceptical arguments against the original demarcation problem:

A Pessimistic Induction – None of the many and varied criteria offered so far have

successfully demarcated science from non-science. Therefore, it is unlikely that there

will be any future success.

A Pseudo-Problem – The demarcation problem presupposes an accurate, precise epistemic

invariant in all and only science. There is no such feature. Therefore, the

demarcation problem is a pseudo-problem.

A New Problem – A better alternative to the demarcation problem is to identify theories that

are well-confirmed. We can (and should) evaluate confirmation without considering

scientific status.

Laudan concludes that terms such as „pseudo-science‟ and „non-science‟ do nothing but

rhetorical work. He recommends that philosophers and scientists trade-in their jargon and

rhetoric for sound argument and strong evidence. If we are serious in our quest to identify

superior theories, then we should evaluate theories solely on the basis of their empirical

and conceptual credentials, and their scientific status should be irrelevant (Laudan, 1983:

125).

It is important to recognise that Laudan does not claim that science doesn‟t exist. He

agrees that the terms „science‟ and „non-science‟ identify a genuine distinction, but he

argues that this distinction has no philosophical and epistemological significance (Laudan,

1983: 125).

In this thesis I reply to each of these three arguments in turn. My aim is modest:

refuting Laudan‟s arguments, rather than putting forward my own criterion. Other critics

of „Demise‟ have been more ambitious. They have attempted to refute Laudan‟s arguments

by counterexample: offering their own criterion as a new solution to the problem of

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demarcation (e.g. (Butts, 1993), (Lugg, 1987)). The problem is that unless such a criterion

is accepted, the refutation also fails. In contrast to these critics, the strength of my

refutation does not rely on any particular criterion being the final solution to the problem

of demarcation.

My central question is: „Has Laudan killed the demarcation problem?‟

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1 The Pessimistic Induction

1.1 Laudan’s Pessimistic Induction

Laudan wonders if a solution to the demarcation problem is still feasible, so he considers

past attempts at demarcation in order to shed some light on this question.

He initially considers the candidates proposed by Aristotle. Laudan tells us that the

task of identifying genuine knowledge had been attempted even earlier. But Aristotle‟s

focus was on scientific knowledge, and the solution he proposed was extremely influential.

Laudan says that Aristotle demarcated science from craft with his criterion of „knowledge of

first causes‟. This distinguished between „know-how‟ (the kind of knowledge a craftsman

has about how to build a boat that floats) and „know-why‟ (the kind of knowledge a

scientist has about why the boat floats). We can only arrive at scientific knowledge about

an event or behaviour (know why it has occurred) by tracing its causes back to first

principles (Laudan, 1983: 112-113).

According to Laudan, this candidate had a varied career. Initially, it served as grounds

for dismissing certain fields of inquiry as unscientific – for example, early mathematical

astronomy failed to qualify as a science because it didn‟t yield knowledge of first causes.

Instead, astronomers offered hypothetical models, which they sought to test by comparing

predictions made by their models with the observed positions of the planets. Laudan tells

us that it wasn‟t until the beginning of the seventeenth century that scholars started to

question this position on the scientific status of astronomy. Galileo, Huygens and Newton

wanted to give scientific status to many systems of belief that laid no claim to

understanding underlying principles or knowledge of first causes. Thus, „knowledge of first

causes‟ failed to become the accepted solution to the demarcation problem (Laudan, 1983:

113-114).

Laudan tells us that Aristotle also identified a second, complementary demarcation

criterion: he demarcated science from superstition with his criterion of „apodictic certainty‟.

He claimed that the product of scientific inquiry was demonstrably certain, i.e. infallible

(Laudan, 1983: 112). Infallibility and knowledge of first causes worked together as a two-

pronged demarcation: science can be distinguished from non-science both by the certainty

of its knowledge and by the basis of this knowledge in first principles (Laudan, 1983: 113).

But after the latter was rejected, infallibility became the sole criterion of demarcation.

Laudan notes that, for a while, infallibility was a great success: despite their disagreement in

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other areas, scholars of the seventeenth and eighteenth centuries widely agreed that

scientific knowledge was apodictically certain (Laudan, 1983: 114).

Laudan tells us that this criterion was finally rejected when scholars noticed that

existing theories were often amended or replaced by better theories – this could only occur

if the existing theories were false. It now seemed that few (if any) scientific theories were

infallible, and philosophers were forced to conclude that scientific knowledge was fallible

after all. Thus, Aristotle‟s „apodictic certainty‟ also failed to solve the demarcation problem

(Laudan, 1983: 114-115).

Laudan tells us that after the final defeat of Aristotle‟s twin criteria for demarcation,

philosophers considered methodology as a possible replacement. They aimed to show that

„the scientific method‟, although fallible, was a better way of testing empirical claims than

any other method. “And if it did make mistakes, it was sufficiently self-corrective that it

would soon discover them and put them right” (Laudan, 1983: 115). Furthermore, this

superior method was the thing that distinguished science from non-science, and made

scientific knowledge epistemically superior (Laudan, 1983: 115).

According to Laudan, while many philosophers believed in the methodological

criterion, they could not agree on the details of this method. The candidates for „the

scientific method‟ were diverse: some thought that scientists reasoned by induction; others

thought that scientists restricted their theories to what could be directly observed; and still

others thought that scientists preferred theories that successfully predicted novel facts.

Without agreement about the details of scientific methodology, philosophers were unable

to argue persuasively that methodology is what demarcates science from non-science.

Laudan also notes that most of the proposed methods failed to resemble the methods

actually used by scientists. So, for these two reasons, this approach failed before it got very

far. Firstly, philosophers were unable to identify the scientific method (that all and only

scientists used). Secondly, philosophers were unable to establish the superior epistemic

credentials of any of the methods considered (Laudan, 1983: 115-116).

According to Laudan, an entirely new approach to demarcation emerged early in the

twentieth century. This new approach equated science with meaningfulness: scientific

statements are those that have a determinate meaning. Philosophers argued that we can

establish whether or not a statement has a determinate meaning by deciding whether or not

the statement can be exhaustively verified. This approach became known as

Verificationism: a claim is scientific iff it can be exhaustively verified (or confirmed) by

empirical testing (Laudan, 1983: 120). Another candidate from the same period is

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Falsificationism: a claim is scientific iff it can be falsified (or refuted) by empirical testing

(Laudan, 1983: 121).

Laudan tells us that these candidates failed in two ways. Firstly, it wasn‟t the case that

all and only scientific statements were verifiable or falsifiable. Laudan remarks that (a)

there are clear examples of scientific claims that are not exhaustively verifiable or falsifiable,

(b) all non-sciences contain at least some claims that are verifiable (Laudan, 1983: 120), and

(c) falsificationism appears to give scientific status to any crank theory, as long as it makes

ascertainably false claims (Laudan, 1983: 120-121). Secondly, Laudan argues that the

notion of testability fails to identify what is important about science: being testable-in-

principle does not make a theory worthy of belief (Laudan, 1983: 122). Laudan argues that

these two candidates mark a significant shift in the approach to demarcation. Where the

earlier candidates attempted to identify an epistemological demarcation, these later

candidates attempted to identify a syntactic or semantic demarcation (Laudan, 1983: 121-

122).

Finally, Laudan wonders whether there are any promising candidates “waiting in the

wings” (Laudan, 1983: 122). He considers the following:4

Scientific claims are well-tested;

Scientific theories exhibit progress or growth;

Scientific theories make surprising predictions that turn out to be true;

Science is the only form of intellectual system building that proceeds cumulatively;

and

Science is the sole repository of useful and reliable knowledge.

He argues that none of these candidates is promising. Some scientific theories are highly

speculative, and therefore untested and unreliable. Some established scientific theories do

not progress rapidly or make lots of surprising, successful predictions. Finally, some

scientific theories do not contain their predecessors as special cases, therefore not all

scientific progress is cumulative. He concludes that none of these candidates identifies a

feature that is always or only displayed by science (Laudan, 1983: 122-124).

4 Laudan does not mention his own demarcation attempt, in which he offers problem-solving effectiveness as

the primary aim of science (Laudan, 1977). This is surprising, since he continues to develop this idea in later

years (e.g. (Laudan, 1990b)).

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After considering these past demarcation attempts, Laudan argues that all future

attempts at demarcation will probably fail. This argument looks like a pessimistic

induction,5 which contains at least this premise and conclusion:6

P1. All past attempts at demarcation have failed.

C. All attempts at demarcation will fail.

1.2 Inductive Inference

Inductive inference can be represented in the following form: 7

P1. All observed xs are Q.

C. All xs are Q.

Inductive arguments are not deductively valid, in that P1 does not entail C by the normal

rules of deductive logic. Rather, the conclusion is ampliative: it expands on what is

contained in the premises. Specifically, C has the same logical form as P1, but it has a

broader range of application, because it also includes unobserved xs.8 Hence, unlike

deductive inferences, inductive inferences are fallible: P1 may be true while C is false.

Although all inductive inferences are fallible, some seem more likely to fail than others.

Some inductions seem „good‟, and hence more convincing. Others seem „bad‟, and hence

less convincing. It is difficult to give precise, general rules for what constitutes a good or

bad inductive inference, but I take it that sometimes we can tell the difference.

5 This pessimistic induction about demarcation attempts should not be confused with Laudan‟s well-known

pessimistic induction to the conclusion that all scientific theories are false (e.g. (Laudan, 1981a: 121-124),

(Laudan, 1984: 121), (Psillos, 1999: 101)).

6 I have three reasons for interpreting this as a pessimistic induction. Firstly, Laudan does not make the

logical structure of his argument entirely explicit and clear, so to some extent I must address arguments that

are implicitly suggested by what he says. Secondly, Laudan does devote a large amount of space to describing

this historical sequence of failed demarcation attempts, and does conclude that all future attempts to identify

an “epistemic version of a demarcation criterion” will probably fail (Laudan, 1983: 124). This certainly

suggests a pessimistic induction, which I am entitled to discuss as an explicit argument. Thirdly, Laudan has

also advanced a similar argument in the past, namely his pessimistic induction about the truth of scientific

theories, so it is not unreasonable to attribute this kind of argument to him.

7 One might call Laudan‟s pessimistic induction a „meta-induction‟, because it is about Philosophy rather than

nature. However, I take it that meta-inductions, pessimistic inductions, and inductions about nature all have

a similar form.

8 So, C does entail P1, and hence is logically stronger than P1.

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Making this particular inductive inference is equivalent to assuming that unobserved xs

will resemble observed xs, at least with respect to the property Q. I shall call this the

„resemblance assumption‟ (a resemblance which is specific to each inductive argument).

We can replace any application of a rule of inductive inference with an additional premise

such as this, and thereby make the argument deductive. Of course, this does not make the

inference any less fallible; we have merely replaced an unreliable rule with an unreliable

premise.

There is some dispute over what additional premises, if any, should be present to make

a good inductive inference. I have used only P1 and C because they capture the basic idea,

on which everybody agrees: induction extrapolates from the observed to the unobserved.

Some philosophers, e.g. (Chalmers, 1999: 45-49), claim that (generally speaking) good

inductive inference requires this additional premise: „A large number of xs have been

observed under a wide variety of conditions‟. Certainly, an inductive inference is unlikely

to be good if we have observed only one x! If all of these many and varied xs (without

exception) are Q, then one usually has good reason to assume resemblance.9

In some cases, it is clear that the resemblance assumption is inappropriate. Consider

the following two scenarios:

Scenario 1: You and I are playing a game. There are four cups lined up on a table. You turn your

back while I hide a coin under one of them. The object of the game is for you to guess

which cup is hiding the coin. You point to the first cup and say, “Is it this cup?” and I

say “No”. You point to the second cup and say, “Is it this cup?” and I say “No”.

You point to the third cup and say, “Is it this cup?” and I say “No”. At this point,

you throw your hands up in the air in frustration and say, “I’m never going to get it!

Show me which cup it is.”

9 P1 demands that all of the xs are Q. But statistical inferences can be described as inductive, because they

extrapolate from the observed sample to the unobserved population. In such inferences, the requirement

that the observed xs are many and varied is replaced by an assumption that the observed xs are a random

sample from the population. The requirement that all the observed xs are Q is usually replaced by a premise

that some specific proportion of observed xs are Q. The conclusion then concerns the proportion of xs in the

population. But I can leave aside all the complications involved in statistical inferences, because Laudan‟s

induction seems to take the traditional, non-statistical form.

18

You have inferred that since all of your past attempts at identifying the correct cup have

failed, all of your future attempts at identifying the correct cup will fail. This inference is

a bad one! By a process of elimination, on your next guess you would have identified the

correct cup.

Scenario 2: You are helping a child who is learning how to tie his shoelaces. On the first day, he

tries and fails. On the second day, he tries and fails. On the third day, he tries and

fails. At this point, you conclude that he’s always going to fail, so you go out and buy

him shoes with Velcro fasteners instead.

You have inferred that since each of the child‟s past attempts to tie his shoelaces has

failed, all of his future attempts to tie his shoelaces will fail. This inference is a bad one!

When a child is learning to tie shoelaces, he will fail many times before he succeeds. If

he keeps trying, probably the child will eventually succeed.

While I do not have a general rule to tell me which inductions are good ones, I can still

tell in each of these cases that the resemblance assumption is inappropriate. In both of the

above scenarios, we have good reason to believe that the unobserved cases have a

significant chance of being different to those already observed. In the first scenario, we

know that one of the cups definitely has the coin in it. So, as each cup is eliminated, the

probability that the next guess will be correct rises. Thus, each new unobserved case is

more promising than the last, and the fourth guess is completely certain to be correct. In

the second scenario, although the child fails to tie his shoes each morning, we hope that he

is learning and improving each day. Indeed, it is reasonable to expect that over time the

child will learn how to tie his shoes. One could even argue that the use of the term „failed‟

is inappropriate here. It is not nuanced enough to convey the notion that each day the

child comes a bit closer to success: each day the failure is partial, not total. So, in both of

the above cases, we have good reason to reject the resemblance assumption.

These examples seem to illustrate a general principle. If relevant progress is being made

towards success, then there is often a good chance that success will eventually arrive,

despite a sequence of failures. Hence, in such cases we have good reason to doubt a

pessimistic induction.

In summary, inductive inference is equivalent to the assumption that unobserved xs

will resemble observed xs with respect to the property Q. There are appropriate and

inappropriate resemblance assumptions – and sometimes we can tell the difference.

Evidence of progress is one good reason to doubt a pessimistic resemblance assumption.

19

1.3 The Resemblance Assumption should be Rejected

Those who believe that the demarcation problem is solvable do not believe that all

attempts at demarcation will succeed; rather, they believe that an attempt will succeed. The

fact that all observed attempts at demarcation have failed is disappointing, but it does not

contradict this optimistic belief in eventual success. In fact, the history of demarcation

attempts may be encouraging if these failures were only partial, and there is some cumulative

progress. Laudan, in contrast, is arguing that the demarcation problem is unsolvable and

that all attempts at demarcation will fail. This resemblance assumption is dubious if we

have good reason to believe that philosophers are making relevant progress. So, to make

his pessimistic conclusion compelling, Laudan must assume that each attempt at

demarcation is a complete failure and that these failures have never led to progress.

I shall argue in the following sections that Laudan‟s bleak assessment of philosophical

progress is not correct. Many past attempts at demarcation have resulted in failures that

are only partial, and many of these failures have led to some cumulative progress. In fact,

one might make an optimistic induction: since many observed attempts have resulted in

significant progress, then many future attempts will also result in significant progress.

Since sufficient progress must eventually lead to success, one might then infer that

philosophers will eventually succeed. This optimistic resemblance assumption leads to the

opposite conclusion! However, I do not need to defend such an optimistic view in order

to refute Laudan‟s pessimism. I only need to establish that some progress is evident. This

leaves the future of the demarcation problem unclear (at worst), with a significant chance

that philosophers will eventually succeed. This is sufficient to establish that Laudan‟s

inductive argument is unconvincing.

I summarise these three possible views of demarcation in Figure 1.1. I argue that

Laudan‟s pessimistic assumptions are untrue (so I place an F for false next to these items),

and therefore it is not clear that we will never succeed (so I place a question mark here). I

claim that the optimist‟s assumptions are true (so I place a T for true next to these items),

but it is still not clear that we should accept the optimistic prediction that we will eventually

succeed (so I place another question mark here). I argue that a mixed assessment of the

situation is warranted, and therefore we should conclude (at worst) that the outcome is

uncertain (so I place a T next to all these items).

20

Assessment Local Result Global Result Future Prediction

Pessimistic Complete Failure F No Cumulative Progress F Never Succeed ?

Optimistic Significant Success T Cumulative Progress T Eventual Success ?

Mixed Mixed Results T Mixed Results T Outcome Uncertain T

Figure 1.1: Three possible views of demarcation.

1.4 Progress in the Philosophy of Method

Laudan tells us that prior to the nineteenth century, Aristotle‟s „Apodictic Certainty‟ was

considered to be the definition of science. However, the replacement of several well-

developed scientific theories by new theories left philosophers with no choice but to

conclude that scientific knowledge was fallible after all. From my point of view, this failure

of Apodictic Certainty was progressive: it led directly (by elimination) to a correct belief

about science.

In any case, nineteenth-century philosophers turned to „the scientific method‟ to do the

job. They thought that the method used by scientists was fallible, but nonetheless superior

to methods used by non-scientists; and hence was an adequate demarcation criterion.

Laudan says that for this approach to succeed, philosophers needed to complete two tasks

(Laudan, 1983: 115):

1. Identify a method that all and only scientists follow; and

2. Justify this method by appealing to its superior epistemic status.

According to Laudan, philosophers could never deliver on either of these tasks because of

their lack of agreement about the basic tenets of the scientific method (Laudan, 1983: 115-

116). Without such agreement, philosophers were unable to argue persuasively that

superior method is what demarcates science from non-science. Furthermore, proposed

methodological rules were either incomprehensible or too complicated to follow: Laudan

identifies a rule instructing one to “eschew theoretical entities” (Laudan, 1983: 116) as

typical of this era. Finally, Laudan tells us that „the scientific method‟ was never adequately

21

justified because philosophers had no good reason to prefer one proposed „scientific

method‟ over another – or to any „unscientific method‟.

Laudan is surely correct in saying that the rule „one ought to eschew theoretical entities‟

is a failure. However, he seems to assume that this kind of rule is symptomatic of the

failure of the entire approach. Moreover, he appears to think that the philosophical theory

of scientific methodology as an approach to demarcation („methodology‟ for short) was a

dead-end, and that the astute philosopher should have realised this in advance (Laudan,

1983: 115). He notes that several of his contemporaries, who he regards as “respectable”

philosophers (Laudan, 1983: 115), approach demarcation in this way; but he doesn‟t discuss

any of their developments. Presumably, he doesn‟t see them as progressive. I claim that

Laudan‟s dismissal of methodology is premature. In fact, recent developments in

methodology have shed some light on the failures of nineteenth-century attempts to

demarcate between science and non-science.

One might wonder which less-than-astute philosophers continued to work on

methodology. In fact, one of them was Laudan. One year after „Demise‟, Laudan

published a book called Science and Values (Laudan, 1984) in which he proposed a theory of

scientific methodology. He developed his methodology in subsequent papers (e.g.

(Laudan, 1987)). He argued convincingly that scientific method is „goal-directed‟. Laudan

claimed that methodological rules make no sense as isolated statements of the form: One

ought to do x. Rather, they should be regarded as conditional statements of the form: If one’s

goal is y, then one ought to do x. So, particular methods can be justified by a rule such as this:

As long as one’s goal is y, and one believes that doing x is more likely than any alternative method to

produce y, then one is justified in doing x (Laudan, 1987: 203).

These points have been accepted by many philosophers of science (e.g. (Sankey, 2000),

(Worrall, 1988)), and may be regarded as developments in the field. Even if philosophers

didn‟t know what was wrong with the „eschew‟ rule at the time, we can now identify at least

one problem: it‟s not explicitly related to a goal, and it‟s not clear what kind of goal would

really justify this rule.

Another development in methodology was made by Rosenberg (1985), who argued

that there are levels of method: some rules are more general than others. We can

distinguish between, say, rules that give general advice about how to proceed, and rules that

specify particular actions that ought to be taken. For example, in Figure 1.2 I have set out

some candidate rules arranged in plausible levels of generality. This shows us something

22

else that was wrong with the „eschew‟ rule: it is not clear what particular actions should

follow from this general rule, i.e. how we could possibly implement it.

Figure 1.2: Candidate rules in plausible levels of generality.

Laudan attributes the failure of the „eschew‟ rule to the fact that it “involved complex

conceptions which neither scientists nor philosophers of the period were willing to

explicate” (Laudan, 1983: 116). This is a good enough reason to reject the rule: if a

methodological rule is such that we cannot tell when it is being followed and when it is

being flouted, then it is of little use to philosophers or scientists. However, this third

criticism (while valid) only tells us that good rules need to be spelled out more clearly.

Compared to the previous two criticisms, it doesn‟t provide us with much new information

about what methodological rules should look like.

Taken on its own, the failure of the „eschew‟ rule is scarcely progressive. It eliminates

one candidate rule, but this still leaves many other possibilities. There does not seem to be

any „good bit‟ that we can take from it! However, philosophers have now learned some

general lessons that would avoid such rules. These serve to identify at least two reasons

why the „eschew‟ rule was a failure (as depicted in Figure 1.3):

1. It‟s not clear what kind of goal would justify this rule; and

2. It‟s not clear what particular actions should follow from this rule.

Figure 1.3: Two inadequacies of the ‘eschew’ proposal.

Avoid forming inaccurate

conclusions

Avoid incorrectly rejecting the null

hypothesis

Avoid experimenter and placebo effects

Avoid making inaccurate

measurements

Always select a low p-value

Always perform double-blind tests

Always calibrate a pH-meter against

distilled water

One ought to eschew

theoretical entities. ACTIONS GOAL

? ?

23

Presumably, Laudan selected the „eschew‟ rule because it is one of the worst rules that

methodology has ever offered. Its flaws are supposed to illustrate the failure of the entire

nineteenth-century methodological approach. But, as I have demonstrated, even this rule

can be used to illustrate my point: since the nineteenth century, progress has been made in

methodology.

1.5 Reply: Scientific Method Changes

I have presented the history of change in „methodology‟ as a positive – i.e. philosophical

accounts of „the scientific method‟ are progressing towards a better understanding of it.

However, one implication of the philosophical developments I have discussed is that

scientific methods themselves may well change over time. This should occur when

scientists develop better techniques for achieving their goals, and does occur frequently

with statistical and experimental techniques.

Laudan sees this kind of methodological change as a negative. He argues that if

everything about science changes, then we cannot give an enduring definition of science

(Laudan, 1987: 214).10 If the change is completely pervasive (so that not even the nature of

the changes is constant), then this is surely correct. So, if we hope to demarcate between

science and non-science, then we would like our account of science to:

a) Account for the variation (over time, between disciplines, etc); and

b) Explain the common aspects (over time, between disciplines, etc).

In (b), we would be identifying some characteristic general things that don’t change.

A plausible response to Laudan‟s objection is that the most general things don‟t

change, even if the details do. Specifically, one might argue that we can identify an

appropriate overall goal (or set of goals) for science. I shall call these „ultimate goals‟. If

the ultimate goals don‟t change over time and across disciplines, then this might be

sufficient. These goals would need to be very general in both their applicability and

attractiveness, since they would need to be goals to which all scientists are at least

superficially committed, despite the disparity of their particular practices. There is one

obvious candidate: a complete, true theory of the world (and preferably, one that is easy to

understand!). The goal of truth is applicable to all areas of science, because regardless of

which aspect of the world is under investigation, scientists can develop theories attempting

10 This is not an argument he makes in „Demise‟. However, because it is relevant to his pessimistic comments

on methodology, I shall digress briefly to examine this argument.

24

to describe this aspect (which are then true or false). Truth should also be very attractive in

any area, for two reasons. Firstly, a true theory offers genuine insight and understanding of

the world, which is often desired for its own sake. Secondly, a true theory allows practical

predictions and manipulations of the world for our benefit. It should be accurate not only

about phenomena we have already observed, but also about any other phenomena

described by the theory (which we have never observed). For the moment, let us suppose

that truth is the enduring ultimate goal of science.

As previously discussed, Laudan agrees that goals can constrain and regulate method.

To this end, he identifies a „naturalist justification‟ of method (Laudan, 1987: 206-207):

If actions of a particular sort, m, have consistently promoted certain cognitive ends, e, in the past, and

rival actions, n, have failed to do so, then assume that future actions following the rule „if your aim is e,

you ought to do m‟ are more likely to promote those ends than actions based on the rule „if your aim is

e, you ought to do n‟.

This justification requires that we can tell when e is achieved. But Laudan argues that

some goals are such that we cannot tell when they have been achieved. He describes these

goals as „transcendent‟,11 and he argues that truth is one such goal (Laudan, 1984: 50-55). If

e is transcendent, then we cannot know which actions can promote or achieve it.

Therefore, a complete, true theory of the world cannot be the e in Laudan‟s rule, and

cannot be the enduring, ultimate goal that motivates scientific methodology.

One natural response to Laudan‟s objection is to replace truth with attractive lower-

level goals that are not transcendent. For example, scientists may aim for theories that are

simple relative to their explanatory breadth, and/or make successful novel predictions.

These goals are more realistic in that (properly explicated) we can tell when they have been

achieved. I shall call these „applied goals‟. Applied goals can guide or constrain the

methodological rules scientists follow according to the naturalist rule for justifying method.

Replacing ultimate goals with applied goals is a move that won‟t appeal to everyone.

Most philosophers and scientists would agree that goals such as simplicity, explanatory

breadth, and successful novel predictions should be preferred to their respective opposites:

complexity, explanatory narrowness, and unsuccessful or unsurprising predictions.

However, many philosophers would be dissatisfied if there were no reason for this

11 Laudan actually uses the term „transcendental‟. But Kant introduced a distinction between „transcendental‟

and „transcendent‟ which I will follow here. Goals are „transcendent‟ if they are beyond human knowledge,

and this is what Laudan wishes to say about truth.

25

preference. For example, it would be nice if we could justify simplicity on the grounds that

nature tends to be simple, and hence simpler theories are more likely to be true.

Alternatively, it would be nice if we could justify simplicity on pragmatic grounds, by

showing that simpler theories are easier to use. If either truth or utility is the ultimate goal,

then this would provide a good justification, because truth and utility both seem obviously

desirable. But otherwise, why should scientists prefer simplicity? It is not so obvious that

simplicity is desirable as an end in itself. It seems somewhat arbitrary as a goal for science,

and not one that is likely to unite scientists always and everywhere. Similar comments

could be made about other applied goals. Without a very desirable ultimate goal, they seem

inadequate to motivate a universal scientific method.

We could extend this argument even to the goal of confirmation, or „belief worthiness‟.

If a scientist aims for truth, then she has good reason to make sure her claims are

adequately justified or strongly supported by empirical evidence. If she does not hope to

advance empirical knowledge, then she needn‟t be concerned with justification or support

at all. In fact, she may prefer to make claims that are unjustified or contradicted by

empirical evidence, if only to make them more interesting! Without an ultimate goal such

as truth, it seems that scientists may have any applied goals whatsoever, and may change

goals whenever they wish. Scientific method remains unfixed and appears to lack all

epistemic credentials.

The universal-goal proposal faces an impasse: the achievement of proposed scientific

goals is either not adequately verifiable or not adequately attractive. However, there seem

to be (at least) two moderate paths we can take to avoid this difficulty.

Firstly, one could argue that empirical accuracy with respect to available data is the

most we can ever know we have achieved. If this is true, then current empirical accuracy

does seem to be the second-best ultimate goal. It does not offer a deep understanding of

the world. But an accurate description of observed phenomena would be all the truth we

can be certain we have obtained. Moreover, it should still allow us to successfully predict

and manipulate the world. We can reasonably expect that a well-tested theory would be

accurate (at least) about the kinds of phenomena we have already observed, even if we have

less reason to feel confident about predicting kinds of phenomena we have never observed.

Therefore, this kind of empirical accuracy seems appropriate as an ultimate goal.

Presumably, empirical accuracy (relative to currently available empirical results) is such that

we can recognise it when we see it. In this case, we may empirically test the correlation

26

between this ultimate goal and the applied goals, and justify them using Laudan‟s naturalist

justification.12

Secondly, one could argue that truth is not always transcendent. I think I can tell when

some everyday claims are true. Perhaps we can also tell when some general theories are

true. For example, consider Harvey‟s conjecture that the function of the heart is to pump

blood around the body (Schultz, 2002). This is now accepted as definitely true, and it‟s

hard to imagine how Laudan could motivate reasonable doubt about this claim.

Admittedly, it is not clear that all our theories will eventually be so certain. For example,

consider the best general theories in Physics. Can we ever be certain that they are true, and

not just predictively accurate? Perhaps this is the kind of example that Laudan had in

mind. Nevertheless, if we can sometimes tell what‟s true, then perhaps through experience

we may learn to recognise „truth indicators‟. These might be things like consistency,

simplicity, explanatory breadth, predictive ability, empirical support, and so on. In the

absence of certainty, we could use these indicators as a guide to which theories are more

likely to be true. On this proposal, the ultimate goal is truth – but wherever truth is

transcendent, we have good reason to prefer the applied goals of simplicity, explanatory

breadth, predictive success, and so on, to their opposites.

On either of these moderate proposals, the applied goals may change as we learn more

about science and about the world, but the ultimate goal of science stays the same.

Figure 1.4: A hierarchy of goals and methods.

12 Similarly, one might argue that Laudan‟s (1977 & 1990b) problem-solving effectiveness is not transcendent.

Therefore, as the primary aim of science, it may succeed where truth fails.

kjlk

Applied Goals

General Methods

Particular Methods

Ultimate Goals

27

The ideas I have discussed may be represented by the structure of scientific practice

depicted in Figure 1.4. In this structure, only the ultimate goals of science remain fixed

over time and shared by all scientists. The applied goals and methodological rules can

change over time and vary between disciplines (and even within a discipline).

Laudan views methodological variation as bad news for demarcation. If the goals and

rules at all levels of scientific practice vary, then we cannot give an enduring definition of

science. I have argued that even superficial allegiance to certain „ultimate goals‟ is enough

to „fix‟ science by constraining and guiding the kinds of methods that scientists use. This

doesn‟t sound like such bad news to me. It leaves open the possibility of an enduring

definition of science, and in particular, it leaves open the possibility of a demarcation

criterion that refers to methods and goals.

1.6 Reply: The New Tradition is not Epistemic

So far I have argued that the failure of nineteenth-century methodology was only partial;

and that subsequent work on methodology can be regarded as progressive. But Laudan

has another reason for making the resemblance assumption. He expects that future

attempts at demarcation will be even less successful than past attempts, because

philosophers no longer seek an epistemically significant distinction. According to this

argument, demarcation attempts that do not make the distinction epistemically significant

are complete and unprogressive failures.

According to Laudan‟s summary, originally the demarcation problem was firmly

grounded in epistemology. It was concerned with, for example, knowledge vs. opinion,

reality vs. appearance, and truth vs. error (Laudan, 1983: 112). Eventually, this problem

became related to the nature of scientific knowledge, and science has continued to be the

focus of the demarcation problem. Laudan distinguishes between the „Old Demarcationist

Tradition‟, characterised by Aristotle and the nineteenth-century methodologists, and the

„New Demarcationist Tradition‟, characterised by the Verificationists and Popper (Laudan,

1983: 112-121). Laudan argues that the shift from the Old Tradition to the New Tradition

might be viewed simply as a shift from epistemic to semantic strategies: the Old Tradition

was concerned with the epistemic merit of claims; the New Tradition was concerned with

the semantic structure of statements. But, Laudan argues, the shift is far more significant.

Philosophers from the Old Tradition were concerned with actual evidential support, and

they proposed criteria that were supposed to provide retrospective judgement of the

scientific status of theories. Philosophers from the New Tradition were concerned with

possible evidential support, and criteria were proposed that were supposed to judge the

28

scientific status of theories in advance. The Old Tradition equated science with belief-

worthiness, and hence it was concerned with actual epistemic warrant. However Laudan

argues that the New Tradition did neither of these things. Rather, the New Tradition saw

science only as knowledge claims that are testable in principle, and hence it was concerned

only with potential epistemic scrutability. Since „testable‟ claims are not necessarily worthy

of belief, any demarcation criterion proposed in the New Tradition, says Laudan, will fail to

make the demarcation between science and non-science epistemically significant. Laudan‟s

dichotomy is summarised in Figure 1.5.

Old Tradition New Tradition

Epistemic Semantic

Judges after empirical tests Judges before empirical tests

Seeks well-tested claims, i.e. actual empirical support

Seeks testable claims, i.e. potential empirical scrutability

e.g. Aristotle, 19th Century Methodology e.g. Verificationism, Falsificationism

Figure 1.5: Laudan’s Old and New Traditions of demarcation.

Laudan regards the New Tradition as having lost its integrity – it has downgraded

science to the point where scientific status has nothing to do with epistemic warrant, and

any „crank‟ theory can achieve scientific status. If this is true, then it is a strong argument

for the complete and unprogressive failure of methodology.

1.7 Rejoinder: Testability is Epistemic

Laudan contrasts „well-tested‟ with „testable‟. While well-testedness suggests strong

empirical support, and hence epistemic merit and belief-worthiness, he says “testability is a

semantic rather than an epistemic notion, which entails nothing whatever about belief-

worthiness” (Laudan, 1983: 121). I agree that testability is a semantic notion, since it

describes a property that statements have in virtue of their meaning. However, I claim that

testability has epistemic implications as well. A theory that is untestable could never be

tested, and hence, could never become well-tested. Testability is a necessary condition for

well-testedness. However, as Laudan says, being well-tested is closely correlated to the

epistemic virtue of „belief-worthiness‟. Thus, being testable has the epistemic implication

that a theory could become more „belief-worthy‟ through surviving tests. Indeed, it is

plausible that being testable is a necessary condition for a theory to be both empirical and

„belief-worthy‟.

29

Unlike Laudan, Popper certainly believed that testability had epistemic implications.

He made testability central to his theory of Falsificationism. Laudan dismisses

Falsificationism, yet Popper gave a persuasive account of how testability is relevant to

epistemic virtue. We can identify three distinct principles in Falsificationism, which are

evident throughout Popper‟s work (e.g. (Popper, 1959), (Popper, 1963)):13

1. Logical falsifiability: A theory must make predictions that could be proven false;

2. Methodological falsifiability: Proponents of a logically falsifiable theory must be willing

to test those predictions, and reject the theory if they prove to be false; and

3. Corroboration: A theory that has survived many attempts at falsification is

„corroborated‟, and it is rational to prefer corroborated theories to theories that are

uncorroborated.

Laudan focuses only on (1), and seems not to recognise that (2) and (3) are just as crucial to

Falsificationism. So he dismisses Falsificationism as if it only consisted of (1). However, as

Popper explains, (1) is just the first step towards (3). Corroboration, (3), must be seen as

an account of well-testedness, empirical support, and an attempt to explain how some

theories “exhibit a surer epistemic warrant or evidential ground” (Laudan, 1983: 118) than

other theories.14 It should be clear, then, that Falsificationism is at least partly epistemic in

approach. Laudan‟s dismissal of Falsificationism is based on a superficial and distorted

characterisation of Popper‟s views.

The fact that Laudan‟s dichotomy between the so-called Old and New Traditions

breaks down in the case of Falsificationism undermines the validity of his distinction. At

best, it must be fuzzy; at worst, there is no important distinction to be made.

1.8 Rejoinder: The New Tradition is Progressive

Laudan‟s history of past attempts at demarcation seems to finish with Falsificationism,

which he takes to be a complete and unprogressive failure. However, I now argue that

Falsificationism did not completely fail, because aspects of Falsificationism have been

13 Popper doesn‟t properly distinguish between (1) and (2). However, he does seem to advocate both at

different times. So I think it is safe to assume that he believes that both are necessary conditions to be a

falsifiable (and hence scientific) theory.

14 Despite Popper‟s scepticism about the idea of support, the notion of corroboration can be seen as quite

similar to the notion of confirmation. Viewed in this way, (3) is very similar to the thesis Laudan puts

forward at the end of his paper: that confirmation is what matters. I shall discuss this in Chapter 3.

30

retained in later methodological accounts. Moreover, the theory of demarcation has seen

much progress since Popper.15

Falsificationism did not completely fail. Rather, it partially failed – and so it failed to

be a completely satisfying account. Nevertheless, it should still be regarded as partially

correct. Many philosophers of science have retained at least some of Popper‟s claims in

their own accounts of methodology. For example, Cioffi (1970) sees falsifiability as a

crucial part of the „scientific attitude‟. Genuine scientists are those who are willing to test

their theories; pseudo-scientists are unwilling to risk falsifying their theories. Thus, for

Cioffi, falsifiability is a unique methodological and psychological property of science.

Similarly, Godfrey-Smith (2003) argues that it is a mistake to say that theories like Marxism

and Freudianism are themselves unscientific. He says we should distinguish between

scientific and unscientific “ways of handling ideas” (Godfrey-Smith, 2003: 71). Science, he

says, exposes ideas to the risk of falsification. Sober (1999) says a theory can be untestable

for many reasons: some theories are logically untestable; some theories are untestable

because of current technological limitations; some theories are untestable because the

necessary evidence no longer exists (e.g. some prehistoric evidence about dinosaurs); and

so on. It is the scientist‟s job to identify those theories that are testable right now: these are

the ones they should work on. Sober argues that selecting for current empirical testability

is unique to science. I am not suggesting that all these ideas are correct, or that any of

these ideas is, in itself, an adequate solution to the demarcation problem. My point is

simply that parts of Falsificationism have been employed in later accounts of science, and

many philosophers still think that these parts have significant merit.16 Therefore, it must at

least be plausible that Falsificationism has contributed to the progress of demarcation and

was not a complete failure – and Laudan certainly hasn‟t established the opposite.

I now give a very brief sketch of some significant methodological developments since

Popper, and the way in which earlier theories contributed to later ones in a progressive

15 I am not referring to the “candidates waiting in the wings” that Laudan mentions briefly and then rejects

(Laudan, 1983: 122-123). The accounts of science that I am referring to are sophisticated, developed, and

promising.

16 Even the „merely semantic‟ issue of falsifiability has been considered worth emphasising. Butts (1993) sees

texts as the appropriate unit of demarcation (rather than, say, theories, claims or methods). He argues that

scientific texts have only one permissible interpretation, and this seems to open them to criticism and

refutation, whereas pseudo-scientific texts have many permissible interpretations, which protects them from

criticism and refutation.

31

way. Figure 1.6 shows the connections between theories, where each arrow represents a

transmission of „good bits‟ from the earlier theory to the later theory.17

Figure 1.6: Some methodological progress since Popper.

1. My historical diagram begins with Inductivism, a very influential view that preceded

Falsificationism. It had many adherents, including J.S. Mill and R. Carnap.

2. There is a tentative (dashed) arrow between Inductivism and Falsificationism.

Popper developed Falsificationism in reaction to the earlier view. He objected to

the idea that we could regard scientific theories as probably true, based on inductive

inference. He took the opposite position: scientific theories always have a

probability of zero (Popper, 1959: vii). So, Popper presumably wouldn‟t have

thought that he had taken any „good bits‟ from Inductivism. However, the long-

running debate between Popper and Carnap suggests that there was enough

common ground between the two views to make the differences worth arguing

about.18 At the very least, Popper would have agreed that he had learnt from the

mistakes of Carnap, and so progressed in the opposite direction!

17 I will not give extensive evidence for these connections. Firstly, I must assume some familiarity with the

literature on methodology. Secondly, I‟m only claiming that my sketch is plausible, not that it is definitely

correct in every detail.

18 Earlier, I pointed out that corroboration is not as different from confirmation as Popper liked to think.

Inductivism

Experimentalism

Bayesianism

Lakatos’ Methodology

Falsificationism

Past

Present

32

3. Lakatos, in turn, saw his Methodology as building on Falsificationism (Chalmers,

1999: 130). Despite the differences between these two theories, it is easy to

recognise several „good bits‟ of Falsificationism retained in Lakatos‟ Methodology.

Principally, there is the same emphasis on making and testing novel predictions,

and looking favourably on a theory if these predictions are successful.

4. Bayesianism has been around a lot longer than my diagram suggests. However, as

an account of scientific reasoning, it is relatively recent. Dorling (1979) argued that

the „good bits‟ of Lakatos‟ Methodology are found in Bayesianism. In fact, as

Hacking argued, it seems the „good bits‟ of Falsificationism and Inductivism are

found in Bayesianism too (Hacking, 2001: 256-260).

5. Many philosophers of science (e.g. (Dorling, 1979), (Horwich, 1993), and (Howson

and Urbach, 1989)) believe Bayesianism shows a great deal of promise. However it

is not necessarily the end of the road. Some philosophers (e.g. (Mayo, 1996)) argue

that Bayesianism is just a general rule of rationality that says nothing about many

important aspects of science, such as experiments. So, I have tentatively added

Experimentalism to the chain of theories that represent progress in methodology.

Perhaps it can give an account of experiments in science, while making use of the

rules of rationality identified by Bayesianism.19

My objection to Laudan‟s pessimistic induction does not rely on this sketch being

accurate in every detail. In particular, it isn‟t supposed to be a complete survey of all major

theories: it omits, for example, the demarcation criteria of Kuhn (1996), Explanationism

(e.g. (Lipton, 2004)), and Laudan himself (1977). Nor does it rely on Bayesianism or

Experimentalism being the final solution to the problem of demarcation. I claim only that

something like this sketch is highly plausible, so that Bayesianism and/or Experimentalism

can be viewed as significant progress in methodology. If it is plausible that significant

progress is still being made, then we should not accept Laudan‟s pessimistic induction as

convincing.

1.9 Conclusion

To summarise, Laudan considers five attempts at demarcation, beginning with Aristotle

and ending (mysteriously) with Popper. From this extraordinarily brief history of

demarcation, Laudan makes a pessimistic induction: all past attempts at demarcation have

19 Some philosophers might disagree with my characterising Experimentalism as an improvement on

Bayesianism, but to discuss this further would be to go beyond the scope of this thesis and my expertise.

33

failed; therefore all attempts at demarcation will probably fail. To support this pessimistic

conclusion, Laudan assumes that each of these attempts has been a complete failure, and

that these failures have never led to progress in the theory of demarcation.

I argued that Laudan‟s assumptions are not correct. Many past attempts at

demarcation have only resulted in partial failure, and many of these failures have led to

some cumulative progress. I argued that (at worst) we should draw a more balanced

conclusion: the outcome of the demarcation debate is still uncertain. Firstly, I

demonstrated that recent accounts of scientific method are more sophisticated than

nineteenth century accounts, which suggests that methodology has progressed. Secondly, I

demonstrated that Falsificationism has some „good bits‟ that had been retained, and is not

merely semantic. So Laudan‟s distinction between the Old and New Demarcationist

Tradition is dubious, and this argument does not support his pessimism either. Finally, I

demonstrated that Laudan‟s history of demarcation attempts ends prematurely, since a lot

of progress has been made post Popper.

Therefore, I find Laudan‟s pessimistic induction unconvincing.

34

2 The Pseudo-Problem

2.1 Laudan’s Requirements and Pseudo-Problem

After his pessimistic induction, Laudan discusses what a solution to the demarcation

problem should look like.

Firstly, he says there are clear cases of science and clear cases of non-science, and a

demarcation criterion must agree with these. I shall call this requirement „accuracy‟, i.e. the

criterion accurately classifies the clear cases. Laudan notes that this requirement reflects a

difference between the current demarcation problem and, say, Aristotle‟s demarcation

problem (Laudan, 1983: 117). Aristotle had no cases of established science to consider, so

his demarcation criterion could be extremely legislative. He could specify criteria that were

displayed by no actual cases, i.e. his category „science‟ could be empty. In contrast, we have

many cases of established science (as well as non-science) to consider. We should only use

criteria that are displayed by these clear cases, and our demarcation criterion must be

accurate. Any criterion that does not accurately classify established science as „science‟ (and

established non-science as „non-science‟) will not be considered an adequate solution. For

example, if I were to propose a solution that classifies Quantum Mechanics and

Biochemistry as pseudo-science, or Numerology and Astrology as science, then my

solution would probably be considered a failure. I would have seriously misinterpreted

some of our most important paradigmatic cases. As Laudan says, “A failure to do justice

to these implicit sortings would be a grave drawback for any demarcation criterion”

(Laudan, 1983: 118).

Secondly, Laudan says there are a number of unclear or difficult cases, and a

demarcation criterion should also classify these as science or non-science (Laudan, 1983:

118). I shall call this requirement „precision‟, i.e. the criterion is so precise that it can

classify even unclear cases. Laudan claims that we need to be able to say in every case

whether or not something is science. A criterion that cannot perform this function is “no

better than no criterion at all” (Laudan, 1983: 118).

Thirdly, Laudan says that a demarcation criterion should make science epistemically

superior to non-science (Laudan, 1983: 118). I shall call this requirement „epistemic

superiority‟. There are various ways in which science might differ from non-science. For

example, scientists might make more money or know more mathematics than non-

scientists. However these distinctions are epistemically unimportant. We want to know

what (if anything) is special and superior about scientific knowledge. Any adequate

35

demarcation must explicate the general belief that science is superior in some way, e.g.

science has superior methods, stronger evidential grounds, or more reliable theories. This

requirement is not merely supported by a desire to vindicate normal judgements about

science (as in the requirement of accuracy). Laudan also argues that if it turns out that

science is not epistemically superior, then the demarcation between science and non-

science has no philosophical significance (Laudan, 1983: 118). Therefore, spending time

and effort trying to solve the demarcation problem presupposes (pragmatically) that science

is epistemically superior.

Finally, Laudan asserts that exactly the same demarcation criterion should demarcate all

cases, so that it identifies an “invariant” property (Laudan, 1983: 124). I shall call this

requirement „invariance‟. An invariant is, literally, something that does not change, and in

this context it is a property displayed by all and only cases of science. Laudan claims that

the demarcation problem presupposes that there is such an invariant property (Laudan,

1983: 124).

To summarise, Laudan has specified four requirements for an adequate demarcation

criterion:

1. Accuracy;

2. Precision;

3. Epistemic superiority; and

4. Invariance.

However, Laudan argues that there is no such criterion. We now know enough about

what passes for science, he says, to see that it is “not all cut from the same epistemic cloth”

(Laudan, 1983: 124). Some scientific theories are well-tested, but some are not. Some

scientific theories are making cognitive progress, but some are not – and so on. In fact, he

argues, there is nothing epistemically homogeneous about the variety of theories we

currently call „scientific‟. Therefore, no adequate criterion for demarcation exists (Laudan,

1983: 124).

36

The trouble is that the demarcation problem presupposes that such a criterion exists

(Laudan, 1983: 124). Since the problem presupposes a false assumption, it is a pseudo-

problem, and we ought to abandon it. This argument can be represented deductively:

P1. The demarcation problem presupposes that there is a particular epistemic invariant

that occurs in all and only science. (claim)

P2. The presupposition, that there is a particular epistemic invariant that occurs in all

and only science, is false. (claim)

P3. If a problem makes a false presupposition, then it is a pseudo-problem. (by

definition20)

C. The demarcation problem is a pseudo-problem. (from P1, P2 and P3)

2.2 Requirement One: Accuracy

Laudan says that we have a number of clear cases of established science and non-science to

consider. This is both a blessing and a burden. It is a blessing because we can derive a

demarcation criterion a posteriori from these cases – we don‟t have to solve the problem a

priori from first principles. It is a burden because our demarcation criterion must accurately

classify these clear cases (Laudan, 1983: 117-118). He says (1983: 117):

It is inconceivable that we would find a demarcation criterion satisfactory which relegated to

unscientific status a large number of the activities we consider scientific or which admitted as sciences

activities which seem to us decidedly unscientific.

2.2.1 Objection: Demarcations can be legislative

Deriving a demarcation criterion a posteriori from established cases seems to be a good way

to proceed. Generally, we have stronger intuitions about paradigmatic particular cases than

we have of abstract concepts or general principles. Moreover, the demarcation criterion

cannot disagree with too many clear cases, because then it would demarcate something

other than science.

However, once identified, this demarcation criterion may take on a „life of its own‟ – it

could become the accepted demarcation of science, and so provide a legislative function.21

20 Presupposing a false assumption is not supposed to be a necessary condition to be a pseudo-problem, only

a sufficient one. There may be other sufficient conditions to be a pseudo-problem. In fact, Laudan argues

that the demarcation problem satisfies two such conditions: it presupposes a false assumption; and also, there

is a better problem that should replace it.

37

So, if it turns out that it disagrees with a small proportion of previously clear cases, we

might keep it anyway. We might even revise our opinion of the conflicting cases, rather

than revising the criterion, until we reach a stable, consistent view.22 The philosophical

demarcation project does not presuppose that our naive intuitions will all be correct; it is

quite possible that we will need to rethink our assumptions about science. Therefore, perfect

accuracy is not required.

2.2.2 Sub-conclusion: Reasonable accuracy is sufficient

Laudan says that an epistemic invariant should be accurate – i.e. present in the clear cases

of science, and absent in the clear cases of non-science. Laudan does not appear to insist

on perfect accuracy, so I take it that he would agree with my suggestion that an epistemic

invariant should be reasonably accurate, but we can allow a little room for error.

2.3 Requirement Two: Precision

Laudan argues that we should be able to say in every case whether or not something is

science, even in unclear or difficult cases. He says, “without conditions which are both

necessary and sufficient, we are never in a position to say „this is scientific: but that is

unscientific‟ ” (Laudan, 1983: 119), moreover “the criterion must have sufficient precision

that we can tell whether various activities and beliefs whose status we are investigating do

or do not satisfy it” (Laudan, 1983: 118). To satisfy this requirement:

a) The epistemic invariant must be present in every case of science – including the unclear

ones;

b) The epistemic invariant must be absent in every case of non-science – including the

unclear ones; and

c) It must be evident in every case whether or not the epistemic invariant is present.

Laudan says the demarcation criterion needs to be more precise than our ordinary concept

of science.

21 In fact, this is Laudan‟s (1982) concern when he considers the Arkansas Creationism Trial. He argues that

Judge Overton‟s ruling rests on a demarcation that misrepresents what science is and how it works. He is

concerned that this ruling will set a precedent for other trials of this kind – and there seem to be a lot of them

in the USA – so he tells us that this ruling “may come back to haunt us” (Laudan, 1982: 48).

22 This is what Rawls (1999) called (in relation to moral theory) the process of reaching „reflective

equilibrium‟.

38

2.3.1 Objection: Precise enough for the specific purpose

To be precise enough for Laudan, an epistemic invariant will be present and discernible in

every case of science. To illustrate why this requirement might be too strong, I shall

consider an analogy with the demarcation of diamond.23

The criterion „pure carbon crystallised in octahedrons‟ demarcates diamond from all

imitations. But for some purposes, e.g. valuing jewellery, this demarcation won‟t do.

Identifying gem stones on the basis of their chemical structure is costly and invasive – it is

not something for which the valuer has the equipment or expertise. Instead, she must use

a number of distinguishing properties of diamond such as colour, weight, impurities,

hardness, sparkle, reflection and refraction. While these properties are typically displayed

by diamonds, some of these properties are indiscernible in some diamonds (depending on

the quality, cut and setting of the stone); conversely, some of these properties can be

discerned in some imitations. So the valuer‟s identifications must be performed in a

piecemeal fashion: she will continue to make observations until she is sure whether or not

the stone is a diamond. The chemical structure of diamond identifies a „theoretical

essential‟ that is present in every case of diamond, but it cannot be discerned in any case.

The valuer‟s „mixed bag‟ of tests identifies „practical indicators‟ that are not individually

present in every case of diamond, but some combination of these can be discerned in every

case.

Similarly, it is conceivable that an epistemic invariant may be present but indiscernible

in science. For argument‟s sake, suppose we define science as „an activity where the

fundamental aim is to generate novel, empirical truths‟.24 This criterion could be present in

every case, and yet not be discernible in any case. Looking at a set of practices, how can we

discern the fundamental aim? To identify particular cases of science, we might need to

identify practical indicators such as methods and procedures. So to tell in every case

whether or not something is science, we may need to employ a „mixed bag‟ of properties –

practical indicators rather than a theoretical essential.

This kind of demarcation, in which science is like diamond, should be sufficient for both

philosophical and practical purposes.25 For philosophical purposes, we might be most

interested in the theoretical essential. This satisfies Laudan‟s requirements (a) and (b), but

23 For a more detailed discussion of this analogy, see Appendix A, section A.5.

24 I do not endorse this simplistic definition.

25 There are several possible purposes for the demarcation of science, as identified in section 0.1.

39

not his requirement (c): it does not tell us in every case whether something is a science.

Nevertheless, it can provide some useful insight. For practical purposes, we might be most

interested in the practical indicators. These may be useful, by telling us in some cases

whether something is a science. But they do not seem to satisfy Laudan‟s requirements (a)

and (b), since different indicators are present in different cases. Nor will they necessarily

satisfy Laudan‟s requirement (c), since a mixed bag of indicators may not tell us in every case

whether something is a science.

This shows us two things. Firstly, the same concept – such as science – may require

different demarcations for different purposes.26 Secondly, an adequate demarcation

criterion does not need to be perfectly precise and give us all the answers, in the manner

that Laudan suggests. Demarcations only need to be precise enough to be helpful for the

specific purpose.

2.3.2 Objection: Ordinary vagueness could be replicated

A set of individually necessary and jointly sufficient conditions is usually considered to be a

very neat and precise way of defining a concept.27 While this level of precision can be

appropriate for defining geometrical and mathematical concepts, it often seems to be

inappropriate for defining ordinary concepts.28 In particular, most ordinary concepts admit

instances that have a vague or borderline status. Some philosophers argue (e.g. (Thagard,

1988)) that a set of individually necessary and jointly sufficient conditions cannot replicate

this vagueness; instead, it will increase the precision of the concept by specifying a sharp

line of demarcation. This will turn vague cases into either clear instances or clear non-

instances. Such a definition will either be too broad or too narrow, or it will fail to match

the degree of precision of the concept. If we just want to describe an ordinary concept,

then it is better to replicate its ordinary vagueness.

Many ordinary concepts seem to be defined by family resemblance: something is an x if it

is similar enough to other things that are an x (Rey, 1998). For example, Wittgenstein

famously claimed that game was defined by family resemblance (Wittgenstein, 1953: §66).

26 For a more detailed discussion of using different kinds of demarcation for different purposes (mostly

demarcations that are not perfectly precise), see Appendix A, sections A.3 and A.4.

27 I noted in section 0.1 that demarcations and definitions are similar. In this section I shall discuss the logical

structure of definitions, but I take it that similar issues arise with respect to the logical structure of

demarcations.

28 For more discussion of ideal definitions, see Appendix A, sections A.1 and A.2.

40

To be similar enough to other xs, presumably it must share enough of the same properties

(to a great enough extent). However there need not be any property that is common to all

xs, which is necessary to be an x. Furthermore, judgements of overall similarity are usually

not completely precise. Hence, there are often cases where we are not sure if something is

similar enough to be counted as an x. So, many ordinary concepts are vague, and in

particular, family resemblance concepts are very common and usually vague.

At least three kinds of definition could be used as a demarcation criterion:

1. A definition that specifies the ordinary meaning of the concept;

2. A definition that clarifies the concept; and

3. A definition that explicates the concept.

Since many ordinary concepts are vague, we should not expect the ordinary concept of

science specified in (1) to be perfectly precise. So, we might prefer to use something more

precise, and the form of (2) or (3). However, demarcating our ordinary concept of science

is of philosophical interest in itself, and for this purpose only (1) is correct. Furthermore,

(1) might still be helpful for some practical purposes, e.g. as a starting point for a legal

argument about „Creation Science‟. Therefore, for some purposes, we would settle for less

than perfect precision.

2.3.3 Sub-conclusion: Moderate precision is sufficient

Laudan says that a demarcation criterion must be very precise. To be precise enough, it

must be present and discernible in every case of science. My argument against perfect

precision is two-pronged. Firstly, I argued from the diamond example that it may not be

possible to specify an epistemic invariant that is present and also practically discernible in

every case. Nevertheless, imperfect precision can be helpful for specific purposes.

Secondly, I argued that ordinary concepts are often vague, but it is possible to specify

definitions that replicate this vagueness. We might want to describe the ordinary concept

of science, and this is quite likely to be vague – so for this purpose we would accept a non-

ideal, imprecise demarcation criterion. Thus, our minimal requirement for a demarcation

criterion should only be moderate precision.

2.4 Requirement Three: Epistemic Superiority

Laudan observes that there are many ways in which science differs from non-science. For

example, scientists often wear white lab coats, and tend to receive more public funding

than non-scientists. But these distinctions are not “philosophically interesting”. Laudan

41

says “we want to know what, if anything, is special about the knowledge claims and modes

of inquiry of the sciences” (Laudan, 1983: 118). So he says a demarcation criterion should

make science epistemically superior to non-science – epistemic superiority is

“philosophically interesting”. Moreover, he says, the demarcation problem presupposes that

science is epistemically superior to non-science.

2.4.1 Objection: Only epistemic significance

One might argue that working on the demarcation problem doesn‟t presuppose epistemic

superiority, only epistemic significance. By significance I mean that there is some difference

between science and non-science that is worth taking the trouble to identify. I can imagine

a philosopher presupposing that science is epistemically distinct in a significant way, but

not superior. They might advance the following argument:

To say that one activity is epistemically superior to another activity, one needs to be

able to compare them in terms of their epistemic merits. But in some cases, this may not

be possible. Suppose, for example, that we want to distinguish between History and the

sciences. We might say that they differ in their subject matter. History seems to be

concerned with the particular sequence of past events for which we have contemporary

human records. In contrast, the sciences seem to be concerned with general patterns in

events, which may well be replicated (in the past, present, or future). Consequently, the

appropriate methodology also differs. History tends to involve the collection and

interpretation of human records. The sciences tend to involve experiments to replicate

patterns of interest. If we say that the sciences are epistemically superior to History, then

this suggests that the more History was like a science, the better it would be. Yet if History

became too much like a science, then it would no longer be History! To avoid this,

perhaps we should say that the sciences are not epistemically superior to History, only

different.29

I do not need to evaluate whether this particular argument is convincing or the

conclusion is correct. My point here is that even if it is correct, this line of argument is

difficult to sustain for all other knowledge claims. While there are some activities that we

may be reluctant, or unable, to say are epistemically inferior to science – for example,

29 This demarcation between History and the sciences need not be completely sharp for this argument to

succeed. For instance, it might be conceded that scientific methods can sometimes be used in historical

enquiry, and that History would be better if this happened more often! Nevertheless, it might be argued that

History could never become entirely scientific.

42

Philosophy and History – most philosophers do not want to say that science is no better or

worse than anything. This is epistemic relativism, a view that is very difficult to defend (and

is usually held by academics who are particularly unscientific!). Some philosophers may be

willing to declare, in the abstract, that science is not epistemically superior to other

activities. But when it comes to particular cases, most philosophers of science want to be

able to claim, for example, that Western medicine is epistemically superior to Homeopathic

medicine, and that Evolutionary theory is epistemically superior to Creationism. Moreover,

it looks as though the distinction between Western medicine and Homeopathic medicine,

and the distinction between Evolutionary theory and Creationism, rests on more than the

simple fact that one is true or well-confirmed and the other is false or ill-confirmed. The

difference appears to run deeper: it is a distinction between two fundamentally different

ways of investigating phenomena or acquiring knowledge, and one of them seems to be

better than the other.

In practice, most demarcation attempts seem to presuppose superiority, not just

significance. The reputation held by science leads many philosophers to suppose that if

science is in any way distinct, it is also superior. Since I am sympathetic to that view

myself, and view epistemic relativism as very difficult to defend, I won‟t dispute Laudan‟s

requirement of epistemic superiority.

2.4.2 Objection: Indirect epistemic virtues

If science is epistemically superior to non-science, then presumably some of the distinctive

properties we need to identify are epistemic virtues. Laudan never explicitly says what kind

of property he would call an epistemic virtue. But from his paper, we can get a pretty good

idea:

1. Truth is an epistemic virtue.

2. Things are also epistemic virtues if they tend to produce truth in a very direct way.

3. No other things are epistemic virtues.

For example, on Laudan‟s conception, „being well-tested‟ is an epistemic virtue. If a belief

is well-tested, then presumably this makes the belief more likely to be true. So it qualifies

according to (2). I shall call all the things identified by this definition „direct epistemic

virtues‟.

A sceptic might object that being well-tested does not necessarily produce truth. For

example, a well-tested belief may not be true if a contrary belief has just been proposed that

is extremely plausible. Nevertheless, being well-tested tends to help. Hence, in this context,

it is the kind of „epistemic virtue‟ we need. If a demarcation criterion specified that

43

scientific beliefs have the virtue of being well-tested, then this would help to explain why

science is epistemically superior.

However, I claim that this notion of epistemic virtue is too narrow. It excludes some

properties that are actually very important to making science epistemically superior. For

example, selective funding, a large number and high quality of participants, vigorous

competition, systematic criticism and peer review are widely regarded as important features

of science. I suggest that these properties also tend to produce truth. But they do so more

indirectly, and may require appropriate circumstances to be effective (such as the presence

of other virtues). I shall call them „indirect epistemic virtues‟. Some might dismiss them as

merely social properties. But, in this context, they are the kind of „epistemic virtues‟ we

need. If a demarcation criterion specified that sciences have these social arrangements,

then this would help to explain why science is epistemically superior. So, when we take a

broader view of epistemic virtue, it is easier for a demarcation criterion to satisfy this third

requirement.

I have used counterexamples to argue that Laudan‟s concept of epistemic virtue is too

narrow. I do not claim to have a perfect definition of this concept, nor do I need to

provide one for my argument. But a better formulation would be:

1. Truth is an epistemic virtue.

2*. Things are also epistemic virtues if they tend to produce truth, directly or indirectly, in

appropriate circumstances.

3. No other things are epistemic virtues.

(2*) is rather vague: we need to use our judgement to decide what circumstances are

appropriate. But it is an improvement on (2), because it is much broader. (2*) can include

all the virtues identified by (2), and can also include other important factors like the social

arrangements I have specified.

2.4.3 Reply: Too many virtues

Laudan might reply that (2*) would count too many things as virtues: either a huge number

of things, or things that have only a very remote and weak tendency to produce truth.

My rejoinder is that we can include as many factors as we like, provided that we don‟t

regard them all as equally important for demarcation. We can identify some important,

distinctive virtues that tend to produce, in science, significantly more truth than occurs in

non-science. In contrast, we can dismiss many virtues as relatively unimportant, because

they are not distinctive to science and/or the comparative benefit they produce is small

44

(e.g. caffeine to combat drowsiness, or white lab coats for protection against chemical

spillage). Thus, we can focus on a manageable list of important virtues.30

2.4.4 Reply: Too unreliable

Laudan might reply that these „indirect epistemic virtues‟ shouldn‟t be counted as virtues at

all, because they aren‟t connected reliably enough to truth or justified belief.

Features such as white lab coats, textbooks and fume cupboards are all part of the

scientific tradition, but are relatively incidental to its epistemic virtue. They can easily be

imitated by pseudo-scientists, without making pseudo-science any more virtuous. Even the

social arrangements that I have mentioned could be imitated in this way, without bringing

any benefit. For example, if pseudo-scientific results are not judged by their accuracy,

novelty and usefulness, then selective funding might help pseudo-scientists to get more

results – but these results might not be of any real value.

I agree that compared to the direct virtues, it is easier for the indirect virtues to be

present without actually promoting truth or justified belief. To be effective, they may need

appropriate circumstances – including, perhaps, the presence of other virtues. Yet I

maintain that something like selective funding can still be counted as a virtue, because it is

effective in promoting good results when it is combined with the other scientific virtues

under normal circumstances. Under these circumstances, selective funding is positively

correlated to better performance.

Laudan might concede that in appropriate circumstances, the indirect virtues are

positively correlated to epistemic success. But he might say that they are merely „indicators‟

of epistemic virtue, not virtues in themselves.

My rejoinder is that the term „indicator‟ is too weak. Properties such as selective

funding do not merely indicate epistemic virtue; they generate epistemic virtue. Moreover,

their effectiveness often involves a positive feedback loop: every time they generate

epistemic virtue, it becomes more likely that they will generate epistemic virtue again. For

example, selective funding is not bestowed on everyone who asks for it. Researchers must

apply for grants. They must say what they plan to do with the money; how long it will take

30 Those philosophers who do not think it is realistic to aim for truth might think that this revised definition

would be even better if we substituted something else for „truth‟, for instance, „epistemic warrant‟. For my

purposes, I do not need to take sides on this issue. My point here is simply that, whatever the ultimate virtue

may be, we should take a broader view of what counts as an epistemic virtue (by including more things that

tend to produce the ultimate virtue).

45

to get results; how likely it is that they will get results; how useful their results will be; and

so on. If they are fortunate enough to receive funding, then they need to generate the

promised results. If they manage to generate the results, then they probably will get more

funding. If they don‟t generate the results, then they probably won‟t get more funding.

This virtuous (or vicious) cycle is depicted in Figure 2.1.

Figure 2:

Figure 2.1: Two epistemic virtues in a positive feedback loop.

Of course, this system is fallible. Success of grant applications depends a lot on the

quality of the application, who is applying, who is assessing the applications, and the nature

of the proposed work. Some proposals that would yield epistemic success are rejected in

favour of proposals that won‟t. But in the long run, I suggest that this system tends to

generate epistemic success. It is fallible, but nonetheless reasonably effective.

2.4.5 Sub-conclusion: Indirect epistemic virtues are sufficient

Laudan seems to assume that the only epistemic virtues in science are the direct epistemic

virtues. I have argued that there are important indirect epistemic virtues as well. So, we

should interpret „epistemic virtue‟ more broadly than Laudan. The „epistemic invariant‟

may include direct epistemic virtues, indirect epistemic virtues, or some complicated

combination of the two kinds of virtue. This gives us more latitude to find a suitable

demarcation criterion.

2.5 Requirement Four: Invariance

Laudan thinks that the same demarcation criterion should demarcate all cases, and do so by

picking out an „invariant‟ property. In this context, an invariant property is one that is

displayed by all and only cases of science. Laudan argues that the demarcation problem

presupposes that there is such an invariant property (Laudan, 1983: 124).

2.5.1 Variance and complexity

In order to determine whether or not the demarcation problem presupposes an invariant,

first we need to understand the possible logical structures of an invariant property. I take it

Selective Funding

Getting Results

46

that Laudan is imagining something like a property P, which corresponds to the property

of being science, S, as follows:

(x)(Sx↔Px)

So, property P is displayed by all cases of science and no cases of non-science. Now, if a

property was completely invariant, it would be displayed (invariably) by all cases. But note

that P is only displayed by some cases; i.e. cases of science. Technically, P is variant, not

invariant. The invariant here is really the meta-property Q, where:

(x)Qx

(x)(Qx ↔ (Sx↔Px))

According to logical principles, variance can be converted to invariance simply by

„going up‟ a level. I do not present this as an objection to Laudan, only as a technical

clarification of what he means. Presumably, when Laudan talks about an „invariant‟ he is

really talking about P: something that is invariably present in cases of science. i.e.:

a) (x)(Sx↔Px)

Equivalently, we can specify a rough definition of a suitable invariant, beginning with:

i. Invariant means „a property that is displayed by all and only cases of science‟.

One may well object that i is too narrow, since often a demarcation criterion tries to

specify a set of properties that are individually necessary and jointly sufficient. For

example:

b) (x)(Sx↔(P1x & P2x & P3x))

This would surely be sufficiently invariant for philosophical or practical purposes, but my

current formal definition of a suitable invariant i does not seem to reflect this.

There are two ways that I could correct my definition to respond to this objection. I

could say that an invariant is „either a single property that is displayed by all and only cases

of science, or else a conjunction of properties that are jointly displayed by all and only cases

of science‟. This would complicate my definition. The alternative is to say that the word

„property‟ is meant to be a broad term that includes both individual properties and

conjunctions of properties. According to logical principles, the technique we used to

convert variance to invariance can be used to describe a set of properties as a new single

property:

47

(x)(Sx↔Px)

(x)(Px ↔ (P1x & P2x & P3x))

In this sense (b) is just as invariant as (a), if we can count conjunctions of properties as new

properties. This would then preserve my simple definition of an invariant i, since (b)

becomes an example of i instead of a counter-example. So I will choose to clarify i with:

P. A property may be an individual property or a conjunction of other properties.

But, again, one might object that i is too narrow – since a reasonable demarcation

criterion could specify two properties that are individually sufficient, but not individually

necessary: only one or the other is necessary, but not both:

c) (x)(Sx↔(P1x P2x))

This disjunctive form seems to be sufficiently invariant to provide a decent definition of

science. But again, i does not seem to reflect this.

Similarly, a reasonable demarcation criterion could specify the inclusion of one

property and the exclusion of another. For example, Falsificationism tells us that all and

only cases of science are falsifiable but not yet falsified. If P1 is the property of being

falsifiable and P2 is the property of having been falsified, then we have:

d) (x)(Sx↔(P1x & ~P2x))

This certainly seems to fit our notion of a suitable invariant just as well as both of the

previous criteria, but i does not seem to reflect this, since i does not include negation.

Once again, there are two possible replies to these objections. I could complicate my

definition by saying that an invariant can be either a property or a conjunction of properties

or a disjunction of properties or the negation of a property. Or I could keep my definition

i and just say:

P*. A property may be an individual property, a conjunction of other properties, a

disjunction of other properties, or the negation of another property.

If we may combine properties to form complex properties, then presumably we may

combine complex properties to form even more complex properties. Certainly, my

definition P* allows this, because it can be applied repeatedly. So we can obtain:

48

(x)(Sx↔Px)

(x)(Px ↔ (Qx Rx Tx))

(x)(Qx ↔ (Q1x & Q2x & Q3x))

(x)(Rx ↔ (R1x & ~R2x))

(x)(Tx ↔ (T1x T2x))

So in this case,

(x)(Px ↔ ((Q1x & Q2x & Q3x) (R1x & ~R2x) (T1x T2x)))

One property can really stand for any number of other properties, in various logical

combinations. So any set of properties, no matter how complex, can be re-described as „a

complex property‟.

I have shown that through some principles of logic, we can „cover up‟ all manner of

variance and complexity:

1. Variance at one level can be converted to invariance at a higher level; and

2. Complexity at one level can be converted to simplicity at a higher level.

It looks as though Laudan‟s invariance requirement is easily achieved just by manipulating

the level of description. In this case, an epistemic invariant may be extremely complex at

one level, and yet extremely simple at another.

2.5.2 Objection: An extremely complex invariant

If we allow an unlimited degree of logical complexity, then one might argue that there must

be some complex property that is an invariant property of science. But if this were spelled

out in terms of simpler, recognisable properties, then what would such a complex property

look like?

It could be a complex property that contains as many properties as there are cases of

science. For example, if A is being Physics, B is being Chemistry, and C is being Biology:

(x)(Sx↔Px)

(x)(Px ↔ (Ax Bx Cx))

We could add as many disciplines as necessary, or identify what is scientific at the level of

sub-disciplines instead.

49

In this way, P could „pick out‟ all the cases of science – but Laudan wouldn‟t accept

this as an epistemic invariant, because A, B and C are names, not epistemic properties. To

get around this objection, we might be able to make P even more complex:

(x)(Sx↔Px)

(x)(Px ↔ ((A1x & A2x … Anx) (B1x & B2x … Bnx) (C1x & C2x … Cnx)))

Where A1 to An are the epistemic properties of Physics, B1 to Bn are the epistemic

properties of Chemistry, and C1 to Cn are the epistemic properties of Biology.

Presumably, these sets of properties would include methodological rules at various

levels of description. Since many non-sciences appear to employ „scientific‟ methods and

procedures, it would be challenging to specify enough properties to distinguish Physics,

Chemistry and Biology from these non-sciences. But we need not be restricted to such

methods: the subject matter might be used as one of the key distinguishing features.

2.5.3 Reply: The demarcation criterion must be simple

Laudan doesn‟t deny that it might be possible to specify all the methods and goals of the

various cases of science. But large and complex sets of properties would not satisfy his

notion of an epistemic invariant, even if they are re-defined as a single new property. His

idea of an epistemic invariant is a property that is genuinely extremely simple, before any

special logical trickery.31

Laudan would surely argue that proposing a complex property like this one as a

demarcation criterion would beg the question. The property is designed in an ad hoc way,

just to pick out all the cases of science. It appears that the only thing that makes the

selected cases count as science is really just the fact that we already think they are cases of

science. The complex property might include only epistemic properties, but there is no

prima facie reason for including those epistemic properties and excluding others. A

demarcation needs to give a deeper reason for its classification. The problem with a

complex property is that it is not clear why that particular set of properties should have any

special status, or even why they should be grouped together at all. A demarcation criterion

must do more than artificially „pick out‟ all the cases of science: it must unify them. An

extremely simple property (if there is one), will presumably identify a genuine commonality.

31 I take it that we can count some properties as genuinely more complicated than others. These can usually

be broken down into simpler properties, which in turn might also be broken down into even simpler

properties. It is difficult to say where this might end.

50

This argument is a strong one, and I accept that this kind of arbitrarily complex, ad hoc

property will not suffice. But must we except extreme simplicity?

2.5.4 Rejoinder: Demarcation needn’t be extremely simple

I imagine that Laudan would argue for the opposite extreme: the demarcation problem

presupposes an extremely simple property. Yet this view cannot be correct either.

If the demarcation problem presupposes an extremely simple property, then when we

examine the recent history of the demarcation problem, we would expect to see two things:

1. Most demarcation attempts specify extremely simple properties or demarcation criteria;

and

2. Any non-simple solutions have been criticised because they are not extremely simple.

Accounts of science can be roughly divided into monist accounts, i.e. one property

unites all cases of science, and pluralist accounts, i.e. there is no property that unites all

cases of science.32 On monist accounts, a demarcation criterion can be extremely simple;

and Laudan appears to assume that only monist accounts of science support demarcation.

As an empirical fact, it does seem that pluralism is correlated to epistemic relativism about

science.33 However, there are at least a few philosophers who support pluralist accounts of

science, and yet think we can demarcate science in a way that is epistemically significant. I

shall consider solutions proposed by three pluralists: Thagard, Lugg and Derksen.34 The

demarcation criteria they propose are more complex than those proposed by monists.

Thagard (1988) thinks that the various scientific disciplines, fields, programs and

theories display a „family resemblance‟. He specifies a „conceptual profile‟ for science and a

contrasting conceptual profile for pseudo-science (Thagard, 1988: 170), but he expects that

most cases of science will display some properties from both profiles. I consider Thagard‟s

demarcation relatively complex, for three reasons. Firstly, he uses more properties than,

say, Falsificationism. Secondly, some of these properties seem inherently quite complex.

Thirdly, the logical function required to capture „family resemblance‟ is considerably more

complex than, say, a conjunctive definition. Thagard thinks this complexity is unavoidable

32 This is a useful dichotomy, but not intended to be a sharp one. Sankey (2000) notes that various hybrid

theories of scientific methodology have been proposed.

33 Sometimes this relativism is intentional, e.g. (Feyerabend, 1975). Sometimes it seems to be unintentional,

e.g. (Kuhn, 1996).

34 For more detail of these complex solutions, see Appendix B.

51

– science is complex, so the demarcation criterion must be complex too. His conceptual

profiles have been criticised by many and accepted by few. I have not introduced them

here to add either my criticism or my support. Rather, I am interested to note the kinds of

criticisms that have been made. This solution has been criticised because it is not accurate

with respect to the clear cases of science and clear cases of pseudo-science (Derksen, 1993).

Thagard‟s solution has not been criticised because of its complex structure.

Lugg (1987) argues that pseudo-sciences can be identified as such because they are

structurally flawed, and each can be flawed in a different way. His demarcation criterion

could become extremely complicated: he might specify as many kinds of flaws as there are

cases of pseudo-science. Once again, this demarcation criterion has not been accepted by

the majority of philosophers. It has been criticised for its circularity and scientific prejudice

(Derksen,1993). But the point I wish to stress is that Lugg‟s solution has not been criticised

for its complexity.

Derksen (1993) argues that the key to distinguishing between science and pseudo-

science is to realise that pseudo-scientists are pretending or trying to be scientists. By

identifying the characteristic pretensions of the pseudo-scientist, i.e. “the seven sins of

pseudo-science”, we can distinguish between science and pseudo-science. Like Thagard, he

says these are not individually necessary conditions for pseudo-science; but they are jointly

sufficient. Derksen‟s seven-sin solution has not been accepted by the majority of

philosophers, and has been criticised for being just as circular and prejudiced as Lugg‟s

solution (Lugg, 1995). Again, the point I wish to emphasise is that Derksen‟s solution is

just as complex as those of Lugg and Thagard – yet no one has criticised it for its

complexity.

Philosophers are not always astute, but they are seldom stupid. If the demarcation

problem presupposes an extremely simple epistemic property, then we should expect to see

few, if any, complex solutions proposed. Yet I have outlined three complex solutions to

the demarcation problem that have been proposed – and there are several others.35 We

should expect to find that these complex solutions have been criticised for their

complexity. Yet the three complex solutions I have considered have not been criticised for

their complexity. They have been criticised because they are inaccurate, non-epistemic and

prejudiced – not because they are complex.

35 For example, (Resnik, 2000), (Reisch, 1998), (Kuhn, 1996).

52

One can certainly argue that the complexity of these criteria makes it more difficult to

apply them. For example, to assess Thagard‟s family resemblance we presumably need to

weigh up his multiple factors in an unspecified way, in order to decide if an area of

knowledge is scientific or unscientific. However, while this task may be difficult, it is not

obvious that a family resemblance criterion of this kind could never be a viable solution.

Therefore, Laudan has not established that such a solution is too complex to be viable.

Furthermore, there are many other possible solutions of this (or greater) complexity,

that Laudan has not managed to rule out. For example, suppose we developed some way

of measuring the degree to which various factors were present in an area of knowledge, and

some mathematical rule for how to combine them, which seemed to satisfy our intuitions.

Then we might have succeeded in developing a criterion that is more complex than

Thagard‟s, yet more precise and with a better-specified method of application. Laudan has

not established that all such criteria must be too complex to be viable solutions.

2.5.5 Sub-conclusion: Moderate invariance is sufficient

To summarise, Laudan argues that the demarcation criterion must be an invariant property.

I have argued that properties can be more or less variable, and that they range from

extremely simple to extremely complex. I have shown that any set of properties, no matter

how complex, can be described as a single property – and indeed, an invariant one.

So, one could argue that it is possible to specify an extremely complex set of epistemic

properties that functions as an epistemic invariant. However, this would probably not be

satisfactory. A demarcation criterion should identify some genuine „commonalities‟, and

not just an artificial, extremely complex invariant property.

I imagined that Laudan would argue for the other extreme view: the epistemic

invariant should be extremely simple. However I have argued that the demarcation

problem doesn‟t presuppose such a simple property. For philosophical and practical

purposes, there is no reason why a moderately complex property should not suffice.

Philosophers implicitly seem to agree with this assessment: many such criteria have been

proposed, and yet these criteria have not been criticised for their complexity.

So I claim that a demarcation property can lie between the two extremes: it mustn‟t be

too variant (or equivalently, complex), but it can be moderately variant (or complex).

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2.6 The Moderate Demarcation Criterion

Laudan specifies four requirements for a solution, which I have considered in turn. I am

now in a position to provide my own contrasting list of minimum requirements for a

solution, as shown in Figure 2.2.

Laudan Walsh

Accuracy Reasonable Reasonable

Precision Perfect Moderate

Epistemic Superiority Direct Indirect

Invariance Extreme Moderate

Figure 2.2: My alternative requirements for a demarcation criterion.

I have argued that, with the exception of accuracy, the requirements for a demarcation

criterion should be more moderate than Laudan has demanded. The demarcation problem

does presuppose an epistemic invariant, but it only needs to be reasonably accurate,

moderately precise, indirectly epistemic and moderately invariant. Now I shall move on to

Laudan‟s argument for P2, and ask „Is there a suitable epistemic invariant?‟

2.7 The Existence of the Epistemic Invariant

Laudan claims that there isn‟t an epistemic invariant in science. He declares emphatically,

“The evident epistemic heterogeneity of the activities and beliefs customarily regarded as scientific should alert

us to the probable futility of seeking an epistemic version of a demarcation criterion” (Laudan, 1983: 124

– Laudan‟s italics). This argument can be reconstructed as follows:

1. Science is epistemically heterogeneous.

2. If science is epistemically heterogeneous, then probably there is no epistemic

invariant in science.

3. Probably there is no epistemic invariant in science.

Laudan‟s argument seems plausible, yet is less convincing after closer examination.

The presence of a great deal of heterogeneity is quite consistent with the presence of a

moderately complex invariant. One reason for this is that there are many aspects of

science. Therefore, many aspects can vary while a few aspects remain the same. Similarly,

cricket balls, British telephone boxes, clown noses and poppies have very little in common

apart from the fact that they are red. Red things are heterogeneous in almost every way;

54

they are only homogeneous in one respect. Another reason is that a moderately complex

logical function, such as a family resemblance between the sciences, allows all simple

properties to vary while maintaining a more complex invariant. Similarly, there are

considerable differences between the faces of my family members, yet strangers can detect

a distinct family resemblance.

Nobody expects all cases of science to be the same in every way: we already know that

there will be many differences in subject matter and investigative techniques, and possibly

deeper differences as well. But this is quite consistent with the presence of an invariant –

particularly a deep property like my ultimate goals, or a moderately complex property like

Thagard‟s conceptual profiles. So it simply does not follow that discovering an epistemic

invariant is improbable (i.e. has a probability significantly less than 50%) just because we

see a lot of heterogeneity.36

Furthermore, we should not confuse lowering a probability with causing a low

probability. Laudan‟s second premise would be much more plausible if it were:

2'. If we discover more epistemic heterogeneity in science, then this lowers the

probability that there is an epistemic invariant in science.

This must be true, if discovering the additional heterogeneity eliminates some of the

plausible epistemic invariants that we thought might exist in science. But it does not follow

that lowering the probability that an invariant exists (say, by 5%) will reduce the probability

below 50%.

To make such a calculation, we would need to take into account numerous other

factors, which Laudan does not quantify. What was the prior probability that a suitable

invariant exists? When philosophers began their search, their hopes were no doubt very

high, given that the various sciences were already intuitively placed in the same class

(science), and they do appear to share many similarities. So the initial probability of success

might have been as high as 90%. What other information have philosophers discovered,

that might have caused them to revise this probability? I argued that there has been

significant philosophical progress, which should be a factor that significantly increased the

estimated probability of success. If we take all the positive and negative factors into

account (including initial optimism, much heterogeneity, many similarities, numerous

failures, significant progress, and many remaining possibilities that are only moderately

36 We may need to see extreme or maximal heterogeneity before the probability of any invariant is low. This

is a possibility I will discuss shortly.

55

complex) then it is not at all clear that the probability of an epistemic invariant must be less

than 50%, as Laudan claims.

In fact, Laudan needs to do more than establish that the conclusion is probable.

Suppose that the remaining probability of finding an invariant is 40%. Solving the

demarcation problem has seemed important enough to attract a great deal of philosophical

attention and labour. Indeed, after years of failure, success would be even more glorious.

Therefore, at those odds, we should expect that many philosophers would be willing to

continue to work on demarcation and keep the problem alive. It would be premature to

declare either that demarcation is dead, or that a solution will not be found, when there is

still a 40% chance of success. Indeed, many philosophers would still hold out hope at

20%! So, to kill off all hope, Laudan needs to establish that failure is extremely probable,

e.g. 90%. Only this kind of conviction could justify „pulling the plug‟ on demarcation.

Indeed, elsewhere in „Demise‟, Laudan writes as though he is almost certain that there

is no epistemic invariant (Laudan, 1983: 124). How could he achieve this near-certainty?37

One way for Laudan to achieve certainty would be to show that science is maximally

heterogeneous. He might make the following argument:

1*. Science is maximally epistemically heterogeneous.

2*. If science is maximally epistemically heterogeneous, then there is no epistemic

invariant in science.

3*. There is no epistemic invariant in science.

If Laudan could show that science is maximally epistemically heterogeneous, then his

conclusion would follow. At either extreme of heterogeneity, we can achieve certainty (as

depicted in Figure 2.3). But in the more plausible middle ground, there is uncertainty.

37 A very bad argument would be to reverse the conditional, so that the consequent becomes a certainty: „If

there is no epistemic invariant in science, then science is epistemically heterogeneous.‟ Then the logical error

of „affirming the consequent‟ would generate the desired conclusion. But in the passage I quoted, Laudan

clearly isn‟t doing this.

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Heterogeneity Homogeneity Epistemic Invariant

Maximum Minimum No Minimum Maximum Yes Moderate Moderate ?

Figure 2.3: The implications of heterogeneity for the invariant.

2.7.1 Objection: Rules don’t vary that much!

Laudan‟s claim that science is epistemically heterogeneous is true, but almost trivially so.

Scientists in different disciplines study different phenomena, so it should come as no

surprise that their experimental techniques also differ. Scientists use different experimental

techniques and equipment, and have different background knowledge to scientists who

worked in the same discipline twenty years ago. Even scientists working side-by-side in the

laboratory may conduct different experiments to test the same theory. Scientists often

disagree about what constitutes good evidence, or sufficient data, or a significant result.

In fact, some divergence and disagreement, even at the methodological level, seems to

be a good thing for science. One could argue that divergence leads to the production of

various and diverse theories and approaches, and disagreement leads to the testing and

replacement of old theories and approaches with new ones, so that science can improve

over time. Some heterogeneity in science seems to be important to explain its long-term

success. None of this should come as a surprise to philosophers of science.38

If science is maximally heterogeneous, then all variants should appear in science. For

example, for every methodological rule that is followed by some group of scientists, we

should see the opposite rule followed by some other group of scientists. Consider the rules

and their opposites listed in Figure 2.4. 39

38 Indeed, Kuhn made this point about scientific assessments of theories (Kuhn, 1977: 112). If everyone

thought the same theory was the most promising and worked on it, then no alternatives would receive any

attention, which would be bad for science.

39 This is not by any means an exhaustive list: there are many other rules like these, and there is disagreement

about the precise wording and relative importance of such rules. However, I take it that rules of this sort

tend to be considered acceptable by scientists.

57

Prefer true theories. Prefer false theories.

Prefer theories for which there is strong empirical support.

Prefer theories for which there is strong empirical opposition.

Prefer theories that are as simple as possible.

Prefer theories that are as complicated as possible.

Prefer theories with a broad scope of application.

Prefer theories with a narrow scope of application.

Prefer theories that make successful predictions.

Prefer theories that make unsuccessful predictions.

Prefer theories that have passed rigorous empirical tests.

Prefer theories that have failed rigorous empirical tests.

Prefer theories that give a low probability to false claims.

Prefer theories that give a high probability to false claims.

Figure 2.4: Some accepted rules and their opposites.

The rules in the left-hand column are endorsed and followed by many scientists. The rules

in the right-hand column are not endorsed or followed by any scientists (as far as I know!).

In fact, such rules would surely be regarded as distinctly unscientific. The fact that there are

some rules that are regarded as unscientific – even intuitively or implicitly – tells us that

scientific methodology does not vary to an unlimited extent. Science is not maximally

heterogeneous.

2.7.2 Objection: Ultimate goals might not vary

One could argue that to sustain his argument, Laudan only needs to show that science is

heterogeneous in crucial ways. We have already established that methods vary, along with

techniques, experiments, evidence, facts and so on. Is this enough variance to support

Laudan‟s conclusion?

Some philosophers find it surprising that such divergence and disagreement over

techniques, experiments, evidence and even facts should eventually give way to

convergence and agreement in these same areas. Yet, this is what happens repeatedly in

science. As I demonstrated with my objection from limited heterogeneity, only certain

methodological rules are considered „scientific‟. This suggests that science is somehow

58

constrained. If there were no overarching epistemological, methodological or axiological

constraints, then we would see more variation in methodology among the numerous and

various cases of science. Consider the two sets of rules listed in figure 2.5. The rules in the

left-hand column are certainly diverse – yet they all seem to share common goals: such as

adequacy in light of current empirical results and the advancement of useful empirical

knowledge. This contrasts with the rules in the right-hand column. Following those rules

would lead to inadequacy in light of current empirical results and degeneration of useful

empirical knowledge. So the methodology we see in science appears to be constrained by

general epistemic and empirical goals on which scientists agree. This suggests that in

science, heterogeneity at the level of method gives way to homogeneity at the level of

cognitive goals and values. I think we can reasonably conclude that such shared goals are

probable (i.e. have a probability greater than 50%), and these goals are a crucial epistemic

feature of science.

If my argument is plausible, then Laudan is very far away from establishing

conclusively (with, say, a 90% probability) that no epistemic invariant exists.

2.7.3 Sub-conclusion: Deep epistemic homogeneity

Laudan argues that the epistemic heterogeneity of science makes it highly improbable that

there is an epistemic invariant in science. Scientific theories are different to each other in

many ways, so Laudan doubts that they are similar to each other in any way. I have argued

that, although the methods of science vary, this variation is limited. It seems to be limited

to methods that promote specific kinds of goals: the ultimate goals that appeal to all

scientists. So there is probably a deep epistemic homogeneity, or invariant, in science.

2.8 Conclusion

To summarise, Laudan argues that the demarcation problem presupposes a false

assumption. Therefore, it is a pseudo-problem and should be abandoned. I disagree.

Firstly, Laudan argues that the demarcation problem presupposes a particular kind of

property: one that satisfies the four requirements of accuracy, precision, epistemic

superiority, and invariance. He suggests that, with the exception of accuracy, these

requirements must be satisfied perfectly or extremely. This means he has a very strict

interpretation of an „epistemic invariant‟. I have argued that these requirements only need

to be satisfied moderately. This leads me to a more moderate interpretation of an

„epistemic invariant‟.

59

Secondly, Laudan argues that there is no suitable epistemic invariant in science, on the

grounds that science is epistemically heterogeneous. I have argued that if science is

maximally epistemically heterogeneous, then we would expect to see all possible variants

displayed by science. I have shown that there are some possible methodological rules that

are never displayed by science. This illustrates that, in fact, we only see limited epistemic

heterogeneity in science, and cries out for some kind of explanation. The methodological

rules followed by scientists can all be plausibly explained by common goals and aims. This

suggests that heterogeneity at the level of method gives way to homogeneity at the level of

ultimate epistemic goals.

Laudan Walsh

Interpretation of ‘Epistemic Invariant’ Strict Moderate

Claim about Epistemic Heterogeneity Extreme Moderate

Figure 2.5: My alternative interpretation and claim.

The difference between our views is summarised in Figure 2.5. These differences lead

us to different conclusions. To show exactly how, I now return to the formal version of

Laudan‟s Pseudo-Problem Argument, to assess the truth value of each premise and the

conclusion. Where do I say it goes wrong? My answer depends on how we interpret the

premises, as well as my view of the facts about science. So in Figure 2.6, I set out the

argument and give two truth values for each statement.

Interpretation of ‘epistemic invariant’: Laudan’s Walsh’s

P1. The demarcation problem presupposes that there is an epistemic invariant in science.

F T

P2. The presupposition, that there is an epistemic invariant in science, is false. T ?

P3. If a problem makes a false presupposition, then it is a pseudo-problem.

T T

C. The demarcation problem is a pseudo-problem. ? ?

Figure 2.6: Two interpretations and my corresponding truth values.

In the left-hand column of truth values, I deal with the premises if they are interpreted

as Laudan would have it. Demarcation doesn‟t presuppose a strict invariant, so P1 is false.

There is no such strict invariant, so P2 is true. The false premise P1 leaves the truth value

of C unclear.

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In the right-hand column, I deal with the premises if they are interpreted as I would

have it. P1 is now true, because working on the problem does presuppose at least a

moderate invariant. But P2 is now unclear, because (despite my goals-and-rules argument)

I leave open the possibility that no demarcation criterion will ever be completely

satisfactory and generally accepted. The dubious premise P2 leaves the truth value of C

unclear.

On either interpretation, I find Laudan‟s pseudo-problem argument unconvincing,

because it doesn‟t establish that C is true.

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3 The New Problem

Laudan argues that the problem of demarcation between science and non-science should

be replaced with a new demarcation problem. I take this to be the problem of demarcating

between well-confirmed and ill-confirmed theories. I argue that it should not replace the

original problem.

3.1 Laudan’s New Problem

Laudan argues that we should replace the original demarcation problem with a new one.

He is not entirely consistent in the terminology he uses to describe his new problem. He

talks about „well-founded beliefs‟ and „reliable knowledge‟, as well as asking questions such

as „When is a claim well-confirmed?‟ and „When can we regard a theory as well-tested?‟.

However, the general idea is clear. According to Laudan, the legitimate purpose of the

original demarcation problem was to choose which theories to believe, and this purpose

was evident in the Old Demarcationist Tradition (Laudan, 1983: 122).40 He also says we

should only believe things for which we have substantial evidence (Laudan, 1983: 125).

Indeed, throughout his paper, Laudan makes it clear that he sees strong empirical support

as the highest form of epistemic merit that a theory or claim can achieve.41 Thus, Laudan

argues that confirmation is the key criterion by which we should evaluate a theory‟s epistemic

merit. For this reason, I take his new demarcation problem to be a problem of

demarcation between well-confirmed and ill-confirmed theories.42

40 I discussed the alleged transition from the Old Tradition to the New Tradition in section 1.5.

41 This is a view that Laudan continues to hold in later papers. For example, Laudan (1984) distinguishes

between permissible and impermissible beliefs. He says “A belief is permissible precisely when, among the

various alternatives to it under active consideration, none has a higher degree of empirical support than it

does” (Laudan, 1984: 28-29). However, in earlier papers Laudan does not emphasise confirmation to such a

high degree. For example, he says “In appraising the merits of theories, it is more important to ask whether

they constitute adequate solutions to significant problems than it is to ask whether they are „true‟,

„corroborated‟, „well-confirmed‟ or otherwise justifiable within the framework of contemporary

epistemology” (Laudan, 1977: 14).

42 Laudan refers to various things that need to be confirmed in order to be credible: „beliefs‟, „knowledge‟,

„claims‟, and „theories‟. We might add to this list „hypotheses‟, „statements‟, and so on. I take it that all these

things are slightly different, but these differences are not important to Laudan‟s argument. So, like Laudan, I

will vary these terms freely to fit my examples.

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Laudan‟s argument can be formalised as follows:

P1. We only want to know which theories we should believe. (claim)

P2. We should believe all well-confirmed theories and we shouldn‟t believe any theories

that are not well-confirmed. (claim)

P3. We only want to know which theories are well-confirmed. (from P1 and P2)

P4. Not all scientific theories are well-confirmed and some non-scientific theories are

well-confirmed. (claim)

P5. Scientific status is completely irrelevant to well-confirmedness. (supported by P4)

P6. We don‟t want to know which theories are scientific. (from P3 and P5)

C. We only want to know which theories are well-confirmed; we don‟t want to know

which theories are scientific. (from P3 and P6)

Laudan‟s argument is two-pronged: the arguments form two sub-conclusions, P3 and P6,

before the main conclusion, C. I shall consider each of these sub-arguments separately. In

Section 3.2, I will begin by considering some definitions of confirmation and whether

confirmation alone could plausibly dictate our beliefs. In Section 3.3, I will argue that

scientific status is relevant to confirmation (contrary to P5, and hence to P6). In Section

3.4, I will argue that scientific status also indicates other virtues that we want to know

about, for other purposes besides belief (contrary to P1 and P3).

3.2 Accounts of Confirmation

3.2.1 Laudan’s views

Confirmation is a relationship in which the available facts are supposed to offer some

degree of support for a candidate theory.43 The term „confirmation‟ is commonly used in

two distinct senses:

1. In an absolute sense, facts can confirm a theory if they provide conclusive support

for the theory; and

43 The term „confirmation‟ is sometimes applied to the predictions of a theory. For example, physicists say

that special relativity has been confirmed to 10-20(Coleman and Glashow, 1997). But this refers only to the

discrepancy between the predicted measurement and the actual measurement. It does not measure the degree

of support for any deeper theoretical claims. So this is a slightly different sense of „confirmation‟ to standard

philosophical usage.

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2. In an incremental sense, facts can confirm a theory if they provide some further

degree of support for the theory.

If we talk about theories being confirmed in the first sense, then theories are either

confirmed or unconfirmed. It makes no sense to say that one theory is well-confirmed

while another is ill-confirmed – either it is confirmed, or it isn‟t. If we talk about theories

being confirmed in the second sense, then one theory may be confirmed to a higher degree

than another theory. Thus, we can talk (as Laudan does) about theories being well-

confirmed and ill-confirmed. So, to formulate Laudan‟s new demarcation problem in a

way that is consistent with his remarks, confirmation should be understood in an

incremental sense.

Laudan thinks that all judgements of evidential support are comparative

(Psillos, 1999: 171 & 175). A given fact F confirms a theory T1 better than it confirms an

alternative theory T2 when F gives a higher degree of support to T1 than to T2. So, I take it

that confirmation should be understood in a comparative sense.

Laudan also notes that there are various possible positive relationships between a fact

and a theory. A theory may (Laudan, 1990a: 329):

a) Be logically compatible with a fact;

b) Logically entail a fact;

c) Explain a fact; or

d) Be empirically supported by a fact.

He argues that these relationships are not reducible to each other. For example, a theory

that satisfies (a) or (b) does not necessarily satisfy (c) or (d). This complicates any

definition or assessment of confirmation.

3.2.2 A simple account

One could give a „simple‟ account of confirmation which specifies a purely deductive

relationship between an accepted fact F and a theory T. On such accounts, only Laudan‟s

relationships (a) and (b) are relevant. There are three logical possibilities: (i) if T entails F,

then F confirms T; (ii) if T entails ~F, then F disconfirms T; and (iii) if T does not entail

either F or ~F, then F neither confirms nor disconfirms T. For example, the hypothesis

„all crows are black‟ would be confirmed by a black crow, disconfirmed by a non-black

crow, and neither confirmed nor disconfirmed by a black cat. A set of accepted facts can

confirm T1 to a higher degree than T2 either because fewer of these facts are denied by T1

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than T2, or because more of these facts are entailed by T1 than T2. At times, Popper seems

to hold this simple view of confirmation (e.g. (Popper, 1963)): either a fact falsifies a

theory, or it could have falsified it but now corroborates it, or could never have falsified the

theory and was always irrelevant. Proponents of such a simple view need to weigh up the

relative importance of confirming and disconfirming facts. Popper clearly regarded

disconfirming facts as much more important than confirming ones.

3.2.3 Sophisticated accounts

There are more sophisticated accounts of confirmation in which each individual fact can

give a greater or lesser degree of support to a theory. This includes probabilistic theories of

confirmation, such as those of Carnap (e.g. (Carnap, 1950)) and many Bayesians (e.g.

(Dorling, 1979), (Earman, 1992), (Howson and Urbach, 1989)). All of Laudan‟s

relationships (a), (b), (c) and (d) are relevant to these accounts.

For example, Bayesian confirmation theory tells us that facts confirm theories by

increasing the probability that the theory is true, in accordance with Bayes‟ Theorem:

According to Bayesian confirmation theory, we begin with some prior probability P(T) for

the theory T before the fact F is taken into account. Subsequently, some facts will support

T more than others. The amount of support that a fact can give to a particular theory

depends, firstly, on how likely the fact is if we assume that the theory is correct, i.e. P(F|T).

If T entails F, then P(F|T) will be 1. If T entails ~F, then P(F|T) will be 0. If T is merely

consistent with F, then P(F|T) will be more than 0 and less than 1. F will give more

support to T if, say, P(F|T) = 0.9 than if P(F|T) = 0.5. Secondly, the amount of support

that a fact can give to a particular theory depends on how likely the fact is if we don‟t

assume the theory is true, i.e. P(F). This depends on all the alternative theories and our

background knowledge (Chalmers, 1999: 175-176). If P(F) is very low, but P(F|T) is very

high, then discovering F will significantly increase the probability of T. If P(F|T) is very

high but P(F) is equally high, then the probability of T will not change. The probability of

T could even decrease if P(F|T) is high but not as high as P(F).

The degree to which a fact supports a theory can be reformulated in a comparative

way, consistent with Laudan‟s views. Bayes Theorem implies that:

P(T|F) = P(F|T).P(T)

P(F)

P(T1|F) =

P(F|T1).P(T1)

P(T2|F) P(F|T2).P(T2)

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That is, the ratio of a posteriori probabilities is equal to the ratio of the likelihoods times the

ratio of a priori probabilities. So the „likelihood ratio‟ measures the effect of evidence on

the „prior ratio‟, i.e. it measures the comparative degree of confirmation of one theory over

another provided by the evidence (Good, 1983: 38).

Bayesian confirmation theory can distinguish between smaller degrees of confirmation

than a purely logical account. This increases the chance that, for any pair of competing

theories T1 and T2, P(T1|F) will be different to P(T2|F) – i.e. one theory will end up more

credible than the other.

3.2.4 Overcoming underdetermination

For Laudan to sustain his argument, he must argue that confirmation is sufficient to dictate

what we should believe. One objection to this argument arises from the alleged

„underdetermination‟ of theory choice by facts. Roughly, the idea is that even the most

well-confirmed theory must always have contraries which are equally well-confirmed but

incompatible with it.44 If confirmation is our only criterion for choosing which theory to

believe, then we can never have good reason to believe any one theory rather than one of

these contraries.

One version of this objection is that theory choice is underdetermined by the observed

facts. This leaves open the possibility that future discoveries will confirm one theory more

than the others, and resolve any particular underdetermination dilemma. Alternatively, one

might seek to rule out the contraries immediately on other grounds. For example, one

might appeal to an epistemic virtue such as simplicity in order to show that one theory is

better than the others. Thus, we can hope to make a rational decision to prefer one theory

over another despite underdetermination. Another option is to accept the conclusion that

we should never believe any one theory more than its contraries, and try to keep a

perpetually open mind (although this seems bizarre, since we often come to believe

particular theories).

A slightly different version of the objection from underdetermination is that there will

be alternative theories that are empirically equivalent with respect to all possible

observations. In that case, we could never hope to distinguish between them on the basis

44 There are various arguments for this claim. See, for example, „Chapter 3: The Duhem-Quine Thesis and

Underdetermination‟ in Curd & Cover (1998).

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of future discoveries. However, we could argue that such empirically equivalent theories

are not contraries at all; rather, they are different formulations of the same theory.45

Yet these replies seem to concede too much to the objection from

underdetermination. They accept the claim that theories are underdetermined by the facts,

but it is not clear that we should concede this. Some philosophers (e.g. (Psillos, 1999) and

(Laudan, 1990a)) have argued that a sophisticated account of confirmation can avoid

underdetermination. They demonstrate that even if we happen to generate two rival

theories that „fit‟ all and only the same facts, the theories may not be equally well-

confirmed, because with a sophisticated account of confirmation the same facts can

confirm each theory to a different degree. For example, on a Bayesian view the a posteriori

probabilities of the theories can still be different. So a sophisticated account of

confirmation could more easily avoid underdetermination, and could plausibly dictate

beliefs, as Laudan‟s argument requires.

3.3 Objection: Science is Relevant to Confirmation

Laudan argues that we should only be interested in assessing the degree of confirmation of

knowledge claims. He says “the „scientific‟ status of those claims is altogether irrelevant”

(Laudan, 1983: 125). Here I argue that scientific status is, in fact, relevant to confirmation.

3.3.1 Science is more than confirmation

As I noted in my Introduction, there are many aspects to science, including (but not limited

to) : (a) basic elements such as theories, predictions, experiments, and results; (b) technical

refinements such as mathematical models and formulae, measurement tools, and statistical

analyses; (c) general virtues of theories such as confirmation, novelty, simplicity,

explanatory breadth, and usefulness; and (d) social arrangements such as qualified experts,

published journals, peer review, large institutions, and competition for funding. Laudan

has commented at various times on many of these aspects of science (e.g. (Laudan, 1977),

(Laudan, 1981a), (Laudan, 1984), (Laudan, 1990b)), so he would agree that science has

other aspects besides assessing the degree of confirmation of theories. In Demise his claim

is simply that all these other aspects are irrelevant to epistemic merit.

3.3.2 Types of relevance

To evaluate Laudan‟s argument, I need to clarify what is meant here by relevance.

45 The theories would have identical Ramsey sentences, which on some accounts makes them identical

theories (c.f. (Mellor, 1998)).

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Firstly, relevance comes in degrees. In particular, we can distinguish between the

following cases:

(i) Scientific status would have no relevance when it gives no indication at all of the

degree of confirmation.

(ii) Scientific status would have some relevance when it gives some indication of the

degree of confirmation. This could be weak, moderate, or strong relevance.

(iii) Scientific status would have exhaustive relevance when it completely determines the

degree of confirmation, so that nothing else is relevant.

Secondly, there are at least three possible ways in which scientific status can be relevant to

confirmation:

a) Scientific status would be logically relevant to confirmation if their definitions were

connected, e.g. one term appears in the definition of the other;

b) Scientific status would be statistically relevant to confirmation (in some specified

circumstances) if the occurrence of one made the occurrence of the other more or

less likely; and

c) Scientific status would be causally relevant to confirmation if scientific status had

some causal influence on confirmation or vice versa, i.e. they were causally

connected.

If Laudan could show that the notions of science and confirmation were completely

irrelevant in all respects, then this would certainly be adequate for his argument. But partial

relevance in some respects may be a problem. If there is a strong logical, statistical or causal

connection between science and confirmation, then demarcating science could be useful

for demarcating well-confirmed theories.

3.3.3 No exhaustive relevance

Laudan claims that not all scientific theories are well-confirmed. Indeed, he says, “many,

perhaps most, parts of science are highly speculative” (Laudan, 1983: 123).46 I agree.

There are some parts of science, particularly new, competing hypotheses, that are not well-

46 He even employs his other pessimistic induction against the truth of scientific theories to support this claim.

He asks, given that the historical record indicates that most scientific theories are false, “how plausible can be

the claim that science is the repository of all and only reliable or well-confirmed theories?”

(Laudan, 1983: 123).

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confirmed.47 For example, it is not clear whether the universe will continue to expand

forever, or whether it will eventually contract in a Big Crunch. Both possibilities are

current and valid scientific hypotheses (Hinshaw, 2008), but we cannot regard both of them

as well-confirmed! Conversely, Laudan claims that some well-confirmed theories are not

scientific. I agree. There are some parts of non-science, particularly well-established

specific hypotheses, that are well-confirmed. For example, in History, it is well-confirmed

that Lincoln was assassinated by John Wilkes Booth; and in professional tennis strategy, it

is well-confirmed that serving is an advantage.48

But Laudan‟s argument really only shows that scientific status is not exhaustively relevant

to confirmation or vice versa, either logically, statistically or causally. If some scientific

theories are not well-confirmed, and some well-confirmed theories are not scientific, it

follows that:

a) Logically, there must be more to the definition of confirmation than scientific status

(and vice versa).

b) Statistically, being scientific does not make it certain that a theory will be well-

confirmed (or vice versa).

c) Causally, being scientific does not always make a theory well-confirmed (or vice versa).

However, this conclusion is weak: it leaves open the possibility of non-exhaustive but

strong relevance.

3.3.4 Causal relevance

I claim that there is a strong causal connection between scientific status and confirmation.

Philosophers of science do not agree on all aspects of the scientific process, but most

philosophers who accept the idea of confirmation would agree with the following general

remarks:

47 Derksen (1993) points out that their scientific status may just be due to the „happy mistake‟ that such

theories have been generated from the scientific tradition. If such speculative and untested theories had been

generated outside science, then Derksen tells us that they wouldn‟t be considered „scientific‟ so easily.

Nonetheless, it is clear that when such theories have been generated within science, then they are regarded as

scientific.

48 It could be argued that non-scientific theory is only well-confirmed because it has undergone some kind of

quasi-scientific process. However, this is stretching the definition of „scientific‟ in a controversial way. I

don‟t need to take this line in order to defeat Laudan‟s argument.

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When scientific theories are first proposed, they are often highly speculative.

However they are considered „scientific‟ before they become well-established or

well-confirmed. By well-established I mean „well-established after considerable

investigation‟.

An important aspect of science is gathering evidence relevant to these speculative

theories, e.g. through experiments that test their predictions.

Not all speculative theories survive to become well-established scientific theories.

Many are disconfirmed and/or not confirmed by the evidence, and are eventually

rejected.

Some theories survive to become well-established. By this stage they are generally

well-confirmed, due to accumulated favourable evidence, e.g. favourable

experimental results.

On this account, science is (among other things) a causal process that achieves

confirmation. Indeed, it is probably our best available process for confirming novel

theories. So being scientific is not irrelevant: it indicates that a theory is either undergoing

the process of confirmation, or has survived that process and has probably become well-

confirmed.

3.3.5 Statistical relevance

Statistically, Laudan‟s argument that science is irrelevant to confirmation is very weak. This

can be illustrated by considering other arguments with the same logical form. For example:

Some short people are successful at professional basketball, and some tall

people are not successful at professional basketball. So being tall is

altogether irrelevant to being successful at professional basketball.

Although the evidence cited shows that being tall is not exhaustively relevant to being

successful at professional basketball (because there are other relevant factors), we know

that in practice the two things are relevant to each other. A player is much more likely to

be successful at professional basketball if he is tall. (Of course, this statistical fact has a

causal explanation: being tall makes it easier for him to score points.) Therefore, if we are

interested in knowing which players are more likely to be successful professional players,

we should be interested in their height. A method of measuring their height would be very

relevant!

Similarly, the causal process I described in section 3.3.4 leads to a statistical connection

between scientific status and being well-confirmed. When we look specifically at well-

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established theories, I think we can see that being scientific makes it more likely that a theory

is also well-confirmed. Unfortunately, it is beyond my present resources to support this

claim with a large randomised sample of well-established theories, and independent expert

assessments of their scientific status and degrees of confirmation to prove that the two

features are correlated. But perhaps I can support my claim with a thought experiment,

which appeals to our intuitions:

Theory A is a well-established scientific theory: firmly believed by scientists and taught as a

fact in science classes. Theory B is a well-established pseudo-scientific theory: firmly believed by

pseudo-scientists and taught as a fact in pseudo-scientific classes. Which theory is more likely

to be well-confirmed?

I think most people would agree with me that theory A is much more likely to be the

genuinely well-confirmed theory, although theory B may give the deceptive appearance of

being well-confirmed. If this intuition about the probability of confirmation is correct,

then scientific status is strongly statistically relevant to confirmation (in the specific

circumstances of examining well-established theories).

Where a theory is not well-established, I do not claim that being scientific increases the

probability that it is well-confirmed. In outlining the causal process, I noted that many new

scientific theories are speculative. By definition, these are not well-confirmed. Of course,

speculative new pseudo-scientific hypotheses would not be well-confirmed either.

However if theory A and theory B were both speculative new hypotheses, it is not clear

that the scientific one would be any more likely to be well-confirmed.

I have argued that (for well-established theories), scientific theories are more likely to

be well-confirmed than unscientific ones. A fortiori, I now argue that our most well-

confirmed theories are more likely to be scientific than unscientific. For example, the

biological theories that „organisms inherit many physical characteristics via the replication

of DNA‟ and that „the function of the heart is to pump blood around the body‟ are among

the most well-confirmed theories we have (e.g. (Alberts, et al., 2002: 235) and (Schultz,

2002)). There are many other examples of extremely well-confirmed theories in various

scientific disciplines. In contrast, few non-scientific or pseudo-scientific theories are

confirmed to an equally high degree. In cases where „folk theories‟ have been extremely

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well confirmed, this is usually by gathering scientific evidence and converting them to

scientific theories!49

3.3.6 Logical relevance

It is clear that „scientific‟ and „well-confirmed‟ do not mean the same thing. Nevertheless, it

might turn out that on some accounts the two terms are logically relevant. For example,

some people might accept a definition of the form „Science is a process of confirming

theories, in which...‟ Alternatively, some people might accept a definition of the form

„Confirmation is a property achieved by the scientific process, when...‟ Even if the two terms

are not as closely related as this, one term might appear somewhere in a detailed explication

of the other. The main problem for assessing logical relevance is that we have no clear

agreement on a definition of science (and perhaps not of confirmation either). So all we can

say is that scientific status might be logically relevant to confirmation, or it might not. This

does not decisively refute Laudan‟s claim that scientific status is logically irrelevant, but it

does cast some doubt on his claim, and hence on his argument.

3.3.7 Sub-Conclusion: Scientific status is strongly relevant

I have conceded that the scientific status of a theory has some logical, statistical and causal

independence from its degree of confirmation. Nevertheless, I have argued that scientific

status may be logically relevant (although this is hard to assess). What seems certain is that

scientific status is strongly statistically and causally relevant to confirmation, contrary to

Laudan‟s claim. If we are interested in identifying which theories are likely to be well-

confirmed, then we should be interested in identifying which theories are scientific.

3.4 Objection: Science has Other Purposes and Other Virtues

Laudan argues that we should only be interested in finding out which theories are well-

confirmed, and hence, belief-worthy (Laudan, 1983: 125). If so, then it seems that the only

purpose of science is to seek well-confirmed theories, and confirmation is the only real

virtue. I shall now argue that there are other well-recognized and legitimate purposes in

science besides seeking well-confirmed theories, and other well-recognized and legitimate

49 For example, folk medicine says that specific herbs are valuable for particular ailments. Scientific

investigation has shown that some of these claims are correct, and some of these claims are mistaken (e.g.

(Morgan, 1999) and (Pirotta, et al., 2004)). The correct claims are now well-confirmed scientific claims, not

just folk claims. It seems that none of these folk claims were actually well-confirmed before scientific

investigation, but afterwards the correct ones became well-confirmed scientific claims.

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virtues besides confirmation. These alternative purposes help to explain why these

alternative virtues are genuinely valuable. We should be interested in identifying what is

scientific, because this indicates the presence of such virtues.

3.4.1 Improving our understanding requires novelty

Science is generally expected to improve our understanding of the world. Science has

served this important purpose admirably in the past, so it is quite reasonable to expect it to

continue to do so, and to regard this as a legitimate purpose of science.

To improve our understanding of the world, scientists cannot rest content with

cautious or well-confirmed theories that tell us things we already know. They must

continue to put forward new theories, even if (initially) they are speculative and not well-

confirmed. This explains why we should value theories that are progressive, or fruitful, or

make novel predictions. These things are often cited as virtues by both scientists and

philosophers (e.g. (Lakatos, 1977), (Laudan, 1977), (Kuhn, 1977)), and yet they are clearly

different to confirmation. In fact, there seems to be a tension between these values, since

pursuing novelty will often lead to different choices than pursuing confirmation alone

(e.g. (Kuhn, 1977)).

3.4.2 Improving our living standards requires usefulness

Science is also expected to improve our living standards. Again, it has served this

important purpose admirably in the past, and it is quite reasonable to regard this as a

legitimate purpose. Science can achieve this through a better understanding of how to

manipulate the world to achieve our ends, which includes the development of new and

useful technology.

To improve our living standards, scientists must pursue novelty. But in particular, they

should prefer theories with potential useful applications, and develop these applications.

This explains why scientists are often interested in practical problems and developing

technology, rather than merely confirming theories of abstract intellectual interest. It could

also help to explain the attraction of simplicity and explanatory breadth in a pragmatic way.

Simplicity helps us to understand and use a scientific theory, and explanatory breadth means

that a theory can be used in a wider variety of situations. These virtues are clearly different

from confirmation.

3.4.3 Achieving confirmation requires testability

Even if we focus on the scientific purpose of confirming theories, there are other virtues

that are useful for this process, which are not in themselves confirmatory. As I argued in

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section 1.6, testability is a necessary condition for becoming well-tested. As Popper

observed, some pseudo-scientific theories are not testable, and for this reason they should

not be classed as scientific. So even if confirmation is the ultimate goal and the primary

epistemic virtue, testability must still be a significant indirect virtue (in my terminology of

section 2.4.2).

Yet testability is not, in itself, confirmatory. It isn‟t an indication that the theory is

well-confirmed, only that it might eventually become well-confirmed. It follows that

scientific theories have a genuine virtue that is not identical to confirmation. So, when a

new, speculative theory is scientific, we can expect it to have the virtue of being testable.

We cannot be so confident that a new, speculative pseudo-scientific theory will have this

virtue.

3.4.4 Pre-selection before confirmation

The nature of the scientific process also suggests that other virtues must be employed

besides confirmation. In section 3.2 I conceded that, at least in principle, a sophisticated

account of confirmation could overcome the underdetermination objection to dictate

which theories to believe, without resorting to any other epistemic virtues. In practice,

however, evaluating theories solely on the basis of confirmation is inefficient.

As I have argued, many novel and promising theories are initially speculative. In fact,

as the underdetermination objection suggested, scientists can potentially generate a huge

number of speculative theories. Such theories can only be considered „well-confirmed‟

after rigorous testing. The problem is that there are too many speculative theories: more

than we could ever possibly test! Confirmation cannot be a strong criterion for deciding

which theories to test, since none of the options will be confirmed to any significant

degree.

For the sake of efficiency, we need to use other values to pre-select theories for testing.

Presumably, these are the familiar virtues of novelty, explanatory breadth, simplicity, etc.

These other virtues do not require data or testing, so they can be used for pre-selection

even if no relevant data is yet available. So, identifying a new speculative theory as

„scientific‟ is useful because it is an indication that the theory has adequate virtues to be

worth investigating – even before it is (or could be) well-confirmed. Fuller considers this

process of pre-selection “a necessary (albeit fallible) condition for granting epistemic

warrant” (Fuller, 1985: 331). However, he says, this point is lost on Laudan (1983), who

only notices the diversity of the different cases of science – Laudan can‟t see the forest for

the trees.

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In support of my argument, some speculative theories do seem to be worth costly

testing, while others do not even seem to be worth considering. For example, some

physicists have proposed alternative theories of gravitation, in order to account for

observed cosmological anomalies (e.g. (Bekenstein, 2004) and (Moffat, 1995)). I could

propose my own theory: gravity is exactly as the current theory dictates, except that

smoked eels will weigh 50% less at the South Pole. Physicists are looking for ways to test

their alternative theories, and yet I doubt that they would be interested in testing my

proposal! I think they would be right to ignore it: all these proposals make novel

predictions, but theirs have other significant virtues such as explanatory breadth and prior

probability, whereas my proposal does not.

3.4.5 Other virtues can outweigh confirmation

Other virtues are particularly useful for pre-selection, but they also seem to be significant

later in the process. In some comparisons between theories, differences in other virtues

seem to outweigh small differences in confirmation.

Suppose there is an alternative to Newton‟s theory of gravity, which is logically weaker.

It has a narrower explanatory scope, because it is restricted to some things that we can

easily observe, and excludes many things that are difficult to observe. It is also non-

quantitative, because it only says that these objects are attracted to each other, and does not

specify by how much. It would seem that this alternative must always be better confirmed

than Newton‟s theory, no matter how much evidence we accumulate. Overall, the things it

predicts will be better observed. Also, on a Bayesian analysis, the posterior probability of

the alternative must be at least as high as Newton‟s theory, because whenever Newton‟s

theory is true then the alternative must be true (but not vice versa). Yet Newton‟s theory

would still seem to be preferable, due to its broader explanatory scope and more specific

quantitative predictions.50

3.4.6 Reply: Pursuit versus acceptance

In earlier work, Laudan (1977: 111) identifies two kinds of theory preference: preferring a

theory to pursue, and preferring a theory to accept. Accepting a theory requires us to believe

the theory (i.e. to believe that the theory is true or empirically adequate). So, when we

prefer to accept theory A instead of theory B (typically after extensive testing), then we are

deciding to believe theory A instead of theory B. In contrast, pursuing a theory does not

50 For a more detailed discussion of this case, see Appendix C.

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require us to believe the theory. So, when we prefer to pursue theory A instead of theory B

(possibly after no testing at all), then we are not deciding to believe theory A, and may

prefer theory A for reasons other than credibility.

Laudan argues that it can be rational to pursue a theory even if one does not believe it.

For example, it might be rational to pursue a theory if one thinks that, should the theory

turn out to be true, then there would be an enormous pay-off. Moreover, it might still be

rational to pursue a theory if one thinks it probably isn’t true but it is an interesting and

important theory that one is best equipped to explore.51 In contrast, it is never rational to

accept a theory that one does not believe. So the kind of preference we exercise in the pre-

selection stage may not be the same kind of preference we exercise at the established-

theory stage. Laudan would surely argue that the preferences I have discussed are for

pursuit rather than for acceptance.

Laudan thinks we should only believe things for which we have “substantial evidence”

(Laudan, 1983: 125) – this suggests that, when it comes to acceptance, empirical support

overrides other virtues. Even if we decide to pursue theories with virtues such as novelty,

simplicity, and potential usefulness, when we are dealing with established theories, we

always prefer the better-confirmed ones.

3.4.7 Rejoinder: Demarcation is still useful for pursuit

Laudan‟s argument that these other virtues are „pursuit‟ virtues rather than „belief‟ virtues, if

correct, would be a strong argument that features such as testability and novelty are not

primary epistemic virtues – they only have secondary epistemic value. This offers some

support for P2 in Laudan‟s main argument: that belief should depend only on

confirmation.

But the distinction between pursuit and belief does not provide a strong argument

against the project of demarcation between science and non-science. It does not support

P1 in Laudan‟s main argument: that we are only interested in belief. I have argued that

there are other purposes in science besides providing well-confirmed theories, and these

other virtues help to achieve these other purposes. So demarcation can still be useful for

identifying theories with the pursuit virtues, which are the theories we should prefer to

pursue. In fact, the „pursuit without belief‟ reply begs the question, since my argument in

section 3.4 is an attack on P1 rather than on P2.

51 For Laudan (1977) the decision to pursue a theory is closely related to the rate of progress in problem-

solving that the theory seems to offer.

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3.4.8 Rejoinder: Other virtues may affect acceptance

Laudan would argue that these other virtues are only „pursuit‟ virtues, but it is not clear that

this is correct. I will not debate this point in detail, but I will list three possible arguments

against it.

Firstly, in some cases it seems reasonable to prefer theories that display virtues other

than well-confirmedness, even after extensive testing – which suggests that these virtues are

affecting acceptance. For example, even after extensive testing we would still prefer

Newton‟s theory of gravity to the logically weaker version mentioned earlier. The

alternative would still be better confirmed, but Newton‟s theory would still have its other

virtues, which seem to outweigh the difference in confirmation.

Secondly, Psillos argues that some epistemic virtues might be said to “capture the

explanatory power of a theory” (Psillos, 1999: 171). He then draws on a combination of

arguments made by Boyd and Salmon to argue that explanatory power is potentially

confirmatory because it raises the prior probability of a theory (Psillos, 1999: 171-172). If

so, these epistemic virtues would affect acceptance.

Thirdly, in section 1.4 I suggested that it might be possible to demonstrate that some

epistemic virtues are fallibly related to the truth (or empirical accuracy with respect to the

available data) of theories. If we could establish this connection, then these virtues would

be valuable as „truth indicators‟ and affect acceptance. However, it is difficult to establish

whether other virtues (e.g. simplicity) are truth indicators, and it raises issues that are

beyond the scope of this thesis.

If any of these arguments could be sustained, showing that other virtues are relevant to

acceptance, then Laudan cannot dismiss the other virtues as merely for „pursuit‟. They are

valuable properties that scientific theories tend to have, which also make them worthy of

belief. This would cast some doubt on P2: the premise that belief should be governed only

by confirmation.

3.4.9 Reply: Science is all about confirmation

In response to my argument that being scientific signifies other virtues besides

confirmation, Laudan might revise his position. He could make almost the opposite reply.

Instead of dismissing other virtues as not important at all, or at least not important to

acceptance, he could concede that they are important, but only for acceptance. The idea

would be that the other virtues are important precisely because they lead to confirmation.

But once we have achieved and assessed the degree of confirmation, these virtues would

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have no further or additional importance. This makes the other scientific virtues relevant for

achieving and assessing confirmation, but irrelevant for belief once confirmation has been

assessed. This is a revision to Laudan‟s blunt claim in Demise that scientific status is

“altogether irrelevant” (Laudan, 1983: 125).

What Laudan would dismiss is my claim that other virtues are important for other

purposes besides confirmation. The purposes of improving understanding and living

standards might be explained away as reasons for wanting specific kinds of confirmed

theories (i.e. just variations on wanting confirmation). Laudan could try to explain every

feature of science by making some kind of connection to confirmation, whether this is

logical, statistical or causal. On this view, science is actually „all about‟ confirmation. In

this way, Laudan could try to defend P1: the premise that we are only interested in

confirmation.

This revised view might also allow Laudan to defend P2, the premise that belief should

be determined by confirmation. It concedes that other virtues could play a limited role in

acceptance, but only in support of confirmation.

3.4.10 Rejoinder: Then science is relevant to confirmation!

I concede that this revised position, if defensible, might save P2 and rebut my „other

virtues‟ argument against P1 in section 3.4. However, this revised position doesn‟t save P1,

because it concedes the main point I argued for in section 3.3! If science is „all about‟

confirmation, then it is relevant to confirmation. Understanding what makes an area of

knowledge a science should help us to understand what makes something well-confirmed.

Identifying things as scientific would help to „pick out‟ things with features that are relevant

to assessing confirmation. Thus, demarcating science could help to demarcate confirmation.

3.4.11 Sub-conclusion: Being ‘scientific’ indicates other virtues

Laudan (1983) appears to think that the only legitimate purpose of science is to seek well-

confirmed theories. I have argued that there are other important purposes in science, such

as providing theories that increase our understanding of the world and improve our living

standards. Hence, there are other important virtues in science besides confirmation, such

as novelty and usefulness. These virtues are particularly useful for deciding which theories

to pursue, rather than wasting time and money investigating all possible theories. So being

„scientific‟ indicates an appropriate mixture of other virtues, not just being well-confirmed.

It is a more general indicator of epistemic merit, similar to the useful summary assessment

that a theory is „epistemically good‟ (although a bit more specific).

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It is possible to argue that these other virtues of section 3.4 are only valuable because

they lead to confirmation. But this concedes that my conclusion of section 3.3 is correct:

being scientific is relevant to confirmation. So it doesn‟t really matter to my argument in

Chapter 3 whether these other virtues are valuable because of their relationship to

confirmation, or valuable for other reasons. Either way, demarcating what is scientific

seems to be a valuable project.

3.5 Conclusion

I will now summarise exactly what is wrong (on my analysis) with Laudan‟s formalised

argument of section 3.1.

In section 3.4, I disputed P1. I argued that there are other important purposes for

science besides telling us what theories to believe. I also disagreed with P3. I argued that

there are other important and well-recognized values in science besides confirmation, such

as novelty, simplicity, explanatory breadth, and usefulness. These other values serve the

other purposes, especially when deciding which theories to pursue. They also seem to play

a role in deciding which theories to accept, which may conflict with P2.

In section 3.3, I did not challenge P4, but I demonstrated that it provides very little

support for P5. I then disagreed with P5. I argued that (1) science might be logically

relevant to confirmation; (2) science is strongly statistically relevant to confirmation; and (3)

science is strongly causally relevant to confirmation.

Hence, I disagreed with P6 and C for two reasons. Contrary to P5, science is relevant

to confirmation, so demarcating science could help us solve Laudan‟s new demarcation

problem. Contrary to P3, being scientific indicates an appropriate mixture of other

important epistemic virtues, so demarcating science could help us to identify things with

such virtues.

I have not argued that we never want to know which theories are well-confirmed. It is

important to answer the questions Laudan asks, such as: „When is a claim well-confirmed?‟,

„When can we regard a theory as well-tested?‟ and „What makes a belief well-founded?‟.

But to answer these questions properly, we need an adequate account of science – which is

also useful for other reasons. Thus Laudan‟s new demarcation problem is worth solving,

but we shouldn‟t reject the original demarcation problem.

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4 Conclusion

Laudan argues that the original demarcation problem cannot be solved. He gives three

main sceptical arguments against it:

A Pessimistic Induction – No past solution has succeeded, therefore future success is unlikely.

A Pseudo-Problem – The demarcation problem presupposes an epistemic invariant in science.

This assumption is false.

A New Problem – We can (and should) evaluate confirmation without considering scientific

status.

I replied to each of these three arguments in turn, asking „Has Laudan killed the

demarcation problem?‟

Against Laudan‟s pessimistic induction, I demonstrated that the failure of many past

attempts at demarcation has been partial, and the theory of demarcation continues to make

cumulative progress. Therefore, I think we can take a more optimistic view.

Against Laudan‟s pseudo-problem argument, I demonstrated that the demarcation

problem does not presuppose an extremely simple epistemic invariant. I also argued that a

satisfactory, moderately complex epistemic invariant may exist. Therefore, I do not think

any false assumption is presupposed.

Against Laudan‟s new demarcation problem, I argued that science is relevant to

confirmation, so solving the original problem is relevant to solving the new problem. I

also argued that there are other valuable aspects to science that are not identified by the

new problem. Therefore, I do not think the new problem is a suitable replacement.

Some readers may be disappointed that my aim has been so modest: to defend the

search for a solution, rather than to propose a brand-new solution to a very old problem. I

am not convinced that a brand-new solution is required. I have identified several recent

developments and current areas in the philosophy of science (for example, Bayesianism and

Experimentalism) that I regard as particularly promising. I suggest that we look to the

current research of other philosophers for improvements to the demarcation between

science and non-science.

To return to my central question, and with apologies to Mark Twain: Laudan‟s report

of the death of the demarcation problem is greatly exaggerated.

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Appendix A Some Non-Ideal Definitions

A.1 Ideal Definitions

According to Swartz (1997), the classical theory of concepts emerges from the

philosophical tradition of looking to geometry and mathematics for paradigm cases of

reasoning and knowledge. Many philosophers considered these to be particularly clear and

precise cases from which to develop rules for use in non-geometrical or non–mathematical

arenas. Geometrical concepts such as square and triangle can be defined by a set of

conditions that are individually necessary and jointly sufficient (e.g. „closed figure, has four

straight sides, sides are all equal in length, interior angles each measure 90o, lies in a plane‟

for square; and „closed figure, has three straight sides, lies in a plane‟ for triangle). So, says

Swartz, philosophers expected that ordinary concepts should also be defined in this way.

But in recent times, philosophers have argued that geometrical concepts should not be

considered exemplary or paradigmatic cases; rather, they should be considered special or

exceptional cases. Most ordinary concepts are not as simple and precise as geometrical

ones.

Traditionally, definitions have been judged by how well they satisfy the following ideal

requirements:52

(i) Accuracy: A definition should be neither too broad nor too narrow. It should include all

the cases that are instances of the concept, and exclude all cases that are not instances

of the concept. For example, „unmarried adult male philosopher‟ is too narrow a

definition for bachelor; but „unmarried adult‟ is too broad.

(ii) Precision: A definition should be neither too vague nor too precise. The definition

should match the degree of precision of the concept being defined. For example, „adult

female‟ for woman satisfies this requirement, but „female at least 18 years old‟ does not.

(iii) Non-circularity: A definition should not be circular. For example, if „desirable‟ defines

good and „good‟ defines desirable, then these definitions are circular.

(iv) Conjunctive: A definition should consist of a set of criteria that are individually necessary

and jointly sufficient. For example, „adult‟, „unmarried‟ and „male‟ are jointly sufficient

criteria for bachelor. But none of these criteria is sufficient on its own; all three must be

52 This list of requirements is compiled from (Yagisawa, 1999) and (Swartz, 1997).

81

satisfied in order to call someone bachelor. So the logical structure of the definition is a

conjunction of properties.

(v) Essentials: A definition should specify only genuinely essential properties of the concept,

not accidental properties. For example, defining vertebrates as „things with both

vertebrae and a liver‟ violates this requirement, for although all actual vertebrates have a

liver, it is logically possible for a vertebrate to lack a liver. Vertebrae are an essential

property of vertebrates; a liver is merely an accidental property.

These requirements stand together in an uneasy alliance as combined conditions for an

ideal definition. Individually, they are often achieved; jointly, they are usually unachievable

– one requirement usually infringes on another requirement.

A.2 Non-Ideal Definitions

There are many examples of non-ideal definitions that do not satisfy one of the criteria (i)-

(v).

I have already discussed family resemblance definitions for ordinary terms such as

game. On this view, an activity qualifies as a game iff it is similar enough to other accepted

games. Presumably, it must share enough of the same properties (to a sufficient extent).

But none of these properties need to be necessary ones: an activity could lack any particular

property but possess all the others, and be judged similar enough to qualify as a game. A

definition like this is not a conjunction of individually necessary and jointly sufficient

conditions, i.e. it does not satisfy requirement (iv).

A definition that specifies essential properties of a concept may turn out to be circular.

This depends on how we interpret the notion of essential properties. On the one hand,

one might take essential properties to be unique, individual properties. For example, one

might argue that the essential property of apple is „appleness‟. However, this is open to the

charge of circularity, for appleness must then be defined as „the property displayed by all and

only apples‟. Therefore, satisfying requirement (v) may violate requirement (iii). On the

other hand, one might take essential properties to be more general properties. For

example, one might argue that the essential properties of apple are „rounded, firm, juicy,

edible fruit‟. This definition appears to avoid circularity, but it is too broad to define apple.

„Rounded, firm, juicy, edible fruit‟ is also satisfied by orange, nectarine, and grape. So, trying to

satisfy requirements (iii) and (v) may cause one to violate requirement (i).

Moreover, to give a definition for a particular purpose, one may need to violate one or

more requirements. For example, sometimes the purpose of the definition is to clarify the

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concept in order to reduce its vagueness. In such cases, (ii) is superseded by a new

requirement (ii*):

(ii*) The definition should be more precise than the concept being defined.

Satisfying this new requirement may come at the expense of violating (i): increasing

precision may lead to a broader or narrower definition of the concept.

Sometimes the purpose of the definition is to explicate the concept, that is, to provide

an analysis of the meaning of the concept. Often an explication brings to the surface a new

meaning – or the „real‟ meaning, or a lesser-known meaning – of the term. In such cases,

requirements (i), (ii) and (v) may be violated: explication may result in a slightly different set

of instances, more or less precision, and it may identify something that was not originally

an essential property.

Sometimes a definition is ostensive rather than verbal. A definition is ostensive when a

concept is defined by showing in some way, e.g. by example, demonstration, or by pointing

(e.g. saying “red is that colour” while pointing to something that is red). A definition is

verbal when a concept is defined by an explicit description of the relevant properties. It

isn‟t clear that any of the requirements (i) to (v) can be satisfied by an ostensive definition.

In general, many ordinary definitions can‟t meet all of these ideal criteria, so maybe it is

unrealistic to expect the demarcation of science to meet all of Laudan‟s stringent

requirements (several of which are the same).

A.3 Gold

Often the same concept can be defined in several different ways – sometimes satisfying the

requirements, sometimes violating them. For example, consider the following definitions

of gold:

„Element with atomic number 79‟: This definition satisfies all of the requirements, and

yet it may be considered unsatisfactory for some purposes. To scientists, „element with

atomic number 79‟ is very informative: it tells them about the atomic structure of gold,

which in turn tells them about the properties of gold, and hence a lot about the concept gold.

For many people, however, this definition does not seem to be very different to the

definition „element identified by the symbol Au‟; atomic numbers mean little to many non-

scientists. So other definitions are often employed.

„Soft, bright, yellow transition metal‟: This definition violates requirement (ii) because

it is less precise than the concept, and requirement (iv) because the specified conditions are

individually necessary but not jointly sufficient, and yet it is satisfactory for some purposes.

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To the non-scientist, this definition says much more about gold than the former definition.

It characterises gold in terms of general properties, and guides the application of the term

„gold‟ in everyday situations.

„Alloy with at least 9 karats of pure gold‟: This definition violates most of the

requirements, and even seems to violate requirement (iii), by defining gold using „gold‟ – and

yet this definition is satisfactory for some purposes.53 In particular, this definition stipulates

a standard for what should be called „gold‟. Pure gold is too soft to use for jewellery, so it

is combined with other metals to make it harder. The resulting alloy is referred to as „gold‟,

and displays a karat54 marking – this tells the buyer the purity of the gold. Authorities in

many countries only allow alloys above a certain level of purity to be marketed as „gold‟. In

Australia, the lower limit is 9 karats; in the USA, it‟s 10 karats. This level is arbitrary, but

once set it provides a necessary condition for calling something „gold‟. This definition of

gold doesn‟t tell us very much about the nature of gold; but it precisely demarcates things

that can be marketed as „gold‟ from things that cannot.55

The gold example shows that it can be appropriate to define ordinary concepts

differently for different purposes, even if this results in violating some of the ideal criteria.

This principle can clearly be extended to definitions of science. So it is plausible that a

demarcation criterion could be sufficient for philosophical and/or practical purposes,

adequately motivating the philosophical search for it, even though it does not meet one or

more of Laudan‟s stringent requirements.

A.4 Science

Science has also been defined in different ways, depending on the purpose of the definition.

Consider the following definitions of various types:

53 One could argue that circularity is avoided by defining pure gold in some other way. Even so, the karat

definition of gold is distinct from the others, and serves a specific practical purpose better than the atomic

number definition.

54 A „karat‟ is a measure of the purity of gold, where pure gold is 24 karats.

55 The concept gold may also be explicated in terms of its use as a figure of speech. For example, when we say

„good as gold‟ we are not concerned with the element and its atomic structure, we are talking about a shiny

metal which symbolises wealth and beauty. When we talk about the proverbial „pot of gold‟ at the end of the

rainbow, we are not concerned with its karat marking – we are talking about something more elusive. When

we say „that‟s gold‟ in response to a statement, idea or plan, we are not necessarily being ostensive, we may be

saying that we like the plan.

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a) A lexical definition: 56 „Systematic and formulated knowledge‟ (Sykes, 1989: 939).

b) A definition by genus and species: 57 „Empirical knowledge‟ (LaDuke, 2008).

c) An ostensive definition: „Science is that kind of activity‟ – where „that‟ identifies, say,

neuroscience, a chemistry experiment, or a copy of the Journal of Applied Physics.

d) A persuasive definition: 58 „The department that gets all the funding‟ (Gieryn, 1983); or

„our most reliable source of knowledge‟ (Hansson, 2008).

e) A recursive definition:59 „Physics is a science; any activity that uses similar methods,

accepts similar theories or publishes in similar journals to Physics is also a science;

nothing else is a science.‟

I take it that none of these definitions, on closer examination, constitutes a demarcation

criterion that would satisfy most philosophers. They are very far from ideal. Nonetheless,

they are not completely without merit, and they may be helpful for their intended purposes.

Similarly, although solving the demarcation problem requires something better, it may well

be something imperfect, and it might be a criterion tailored to a specific purpose (e.g. to

theoretical insight rather than to practical applications).

A.5 Diamonds

The demarcation of diamond provides a very clear illustration of the difference between

demarcations for theoretical and practical purposes.

There is a well-established distinction between diamonds and imitations: diamond is

pure carbon crystallised in octahedrons; imitations are not. Therefore, one could argue that

the criterion „pure carbon crystallised in octahedrons‟ is sufficient to demarcate diamond. If

the purpose of the demarcation is merely to say what the difference between diamonds and

56 This is a definition designed to specify the conventional meaning of a term. This is the kind of definition

normally found in a dictionary.

57 This is a definition that (i) specifies a type to which all the entities must belong (the „genus‟), and then (ii)

specifies a subtype to which only the relevant entities belong (the „species‟).

58 This is a definition designed to affect or appeal to the psychological states of the party to whom the

definition is given, so that a claim will appear more plausible to the party than it should.

59 This is a definition in three clauses, in which (1) the expression being defined is said to apply to certain

particular items (the base clause); (2) a rule is given for reaching further items to which the expression applies

(the recursive, or inductive clause); and (3) it is stated that the expression applies to nothing else (the closure

clause).

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non-diamonds is, then this criterion may be adequate. However a demarcation may have

another purpose, and this criterion may not be useful. Consider, for example, a jewellery

valuer trying to decide whether a particular gem stone is a genuine diamond or an imitation.

There are (at least) two very good imitations on the market: cubic zirconia, made from

zirconium oxide; and moissanite, made from silicon carbide. Unfortunately, identifying

gem stones on the basis of their chemical structure is costly and invasive – it is not

something for which the valuer has the equipment or expertise. Chemical structure might

be essential to the concept diamond, yet it has no practical application for the valuer. I shall

call this kind of property a „theoretical essential‟.

The valuer must consider properties that are observable to the trained person, such as

colour, weight, impurities, hardness, sparkle, reflection and refraction. Good imitations,

like cubic zirconia and moissanite, display many of the properties of diamond, so the

valuer‟s identifications may be performed in a piecemeal fashion. For example, if she

examines the back of the stone with a magnifying glass, and sees two sets of facet lines,

then she will know that she is seeing a strong double refraction, and can conclude that she

is looking at moissanite, not diamond. However if she sees a single set of facet lines, then

she is either looking at diamond or cubic zirconia – she doesn‟t know which. So she must

make another observation. For example, she can see how much light leaks out the back of

the stone. If there is a lot of leakage, then the stone is cubic zirconia; if there is very little

leakage, then the stone is diamond. This observation, though very reliable, can only be

made if the stone is cut in a certain way, so it cannot always be used. Alternatively, the

valuer may examine the stone for impurities. If she sees impurities, then she will know that

the stone is diamond – albeit, a poor quality diamond. But if she can‟t see any impurities,

the stone might be an extremely high quality diamond, or it might be cubic zirconia. She

will continue making observations – she has an extensive selection of tests of this sort to

choose from – until she is able to tell whether or not the stone is a diamond. I shall call the

properties identified by these sorts of tests „practical indicators‟.

The theoretical essential – the chemical structure of diamond – is present in every case

of diamond, but it cannot be identified in any case. The practical indicators – the valuer‟s

„mixed bag‟ of tests – are not individually present in every case of diamond, but some

revealing combination of these features can be identified in every case. The demarcation of

science may turn out to be similar. So even if philosophers agree that they have solved the

demarcation problem to their satisfaction, this may not satisfy everyone else!

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Appendix B Some Complex Demarcations

There are several demarcations of science in the recent philosophical literature that are

moderately complex, but none of them have been criticised for their complexity.

B.1 Thagard

Thagard (1988) thinks that the various scientific disciplines, fields, programs and theories

display a „family resemblance‟. He specifies a handful of properties that are typical of

science, and which can be contrasted with another handful of properties that are typical of

pseudo-science. Thus, he specifies a „conceptual profile‟ for science and a contrasting

conceptual profile for pseudo-science (Thagard, 1988: 170), as shown in Figure B.1.

Science Pseudoscience

Uses correlation thinking Uses resemblance thinking

Seeks empirical confirmations and disconfirmations

Neglects empirical matters

Practitioners care about evaluating theories in relation to alternative theories

Practitioners oblivious to alternative theories

Uses highly consilient and simple theories

Nonsimple theories: many ad hoc hypotheses

Progresses over time: develops new theories that explain new facts

Stagnant in doctrine and applications.

Figure B.1 Thagard’s two conceptual profiles.

Thagard expects that instances of science will display more features from the profile of

science than from the contrasting profile of pseudo-science. Very few (if any) of the

sciences will display all of the characteristic features of science, and many of them will

display one or two features of pseudo-science. Any instance of science will resemble other

instances of science, though they may not have identical profiles. Any instance of science

will also resemble some instances of pseudo-science in some ways.

Thagard specifies only a handful of properties of science, but (as I explained in section

2.5.4) I consider this a relatively complex demarcation, for three reasons. Firstly, he uses

more features than, say, Inductivism or Falsificationism. Secondly, some of these

properties seem quite complex in themselves. For example, it might take a lengthy

description to spell out in simple terms what counts as confirmation and disconfirmation,

or what counts as consilience and simplicity. Thirdly, the logical function required to

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capture „family resemblance‟ is considerably more complex than, say, a conjunctive

definition. A conjunctive definition just says that all its properties must be present for

something to count as a science. But a family resemblance definition needs to capture how

many properties must be present and in what proportions. Thagard thinks this complexity

is unavoidable – science is complex, so the demarcation criterion must be complex too.

Thagard‟s conceptual profiles have been criticised by many and accepted by few. What

is relevant for my purposes is not the quality of his solution, but the kinds of criticisms that

have been made. His solution has been criticised because it is not accurate with respect to

the clear cases of science and clear cases of pseudo-science. For example, Derksen (1993)

argues that Freud never confused resemblance with cause, he paid attention to empirical

matters, and his theory was not stagnant in its doctrine and applications. Psychoanalysis –

widely regarded as one of the clearest cases of pseudo-science – fails to fit Thagard‟s profile

of a pseudo-science (Derksen, 1993: 18). Thagard‟s solution has not been criticised because

of its complex structure.

B.2 Lugg

Lugg (1987) argues that we should distinguish between science and pseudo-science in

much the same way that we distinguish between valid and invalid arguments. There is no

single way that an argument can be invalid; rather, an argument can commit a number of

fallacies that render it invalid. This does not lead us to conclude that there is no real

distinction between valid and invalid arguments. Nor do we conclude that the only

distinction is between true and false conclusions – an invalid argument might happen to

have a true conclusion, and a valid argument might happen to have a false conclusion.

Similarly, Lugg argues that pseudo-sciences can be structurally flawed in many different

ways; but we shouldn‟t conclude that there is no genuine, useful distinction between

science and pseudo-science. We have a number of clear cases of pseudo-science and

analysis of these will provide us with a basis for identifying new cases of pseudo-science.

Some new cases will contain flaws that we have identified in the clear cases, which will

allow us to identify these new cases as pseudo-science. However, it is possible to identify a

new kind of flaw, so the list provided by the clear cases of pseudo-science is revisable.

Lugg‟s demarcation criterion could become extremely complicated: he might specify as

many kinds of flaws as there are cases of pseudo-science. Some cases are flawed before

they are subjected to empirical testing; others are flawed in the way they deal with failures

or refutations. For example, he says:

88

1. Action theory “merely repeats what everyone knows in a misleading and confusing

way”;

2. Psychoanalysis incorporates a set of auxiliary hypotheses that shield it from

refutation;

3. UFOlogy involves the “suspect strategy” of shuffling forward new cases when the

old ones are shown to be problematic; and

4. Parapsychology fails to conform to the canons of good experimentation and sound

statistical analysis.

Once again, this demarcation criterion has not been accepted by the majority of

philosophers. It has been criticised for its circularity and scientific prejudice. For example,

Derksen (1993) criticises Lugg for taking a „good structure‟ to be that which is displayed by

clear cases of science and „bad structure‟ to be that which is displayed by clear cases of

pseudo-science. This distinction, he says, amounts to the presupposition that science is

good and pseudo-science is bad, because Lugg doesn‟t justify it on any other grounds. But

the point I wish to stress is that Lugg‟s solution has not been criticised for its complexity.

B.3 Derksen

Derksen himself uses an analysis of pseudo-science to suggest an alternative solution

(Derksen, 1993). He argues that the key to distinguishing between science and pseudo-

science is to realise that pseudo-scientists are pretending or trying to be scientists. Hence,

there will be a considerable amount of resemblance between science and pseudo-science.

He says that scientists are committed to reliable knowledge and recognise human fallibility,

so the pseudo-scientist must show commitment to these things as well. But by identifying

the characteristic pretensions of the pseudo-scientist, we can distinguish between science

and pseudo-science.

Derksen identifies “the seven sins of pseudo-science”. These are:

1. The dearth of decent evidence;

2. Unfounded immunisations;

3. The ur-temptation of spectacular coincidences;

4. The magic method;

5. The insight of the initiate;

6. The all-explaining theory; and

89

7. Uncritical and excessive pretension.

He says that these are all characteristic failings of the pseudo-scientist. But (like Thagard)

he says they are not individually sufficient conditions for pseudo-science. This is because

scientists can make mistakes, and we shouldn‟t dismiss them as pseudo-scientists on the

basis of one or two sins.60 Nor are the seven sins jointly necessary: a pseudo-science may

not display all of them. But Derksen does argue that the seven sins are jointly sufficient for

dismissing something as pseudo-science.

Most of the criticism of Derksen has come from Lugg (1995), who has returned fire by

arguing that Derksen has failed to avoid his own criticism of Lugg‟s (1987) view.

Derksen‟s „sins‟, he says, really amount to „structural flaws‟, because while Derksen says he

is talking about pretensions, he is really talking about practice. Derksen‟s sins may be more

refined than Lugg‟s own „structural flaws‟, but they are still open to the same charge of

scientific prejudice that Derksen levelled at Lugg. However, while Lugg criticises Derksen

for his inconsistency, Lugg doesn‟t actually see this charge of scientific prejudice as

warranted. He thinks that the standards of good scientific practice are situated in some

sort of epistemological twilight zone. We can challenge these standards. But in the

absence of convincing justification or refutation, they ought to be followed nonetheless –

anyone who doesn‟t follow them “is asking for trouble” (Lugg, 1995: 324). Again, the

point I wish to emphasise is that Derksen‟s seven-sin solution is just as complex as those of

Lugg and Thagard – yet no one has criticised it for its complexity.

60 He even says “Put paradoxically, the sins of pseudo-science lose their sinfulness within science” (Derksen,

1993: 38).

90

Appendix C Other Virtues versus Confirmation

Features such as novelty, simplicity, explanatory breadth and usefulness are often regarded

as virtues of a scientific theory. But Laudan maintains that when we are deciding whether

to believe a scientific theory, we should only consider the virtue of confirmation. He

concedes that other virtues can be important for deciding which theories to pursue, but not

for deciding which theories to accept.

I think that the following hypothetical example illustrates that other virtues are

important. They are certainly important when deciding which theories to pursue. They

seem to be important enough that a difference in these virtues can outweigh a small

difference in confirmation. In fact, they seem to retain this importance even after a lot of

data has been accumulated. This suggests that they might even be significant for deciding

which theories to accept.

C.1 Wonten versus Newton

Suppose two chaps, Newton and Wonten, are sitting under a tree when two apples fall

(knocking them on their heads). As they sit there (rubbing the bumps on their heads) they

each generate a Law, as described in Figure C.1.

Wonten’s Law of Falling Apples Newton’s Law of Universal Gravitation

When apples become detached from trees, they fall down.

Every point mass attracts every other point mass by a force pointing along the line intersecting both points. The force is proportional to the product of the two masses and inversely proportional to the square of the distance between the point masses according to the formula:

F = G

m1m2

r2

Where,

F is the magnitude of the gravitational force between two point masses;

G is the gravitational constant;

m1 is the mass of the first point mass;

m2 is the mass of the second point mass; and

r is the distance between the two point masses.

Figure C.1 Two Laws describing the behaviour of apples.

Even after extensive testing, it seems that Wonten‟s Law must be better-confirmed

than Newton‟s Law, for two reasons. Firstly, it is easier to make the observations that are

relevant to Wonten‟s Law. To confirm Wonten‟s Law, one only needs to observe apples.

91

To confirm Newton‟s Law to the same extent, one needs to observe apples, atoms, planets

and countless other things – many of which are (arguably) unobservable! Secondly,

Wonten‟s Law is entailed by Newton‟s Law.61 So Wonten‟s Law must be at least as

probable as Newton‟s Law62, and according to Bayesian confirmation theory that means it

is at least as credible.

So, if we always prefer well-confirmedness to the exclusion of all other epistemic

virtues, then we should prefer Wonten‟s Law. Yet we don‟t – we prefer Newton‟s Law.

Newton‟s Law has epistemic virtues that Wonten‟s Law doesn‟t have, which seem to make

it preferable to Wonten‟s Law. For example, it gives a broader explanation than Wonten‟s

theory,63 it makes some interesting novel predictions,64 and it is quantitative rather than

simply qualitative.65 If we accept Newton‟s Law for these reasons even after extensive

testing, then in Laudan‟s terminology, it looks like these virtues are guiding acceptance rather

than just pursuit.

This hypothetical example supports an alternative view of the other virtues. Well-

confirmedness is one of a number of virtues that indicate epistemic merit. When faced

with a choice between two rival theories, we don‟t always prefer the better-confirmed

theory. We sometimes prefer the theory which displays other virtues, such as novelty,

simplicity, explanatory breadth, or usefulness. If we focus on well-confirmedness to the

exclusion of all other epistemic virtues, then we will miss many other indicators of

epistemic merit. Ceteris paribus, scientists prefer well-confirmed theories. But usually ceteris

61 Newton‟s Law only entails that detached apples will fall when it is combined with some background

assumptions about the approximate masses of objects such as the earth and apples. But these assumptions

are unproblematic, and even if they were challenged, this would be more of a problem for Newton‟s Law

than for Wonten‟s.

62 This can be demonstrated as follows. Let N = „Newton‟s theory is true‟ and W = „Wonten‟s theory is true‟.

The entailment NW is equivalent to ~(N&~W) which entails that P(N&~W) = 0. From basic probability

theory, we know that P(N) = P(N&W) + P(N&~W), so here P(N) = P(N&W). Similarly, P(W) = P(N&W)

+ P(~N&W). But P(N&W) P(N&W) + P(~N&W), so P(N) P(W).

63 It is interesting that the thing that makes Newton‟s Law less well-confirmed makes it preferable to

Wonten‟s Law!

64 Wonten‟s Law makes novel predictions too, but these are neither interesting nor risky.

65 Even if we are only interested in apples, we might prefer Newton‟s Law to Wonten‟s Law. Wonten‟s Law

tells us that apples fall, whereas Newton‟s Law tells us how fast they will fall.

92

non paribus. If a theory displays other important virtues, then they might prefer it to a

theory that is better-confirmed.

93

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Minerva Access is the Institutional Repository of The University of Melbourne

Author/s:

WALSH, KIRSTEN

Title:

Has Laudan killed the demarcation problem?

Date:

2009

Citation:

Walsh, K. (2009). Has Laudan killed the demarcation problem? Masters Research thesis,

Arts - School of Philosophy, Anthropology and Social Inquiry, The University of Melbourne.

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