Evolutionary theory of human decision-making.

(The meta- and post-evaluation of decisions as the basic element of decision-making)

Abstract: This article seeks answers to the following questions – insufficiently resolved by research thus far – regarding the evolution of human decision-making: (1) what type of decision-making processes do humans employ; (2) why did these specific processes emerge; (3) how does the entire hierarchical decision-making system work. As a framework, this analysis employs the so-called “Matryoshka model”, which describes the human as a composite of behavioural programmes built on top of one another. The author explores the changes in the evolutionary environment and identifies the decision-making processes that emerged as successive layers in the course of evolution. He analyses how programme-controlled ethologically-based behaviour is broken down into individual decisions based on specific analyses. He further shows how a human, based on her impressions of the circumstances at hand – the meta-level analysis –, chooses whether to apply a decision-making model based on human ethology, or one using mental modules, symbols, cultural “rules of thumb” or rationality. It finally shows how certain situations can simultaneously render several decision-making models operative, and that in such situations our decision emerges from their complex interaction.

Economics and to some extent other social sciences, too, have up until the last decade

based their theories on the model of a rational, selfish and profit-maximising Homo

Oeconomicus (Friedman, M., 1953). Everyday experience and research suggests, however,

that humans employ diverse, often contradictory decision-making methods, such as for

example satisficing, i.e. selecting the first option that satisfies a particular need or selecting an

option that appears to address most needs rather than the “optimal” solution (Simon, H.,

1955); the expectation of reciprocity (Camerer, C. and Fehr, E., 2006); altruism (Harbaugh,

W. et al., 2007); the weighing of moral principles (Zaki, J. et. al., 2011; Gintis H. et al., 2005:

3-39); following symbols (Vohs, K. et. al., 2006) and prejudices (Bernhard, H. et al., 2006);

obeying emotions (Akerlof, G. and Shiller, R., 2009: 24-37); acquiescing to pressure from

peer group or authority (Sutter, M., 2009: 2247-2257); obeying gut-reactions (Gigerenzer, G.,

2004); and the use of non-rational rules (Kahneman, D. and Tversky, A., 1979; Rabin, M.,

2000). This wide spectrum of decision-making processes raises the following question: What

is the reason for this diversity and how is the choice of the dominant factor in a given

decision-making situation determined?

Several scholars have based their interpretation of human behaviour on hierarchically

ordered programmes of behaviour. MacLean was the first to develop a model of the brain that

employs three decision-making levels (MacLean, 1990). Donald identified three major

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transitions on the evolutionary road leading from primates to humans, each of which he

associated with a “complex of new cognitive modules” (Donald, M. 1991: 29). Jablonka and

Lamb argued for a three-level structure of a genetically based behavioural programme

(Jablonka, E. and Lamb, M., 2005: 341-344). Frans de Waal employed a three-tiered model of

morality – moral sentiments, the community’s moral expectations and moral judgment (Waal,

F., 2006). Differently from the three-tier system, the so-called dual process model has

proposed a two-level model of decision-making – a quick and automatic first level and a

slower secondary system that applies rational consideration as well (Evans, J., 2003 and

Kahneman, D., 2011: 21). E. Ostrom refers to the instinct of equity, then to social norms, to

heuristic rules and finally to the model of bounded rationality as decision-making levels built

on top of one another (Ostrom, E., 2010: 659-660).

Research has thus far failed to sufficiently clarify three basic questions: 1) why does a

given number of levels exist; 2) why did these particular levels emerge; and 3) how the entire

system of hierarchically structured decision-making works. We seek the answers to these

questions based on the so-called “Matryoshka” model, which describes the human being by

relying behavioural programmes that she developed in response to the challenges she

encountered in the course of biological and subsequently social evolution (Marosan, Gy.,

2011: 472). In this study, the Matryoshka model is adapted to human decision-making as we

trace historical changes in the evolutionary environment and then identify the decision-

making processes that were formed in the course of evolution and built on top of one another.

This provides the foundation for the general model of human decision-making (see Graph 1).

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1. Graph 1: The „Matryoshka” model of human decision-making

The (human) ethological level of decision-making

On her way towards becoming human, the Hominid species was basically composed of

pro-social individuals that lived in communities (Ladeveze, S. et. al., 2011: 83). Her

behaviour can be best understood based on MacLean’s model of three “decision-making

centres” built on top of one another. (LeDoux, J., 2000: 160) At the lowest level is the

“wired” “reptile-brain”, which operates a so-called “click-whirr” mechanism. (Cialdini, R.

2001. 3.) The so-called “mammalian brain” is built on top of the lowest level; it has grown as

compared to the earlier, reptilian version, primarily in response to challenges emanating from

life in a society, and it is capable – through individual learning – of pattern recognition

(Rowe, T. et. al., 2011: 955). The “primate brain”, which occupies the highest level, can

identify patterns of social experience as well, and that in turn serves as the basis of emotional

responses that generate decisions (Flack, J. et al., 2007: 1581).

As a result of continuously emerging new problems, beings instinctively “choose” at

which “decision-making” level they will “handle” a problem, and then apply the model of the

given level and select the appropriate response. Since identifying threats and survival are the

fundamental problems of existence, even highly evolved beings – including humans – react to

such challenges with pre-determined behavioural patterns that match the given trigger. The

animal brain that “occupies” our modern skull induces a reflexive response to situations of

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danger even before we can analyse the given situation rationally (Panksepp, J., 2004). Thus

whenever the modern human encounters – fortunately rarely – the combination of life-

threatening circumstances and pressing time constraints (e.g. a ship accident), “animalistic”

behaviour overwrites the “human” factor in her decision-making (Frey, B. S. et. al., 2010).

At the same time, even before the emergence of the modern human, the optimisation of

time expended the search for food, the recurring problems of one’s status within the

community and mutual assistance become ever more important. It is the ethological

“programmes” at the higher levels of the MacLean model that offer adaptive solutions to

problems. This is the point at which emotions begin to play a role in controlling behaviour,

predominantly in response to the challenges posed by the management of social relations.

Emotions are “instinctively” developed attitudes that prompt the given being’s adaptive

action – which is appropriate for the reigning situation – when facing a particular problem

(Scherer, K., 2005: 699-702.) Life experience progressively activates emotion-generating

programmes, fills them with information and then turns them on. Unless it requires an

immediate reflexive response, a problem triggers the emotion-generating programme, which

alerts the entire body for a response, and then proceeds to guide the individual through the

behaviour that is appropriate to the given situation. Though as compared to predetermined

behavioural patterns that are engaged in response to triggers, behaviour based on emotional

responses is slower, it also allows for a more flexible reaction (Nesse, R. et. al., 2009: 129).

Emotions are especially crucial for regulating social behaviour. That is what makes the

decisions of primates – as revealed in experiments –, which bear a striking resemblance to the

reactions of humans – and which are also influenced by emotions – intelligible to us (Gomes,

C. et. al., 2011: 2183). The impact of these emotion-generating programmes manifests itself

in the modern human – insofar as the given conditions recall the general conditions that

prevailed at the time the programmes developed – in terms risk assessment, sexual stimuli,

family bonds, status within the group (dominance or submission) or the keen awareness of

strict reciprocity. Research has shown that loss aversion – i.e. when the fear of expected

losses outweighs the hope of potential benefits – tends to prevail in human behaviour

(Kahneman, D., 2003: 164). The same behavioural patterns have also been identified in apes,

however, which suggests that the roots of this attitude are found in evolutionary biology

(Chen, K. M. and Santos, L., 2009). Other research has established a clear link between the

activity of the “amygdala” and the so-called framing effect – that is when we apply

experience gathered from previous direct encounters rather than assessing the facts

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objectively –, which also suggests that emotional assessment plays a key role in decision-

making (Martino, B. et al., 2010: 3788).

As the above show, even the innermost core of the Matryoshka doll is complex. The

effect of each of these “radiates out” to the later levels and may influence human behaviour in

complex ways. Nevertheless, in the following analysis we will refer to them as a single level

– this will obviously be a simplification – in the context of human decision-making, that is as

the level that serves as the basis for the ethological programme that we share with primates.

The decision-making level of mental “modules”

The fundamentally new phase on the road towards the emergence of the human began

with the change that brought about the anatomically modern human (AMH) in the period

between roughly 1.5 million BCE to 200,000 BCE. With regard to decision-making, the most

important event in this period was the substantial growth in brain-size from 850cm³ to some

1350cm³, in parallel with major changes in the brain structure. For the most part, these

changes occurred some 800,000-200,000 years ago (Potts, R., 2011: 43). From here on

behaviour is controlled by turning on the suddenly massively enlarged portions of the brain.

The increase in brain size comes at a price: it encumbers the process of birth, makes growing

up more risky and requires greater energy and time commitments. Hence a larger brain is only

“worth it” if it significantly enhances fitness. And the most recent research clearly shows that

the larger brain has indeed been efficient at improving fitness (Navarrate, et al., 2011: 91).

The impact that this level with its unique features has on human behaviour has been

revealed by evolutionary psychology (EP). To describe circumstances that embody particular

challenges, EP introduced the concept of the environment of evolutionary adaptedness (EEA).

(Incidentally, the Matryoshka model posits that this is not the only period which is an

environment of evolutionary adaptedness, but so is in fact every period of evolutionary

history that gave rise to challenges that were “unmanageable” with the hitherto existing

instruments and thus elicited the “crafting” of new instruments). At the same time EP

suggested that such an environment leaves an imprint in the brain, whose effect is that

problem resolution units are formed that work as modules. These modules specialise in

identifying and solving certain types of problems (Cosmides, L. and Tooby J., 1997). The

mental modules are stable components of the brain and play a functional role influencing

behaviour (Barrett, J., 2004: 5). The problems that humans encounter – mating, identifying

threats, testing reliability, exchange “services”, identifying status – render the appropriate

module “operational”, and those in turn develop particular attitudes, preferences that push

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behaviour in direction of an adapted response. Modules are slower than the ethological

programmes from earlier periods, but they shape behaviour with a finer and more accurate

attunement to an increasingly complex environment.

At first, modules were triggered by ethological signs, but in later periods they were

“turned on” by the social experiences gathered in the course of a life spent within a

community. Still later, symbols created during subsequent stages of development were also

capable of setting modules off. The phenomenon of priming, which is manifested in

experiments, points to humans’ persisting sensitivity to symbols. When a module - serving the

solution of a specific problem - is “activated” by the associated symbol, human behaviour,

even without conscious deliberation, changes appreciably (Tulring, E. et al., 1990: 301). The

effect of priming can be demonstrated in the context of a whole series of human behaviours,

be it rule-abiding, cheating, generous or selfish. Thus for example economic behaviour is

“instinctively” influenced if either symbols of money or the concept of money crop up in a

given situation (Vohs, K. et al., 2006: 1154). Money exerts a motivating effect even if

experimental subjects are only subliminally aware of its presence (Pessiglione, M. et al.,

2007: 904). Prosocial behaviour also influences decision-making when the environment in

which a given decision is made manifests symbols that invoke children (Gino, F., 2011).

With regard to economic decisions, mental modules are especially important on

account of their role in shaping preferences (Behrens, T., et al., 2009: 1161). Preferences are

criteria of evaluation that control our choices in life without conscious deliberation. Due to

her circumstances, the creature that gradually morphs into a human has tended to value small

but immediate profits and significantly “discounts” greater but more remote benefits. The

instinctive and large-scale discounting of the future is therefore a consequence of this epoch.

Research has shown that the “closer” historically speaking an individual is to the period when

humans emerged, or the closer she feels to that period, the less she tends to value the future.

(Jones, D., 2009: 782-783 and Kirby, K. et al., 2002: 303). Similarly, the influence of the

workings of modules can be made out when a human living in a modern environment makes

decisions concerning support for the needy in society (Petersen, M., 2012).

One of the still effective and instinctually manifested “imprints” stemming from the

persistent privation humans endured in the course of evolution is that we tend to take more

than we need of things that we have little of or which are available for free. We tend to see

things that we sense are or might become scarce as more valuable, and we are willing to

spend more on these. A similar logic applies to things that we have to compete for and which

we may lose; we tend to value these things more – and are willing to pay more for them –

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than we would in a situation in which we would not have to compete for them. In the context

of modern market economies, those in the business of selling items regularly exploit these

instinctive preferences by creating situations that trigger the application of these preferences

(Cialdini, R., 2001).

Beginning with this period, considerations regarding status in a group of increasing

size – in which we need to be more flexible and more refined as compared to earlier periods –

are also governed by mental modules. One of the “side effects” of the operation of the status

module is the setting that we not only assess what we have in terms of how it satisfies our

needs but also in comparison to what others have. We begin to weigh whether and to what

degree we should sacrifice resources to maintain our position and to improve our reputation

(Fliessbach, K. et. al., 2007: 1307). Because of their ability to improve group fitness, mental

modules simultaneously generate altruism that transcends blood ties, as well as ethnocentric

behaviour vis-à-vis foreign groups.

The decision-making level governed by symbols

Another turning point en route to becoming human occurred roughly 200,000 years

ago. That was when after growing continuously for millions of years, the increase in brain

size stopped and stabilised around 1350 cm³. This fact points to an evolutionary challenge.

There are two possibilities that might have had an impact on increasing fitness: the emergence

of the cultural sphere or the “development” of a more resistant body structure (Navarrate, et

al., 2011: 92). Research shows that the evolution of humans – some 100,000 years later –

clearly took a turn towards culture and symbolic behaviour (Hare, B., 2011: 293). The use of

symbolic objects (decorations, jewellery) becomes more frequent, as do symbolic activities

(funerals, celebrations), symbolic creations (paintings, sculptures, drawings), and symbolic

communication (speech) (Rossano, M., 2010: S 89). That is why this turn may be aptly

characterised as the “symbolic revolution” (Knight et al., 1995: 78-80).

Two new important developments appear that affect decision-making. For one, this is

when the – still ongoing – process begins wherein humans start to use instruments of culture

to establish a sphere between themselves and the natural environment. Though this sphere

enhances fitness, it also raises some particular challenges of its own (Henshilwood, C. et al.,

2011). At the same time a new behaviour-generating level is “built” in the human brain above

the level of mental modules. This is the level of mental constructs, consisting of symbolic

signs, that is language and symbolic thought (Rossano, M., 2010). The responsibility of

collecting, recording, testing and then transmitting or passing on individual and community

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experiences by the way of language or with the help of the elements of material culture

increasingly falls to this level. Speech is of fundamental importance because it can help to

significantly bolster the efficacy of common decisions (Koriat, A., 2012: 360). These two

factors together have led to a significant growth in fitness, which is demonstrated by the

growth in size and complexity of groups of humans living in communities.

The increase in group size played an important role in the spread of modern human

behaviour (Powel, A. et al. 2009: 1298). Growing group size, migration between groups, and

the exchange of knowledge are fundamental features of the survival and innovation of

cultures. Research amply demonstrates that populations grew by leaps and bounds in the

period indicated (Mellars, P. et. al., 2011: 623.) The increase in the number of individuals also

makes encounters with members of outsider groups more likely. The development of symbols

was in fact necessitated by the desire to accurately and quickly establish group identity as well

as the individual’s status within one’s own community (Efferson, Ch. et. al., 2008: 1844).

Ultimately these were the processes that led to the spread of modern humans across the entire

planet.

From the vantage point of decision-making this phase of evolution marked a

fundamentally new situation. First, with the development of the sphere of material culture a

gradually expanding protective layer emerges that shields the human from her natural

environment. As a result, the situation of the average human becomes more standardised and

predictable. A growing proportion of decisions pertains to routine situations that do not

involve pressing matters, and there is also more time to make these decisions. At the same

time, however, decisions require longer-term commitment and the weighing of forward-

looking considerations. Hence the importance of deliberation increases.

Second, the environment becomes suffused with symbols and these substantially

reorder the ranking of preferences as “prescribed” initially by instincts, and subsequently by

mental modules. Individual and communal experience can henceforth be laid down and

transmitted in the form of a set of rules made up of symbols – mental models. One segment of

these rules refers to the “management” of the natural and material environment (i.e. crafting

and using instruments). Another portion is made up of the social rules – norms – guiding

social behaviour. Norms are rules regarding the generally accepted understanding of desirable

behaviour within the community. They are socially – mostly by the way of language –

transmitted rules that everyone is required to abide by and to enforce (Bendor, J. et al., 2001:

1494-1495). The decisions of social creatures – e.g. offering or refusing support – are

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increasingly supplanted by symbolic signs, and they are increasingly controlled by symbolic

rules (Bernhard, H. et al., 2006: 912).

Third, this is the time when the human’s ability to create – based on mental models –

so-called counterfactual constructions emerges. Counterfactuals are things that do not exist in

reality but are artefacts created by our minds, and they offer the possibility of conducting

thought experiments with them as if they were real (Kray, L. J. et al.: 2010). A “world model” created out of concepts allows for the possibility of thinking through “what if…” type of questions. That is how the human becomes capable of forming a clear image of the outcomes of potential actions, an image that is suitable for informing her decisions.

Based on the aforementioned, this is when human decision-making as we understand it

today emerges. The decision “breaks away” and becomes distinct from previous “programme

controlled” behaviour; it is now a singular act, a choice rendered on the basis of analysing the

specific problems of a particular situation, wherein the methods are selected to match the

given problem. Experiments shows that unlike animals, whose behaviour is marked by a

continuous search for nourishment, the human is capable of choosing between two different

modes of searching, one based on a continuous and the other on a discrete decision (Kolling,

N. et. al., 2012: 95). From here on out therefore humans can identify decision-making

situations (indeed, with the help of language they can even write it down), can think through

the potential courses of action, predict the outcomes associated with each of them and can

also pair the respective outcomes with evaluative information.

The surviving legacy of this period – which appears when the right conditions apply –

is the widespread application of symbols in situations that require quick decisions and/or

situations involving uncertainty. That is why corporations turn their products into brands: they

label their wares and incessantly emphasise the benefits of the latter; they associate pleasant

images with the labels. Hence when quick decisions need to be made while shopping in a

supermarket, we choose a product from the mass of options whose symbol evokes a pleasant

memory that is instinctively recalled from our mind. And this is also how politicians use

ideological/national symbols (flags and insignia) so that they can “push” voters’ judgment in

the desired direction (Hassin, R. et. al., 2007: 19.757).

Indicators of social status constitute a distinct class among those symbols that

influence decision-making. Humans tend to rely to a greater degree on the opinion of persons

who wield symbols expressing authority – power and/or knowledge. That is no different

today: the opinion of high status individuals is more likely to be accepted in the course of

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shopping by consumers who are “on alert” to follow status, and they will also more likely be

followed by citizens during elections. The characteristic feature of this period is that exchange

– which is growing in importance – both within and between communities still predominantly

occurs in the form of gifts. A gift is a symbolic act that serves to bolster the co-operation and

coexistence of individuals and communities. This explains a rule that persists to this day: it is

improper to reject gifts and it is obligatory to reciprocate them. Reciprocal gift-giving reaches

beyond the family and extends to networks that encompass the community at large. (Ziegler,

R., 2008: 108). This culturally fixed reaction is exploited by the “covert persuaders” of the

modern age when they want to entice us into buying by offering food samples in shopping

malls, when they send product samples by mail, or when they seek to influence our opinions

with small gifts (Cialdini, R.: 2001).

The decision-making level that is influenced by cultural rules

The new stage in the process of social evolution, which occurred 12,000-8,000 BCE and is

called the Neolithic revolution, also raised novel adaptive problems. The community

gradually become separated from the natural environment by a socially crafted “shell”

furnished with cultural creations. That was when the basic common structure – called cultural

universal by anthropologists – of all societies known today emerged (Brown, D. E.: 1991).

The macro structure of the communities’ cultural universal is almost identical due to the

similarities in their lifestyle. Yet their micro-structure – the specific symbols, signs, terms,

myths, decorations, songs and rites – differ significantly. This makes it possible to easily and

quickly identify whether someone belongs to the group or not, and to decide on that basis how

to relate to her. Group identity becomes a fundamental element of group fitness (Efferson, C.,

et. al.: 2008).

With the emergence of settlements and production, the things necessary for life

become “commodities”, that is they are culturally produced needs that are acquired through

exchange and satisfied through a cultural act. One result is that circumstances become

predictable, threats decline and for the most part decisions tend not to be pressing. At the

same time, however, the complexity of decision-making situations increases along with the

number of options. Two distinct classes of decisions can be distinguished: the handling of

continuously recurring issues and decisions concerning the long-term survival of the

community or the individual. To handle this duality, the meta-level analysis of the decision-

making situation and the decision narrowly understood (i.e. determining potential outcomes,

clarifying evaluation criteria, weighing outcomes and finally choice based on the

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aforementioned) are “separated” during the decision-making. Before rendering a decision, the

decision-maker uses her subjective impressions of the circumstances at hand to “select” the

level at which she will handle the given decision, and then proceeds to apply the methods

associated with the given level.

At the same time, in order to facilitate orientation in increasingly complex situations,

decision-making (behavioural) rules are created based on collective experience. This is when

rules of thumbs (RoTs) emerge, which are rooted in tradition and are culturally transmitted.

They represent collectively tried and tested solutions for problems of everyday life. These

rules – which for the most part are considered not to be in need of any justification (“that’s

just the way it’s done”) and are often referred to as heuristics - are derived from everyday

behaviour that has mostly proven successful. RoTs are therefore decision-making models

applicable to a wide array of situations and they lay down the collective experience and

transmit it with the help of language. They may provide the basis for technological processes

(e.g. how to produce clothing or food), just as they can provide guidance for handling social

situations, even in the form of prejudices (stereotypes).

From here on the “programming” of the younger generation increasingly takes places

through socialisation. RoT-s are gradually transmitted between generations through everyday

situations. Their application, however, requires careful consideration, unlike the preferences

of mental modules or behaviour controlled by symbols. Though rules themselves are never

questioned, what is a subject of analysis is whether the given circumstances mandate that a

particular rule be followed, and if so to what degree it must be followed. A characteristic

“imprint” of this age are aphorisms – which are now also established through mental culture –

that persist even today through traditions of word of mouth. Once it was an ethological

programme that made the individual prefer immediate small benefits, then it was the mental

module and then, finally, it was the symbol. But from this age on it is verbally transmitted

rules – e.g. “better an egg today than a hen tomorrow” – that steer the individual towards

selecting the quicker, albeit seemingly less beneficial option. This is also how the so-called

“golden rules” – e.g. “One should not treat others in ways that one would not like to be

treated” – are created and transmitted as rules establishing behavioural principles deemed

desirable by the community. During the next stage, the institutional epoch, these rules are

transformed into divine commandments by the civilisations and religions of the period.

The level of decisions determined by “social institutions”

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During the “institutional revolution” that occurred 5,000-3,000 BCE – when the social

institutions, e.g. the state, market, politics, laws, morals, religion and ideology, that still play a

key role today were born – conditions of decision-making were once against significantly

altered. Institutions are structures built on rules, roles and relations, and they are operated

based on a system of rewards and penalties. They steer human behaviour by creating

regulated and intelligible frameworks (Knight, J. and Sened, I., 2001: Chapter 1). Their

function is to ensure the integration of hierarchically ordered societies that become

progressively larger in size and are heterogeneous in terms of the division of labour, power

and culture.

Institutions standardise situations, problems and behaviour, and thus everyday life – in

spite of the growing complexity of society – becomes more manageable and also more stable

in the long-run. Decision-making situations become standardised and recurring, though at the

same time a growing proportion of them – the organisation of production and community,

collective action and investments – require long-term planning and the exploration of the

chain of consequences. For the most part, there is enough time for thoughtful deliberation.

The information necessary for a controlled decision-making process and the tried and tested

decision-making methods (RoT) are available, and there are even experts who may be

consulted. The experience accumulated and systematically transmitted over a long line of

generations helps to orient the decision-maker. In light of the aforementioned, the expectation

of rational decision-making is applied to an ever larger spectrum of decisions.

From here on out rational decision means: create options, quantify (with a “cost-

benefit” analysis) the outcomes, collect the values that enter into the thought process,

construct a model of comparison based on the aforementioned, and then “calculate” the best

version based on the information collected. With the spread of bourgeois societies the “cost-

benefit” analysis gains ground in all walks of life. The individual learns to think in

alternatives and to consider benefits forfeited, i.e. opportunity costs. Economic agents realise

the value of time invested and acquire the ability to discount – with due consideration – the

future. (This makes it possible to calculate to what degree it is worthwhile to follow the old

maxim of “Better an egg today than a hen tomorrow!”) Mathematical models of decisions are

gradually developed, and they extend to methods for mathematically capturing uncertainty

and probabilities (Bernstein, P.: 1996).

The space for rational decisions widens until the middle of the 20th century.

Enterprises specialise in the collection of information necessary for making decisions, in the

creation of precise decision-making rules and methods for enabling conscious deliberation.

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The number and influence of these companies grows continuously. The number of

institutions and individuals that deal with the issue of decision-making – in both economic

and public life – increases substantially. At the same time the emergence of market exchanges

as universal phenomena, and the vast spread of social interactions gradually lead to the

emergence of a completely new type of problem. Individuals have to make an increasing

number of rather complex and often not very important decisions very quickly. These days a

person decides anywhere from 2,500-10,000 issues a day! (Douglas, K., 2012: 39). As a

result, a requirement of total rationality would practically render an individual or a group

totally incapable of making a decision. Consequently, the space for rational decisions – which

had previously been expanding for centuries – gradually begins to narrow.

One of the first signs is that the model of bounded rationality makes its appearance in

economic decision-making theory (Simon, H.: 1955). Scholars recognise that the subjects of

economic transactions, decision-makers, acquiesce to rendering “satisficing” choices rather

than making the “best” – read “absolutely” rational – decisions. Rather than weighing all

options, information and considerations, they base their decision on readily accessible and

inexpensively identifiable alternatives, information and models. This is also reflected in the

dual-process theory of decision-making, which distinguishes intuitive (quick and emotional)

and rational (slower and controlled) decision-making (Wen-Jui Kuo et al., 2009: 519). But the

approach offered by the so-called search theory also points in this direction (Caplin, A. et al.,

2011: 2899).

The other important insight that leads to a re-evaluation of the role of perfect

rationality is that on account of the great number of decisions and the inevitable uncertainty of

many situations, subjective satisfaction with the decision is fundamentally determined by the

decision-maker’s assessment of her now non-retractable choice. The importance of this

problem was recognised half a century ago when the phenomenon of cognitive dissonance

was discovered (Festinger, L.: 1957). Cognitive dissonance explains the odd behavioural

pattern wherein following her decision, the decision-maker picks and chooses among the

available information to select those that appear to justify her choice. Experiments show that

even if her choice cannot be regarded as the best, the decision-maker only “accepts”

information that verifies the correctness of her earlier decision. The decisive role of the post-

decision – re-rationalisation – stage is also supported by the fact that in certain cases the

individual – regardless of her actual decision – is capable of making every decision seem

acceptable to her (Hall, L., and Johnsson, P., 2009: 26.).

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In an overwhelming majority of societies, the consumer of the 21st century is

confronted with a vast and confusing selection, the spectrum of considerations is boundless,

the totality of information that is needed for a decision is impossible to collect. As a result of

the aforementioned a comparison is unattainable and, consequently, it is (nigh) impossible to

make a rational decision. The decision might be accelerated and become partially instinctual

if the choices are based on models established during earlier stages of evolution –

“animalistic” instincts, mental modules, symbols or heuristic rules (of thumb). Research has

shown that decision-makers consciously tend to avoid decisions that are tiresome and require

attention and analysis (McGuire, J. et al., 2010: 7922). It was further demonstrated that a

reduction in the number of options that may be selected does not necessarily make the

decision-maker more frustrated, and in fact may have the effect of raising levels of

satisfaction. The decline of rational analysis is also supported by the fact that experiments

have shown – precisely in the case of complex decisions with substantial impact on costs –

that decision-makers tend to resort to a peculiar rule of deliberation-without-attention

(Dijksterhuis, A. et al., 2006: 1005).

The burden of rational deliberation – and also its scope – may be further reduced by

moulding the choices available to the decision-maker – often covertly – and hence nudging

her in certain directions without making her aware of this influence. This method is described

by the term choice architecture (Thaler, R. and Sunstein, C., 2008: 5). Essentially, this

concept revolves around framing a proper decision-making situation – generally in a subtle

and imperceptible manner, but occasionally in a shrill and overt way – that pressures the

decision-makers in a previously designated direction. The authors referenced above call this

“solution” nudge, that is exerting an influence with subliminal messages and subtle signs.

Research discovered a characteristic and striking example of this when evaluating a campaign

that sought to stimulate organ donations (Johnson, E. et al., 2003).

How does the decision-making system work?

This article has reviewed the multi-level hierarchical decision making “programme” of

humans, which was created in the course of evolution. Before making a decision, the

individual selects – based on her impressions of the given situation – at what evolutionary

level she will “deal” with the given problem. Depending on the risks involved and the degree

of confusion in the situation at hand, the importance of the decision and its pressing nature,

the unique nature of the choice to be made and the degree of personal involvement, she will

proceed “backwards” on the evolutionary “ladder”, and employ decision-making models from

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increasingly earlier stages of evolution. Even though we would like to arrive at a simple

decision-making model – for example a two-level system that employs a quick and emotional

method on the one hand, and a slow and rational model on the other –, in reality the multi-

level system can never be turned off.

An understanding of the multilevel system is further encumbered by the fact that a

problem that emerges may simultaneously activate the operation of several decision-making

models, and the decision-making models of earlier periods will radiate out to the higher level

and thereby contribute to the decision. Thus in our developed world the emotional level and

the level of mental modules are almost always “turned on”, and in a decision-making situation

the modern human, too, will automatically seek the assistance of guiding symbols. And

ultimately any and all of these may contribute to a decision; the final choice will be

influenced by their mutual interaction – or their competition with one another, if you prefer

(Sanfey, A. et. al., 2003). One consequence of the aforementioned is that a decision-maker

who is generally considered rational is not necessarily governed by the rules of rational

decision-making.

On account of her biological and social “programming”, a human being may be either

coolly calculating, led by emotions, governed by symbols, altruistic, likely to succumb to

momentary impressions or plain “irrational”. Which of her “selves” will predominate in any

given decision will depend on her subjective impressions of the situation at hand. She will

“relate” differently to the decision if she perceives the situation as pressing, full of threats or

unusual, as compared to a situation seen as the polar opposite of the aforementioned.

Similarly, her attitude will be different if she senses that the decision will have a fundamental

impact on her life or that it leads to a long-term commitment, and vice versa. She will choose

decision-making models based on her impressions, and if her opinion of a given decision-

making situation is altered then she will adjust her preferred model of decision-making

accordingly. This insight has been applied in the economy and politics for a while now:

decision-makers are lured into a previously designed decision trap and thereby their decisions

are substantially preordained regardless of their will.

15

Literature:

Akerlof, G. A. Schiller, R. (2009): Animal Spirits – How Human Psychology Drives the Economy,

and Why It Matters for Global Capitalism. Princeton University Press.

Barrett, J. (2004): Why Would Anyone Believe in God? AltaMira Press. Walton Creek.

Behrens, T. Hunt, L. Rushworth, M. (2009): The Computation of Social Behavior. Science. Vol. 324.

May 29. 1160-1164.

Bendor, J. Swistak, P. (2001): The Evolution of Norms. American Journal of Sociology. Vol. 106. No.

6. 1493-1545.

Bernhard, H. Fishbacher, U. Fehr, E. (2006): Parochial Altruism in Humans. Nature. Vol. 442. 912-

914.

Bernstein, P. L. (1996): Against the Gods. The Remarkable Story of Risk. John Wiley and Sons. New

York

Brown, D. E. (1991) Human Universals. McGraw-Hill. New York.

Camerer, C. Fehr, E. (2006): When Does „Economic Man” Dominate Social Behavior? Science. Vol:

311. 47-52.

Caplin, A. Dean, M. Martin, D. (2011): Search and Satisficing. American Economic Review. Vol:

101. 2899-2922.

Chen, K.M., Santos, L. (2009): The Evolution of Rational and Irrational Economic Behavior:

Evidence and Insight from Non-Human Primate Species. in: Neuroeconomics: Decision Making and

the Brain. Glimcher, P. Camerer, C., Fehr, E., Poldrack, R. Academic Press, Elsevier, 2009.

Cialdini, B. Robert. (2001): Influence: Science and Practice. Edition. Allyn & Bacon.

Cosmides, L, Tooby J. (1997): Evolutionary Psychology: A Primer.

http://www.psych.ucsb.edu/research/cep/primer.html

Dijksterhuis, A. et al. (2006): On Making the Right Choice: The Deliberation-Without-Attention

Effect. Science. Vol: 311. 1005-1007.

Donald, M. (1991): Origins of the Modern Mind (Three stages in the Evolution of Culture and

Cognition). Harvard University Press.

Douglas, K. (2011): Making your mind up. New Scientist. November 12. 39-41.

Efferson, Ch. Lalive, R. Fehr, E. (2008): The Coevolution of Cultural Groups and Ingroup Favoritism.

Science. Vol. 321. Sept. 26. 1844-1849.

Elinor O. (2010): Beyond Market and State: Polycentric Governance of Complex Economic Systems.

American Economic Review. Vol. 100. (2010 June) 641-672.

Evans, J. (2003): In two minds: dual process account of reasoning. TRENDS in Cognitive Sciences

Vol. 7. No. 10. 454-459.

Festinger, L. (1957): A Theory of Cognitive Dissonance. Stanford University Press.

16

Flack, J. Waal, F. de (2007): Context modulates signal meaning in primate communication. PNAS.

Vol. 104. No. 5 1581-1586.

Fliessbach, K. Weber, B. Trautner, P. Dohmen, T. Sunde, U. Elger, E. Falk, A. (2007): Social

comparison Affects Reward-Related Brain Activity in the Human Ventrum Striatum. Science. Nov.

23. Vol: 318 1305-1308

Friedman, M. (1953): Essays in Positive Economics. University of Chicago Press. Chicago

Frey, B.S. Savage, D. A. Torgler, B. (2010): Interaction of natural survival instincts and internalized

social norms exploring Titanic and Lusitania disaster. PNAS. Vol: 107. No 11. 4862-4865.

Gigerenzer, G. (2004): Gut Feelings. (The Intelligence of the Unconscious). Viking.

Gino, F. Desai, S. (2011): Memory Lane and Morality: How Childhood Memory Promote Prosocial

Behavior. Harvard Business School. Working Paper. 11-079.

Gintis, H. Bowles, S. Boyd, R. Fehr, E. (2005): Moral Sentiments and Material Interests: Origins,

Evidence, and Consequences. In: Moral Sentiments and Material Interests. Edited by: Gintis H.

Bowles S. Boyd, R. Fehr, E. The MIT Press.

Gomes, C. Boesch, C. (2011): Reciprocity and trade sin the wild West African chimpanzees. Behav.

Ecol. Sociobiology. Vol. 65. 2183-2196.

Hall, L. Johansson, P. (2009): The reason I chose that one is.. New Scientist. Apr. 18. 26-27.

Harbaugh, W. Mayr, U. Burghart, D. (2007): Neural Responses to Taxation and Voluntary Giving

Reveal Motives for Charitable Donations. Science. Vol: 316. 1622-1625.

Hare, B. (2011): From Hominid to Hominid Mind: What changed and Why? Annu. Rev.

Anthropology. Vol: 40. 293-309.

Hassin, R. Ferguson, M. Shidlovski, D. Gross, M. (2007): Subliminal exposure to national flags

affects political thought and bevavior. PNAS..Vol. 104. No. 50 19757-19761

Henshilwood, C. Dubreuil, B. (2011): The Still Bay and the Howiesons Port, 77-59 ka (Symbolic

Material Culture and the Evolution of the Mind during the African Middle Stone Age) Current

Anthropology Vol. 52 Nu. 3. Jun 361- 381.

Jablonka, E. Lamb, M. (2005): Evolution in Four Dimensions. (Genetic, Epigenetic, Behavioral, and

Symbolic Variation int he History of Life. A Bradford Book The MIT Press.

Jensen, K. Call, J. Tomesello, M. (2007): Chimpanzees Are Rational Maximizers in an Ultimatum

Game. Science. Vol. 318. 107-109.

Jones , D. (2009): Human nature: the remix. Nature. Darwin 200. News Feature. 780-783.

Johnson, E. Goldstein, D. (2003): Science. Vol: 302. Nov. 21. 1338-1339.

Kahneman, D. Tversky, A. (1979): Prospect Theory: An Analysis of Decisions under Risk.

Econometrica. Vol: 47. No. 2. 313-327.

Kahneman, D. (2003): A Psychological Perspective on Economics. Vol. 93. No. 2. 163-167.

Kahneman, D. (2011): Thinking, Fast and Slow. Allen Lane/Farrar, Straus and Giroux.

17

Kirby, K. et al. (2002): Correlates of delayed-discount rate: Evidence from Tsimane amerindians of

the Bolivian rain forest. Journal of Economic Psychology. Vol. 23. 291-316.

Knight, Ch, Power, C. Watts. I. (1995): The Human Symbolic revolution: A Darwinian Account.

Cambridge Archeological Journal. 5 (1), 75-114.

Knight, J. Sened I. (2001) (ed.): Explaining Social Institutions. The University of Michigan Press.

AnnArbor.

Kolling, N. Behrenns, T. Mars, R. Rushworth, M. (2012): Neural Mechanism of Foraging. Science.

Vol. 336. 95-97.

Koriat, A. (2012): When Are Two Heads Better then One and Why? Science. Vol. 336. 360-362.

Kray, L. J. George, L. G. Liljenquist, K. A. Galinsky, A.D. Tetlock, Ph. E. Roese, N. J. (2010): From What Might Have Been to What Must Have Been: Counterfactual Thinking Creates Meaning. Journal of Personality and Social Psychology Vol. 98, No. 1, 106–118Ladeveze, A. Muizon, Ch. Beck, R. Germain, D. Cespedes-Paz, R. Earliest evidence of mammalian

social behaviour in the basal Tertiary of Bolivia. Nature. Vol. 474. Jun. 2. 83-86.

LeDoux, J. (2000): Emotion Circuits in the Brain. Annual Review Neuroscience. Vol. 23. 155-184.

MacLean, P. D. (1990): The Triune Brain in Evolution: Role in Paleocerberal Function. Plenum Press.

New York.

Marosan, Gy. (2011): The Evolutionary Interpretation of Man: the Matryoshka Model. JSPES. Vol:

36. No: 4. Winter. 472-493

Martino De, B, et al. (2010): Amygdala damage eliminate monetary loss aversion. PNAS. Vol. 107.

3788-3792.

Massey, D. (2002): A Brief History of Human Society: The Origin and Role of Emotion in Social

Life. American Sociological Review. Vol. 67. Febr. 1-29.

McGuire, J.T. Botvinick, M.M. (2010): Prefrontal cortex, cognitive control and the registration of

decision cost. PNAS. Vol. 107. No.17. 7922-7926.

Mellars, P. French, J. (2011): Tenfold Population Increase in Western Europe at the Neanderthal-to-

Modern Human Transition. Science. Vol. 333. 623-627.

Navarrete, A. Schaik, van C. Isler, K. (2011): Energetics and the evolution of human brain. size.

Natzre. Vol. 480. Dec. 1. 91- 94.

Nesse, R. Ellsworth, P. (2009):Evolution, Emotions, and Emotional Disorders. American

Psychologist. Vol. 64. No.2. 129-139.

Ostrom, E. (2010): Beyond Markets and States: Policentric Governance of Complex Economic

Systems. American Economic Review. Vol: 100. 641-672.

Panksepp, Jaak. (2004): Affective Neuroscience: The Foundations of Human and Animal emotions.

Oxford University Press. USA.

18

Pessigliona, M. Schmidt, L. Draganski, B. Kalisch, H. Dolan, R. Firth, C. (2007): How the Brain

Translates Money into Force: A Neuroimaging Study of Subliminal Motivation Science Vol. 316 no.

5826 pp. 904-906

Petersen, M. (2012): Social Welfare as Small-Scale Help: Evolutionary Psychology and the

Deservingness Heuristic. Americal Journal of Political Science Vol. 56. No.1. 1-16.

Potts, R. (2011): Big Brain explained. Nature. Vol: 480. Dec. 1. 43-44.

Powel, A. Shennan, S. Thomas, M. (2009): Late Pleistocene Demography and Appearance of Modern

Human Behavior. Science. Vol. 324. 1298- 1301.

Rabin, M. (2000): Risk Aversion and Expected Utility Theory. Econometrica. Vol: 68. No: 5 1281-

1292.

Rossano, M. (2010): Making Friends, Making Tools, and Making Symbols. Current Anthropology.

Vol. 51. Supplement. 1. June. S89-S98.

Rowe, T. Macrini, T. Luo, Z. (2011): Fossil Evidence on the Origins of the Mammalian Brain.

Science. Vol. 332. 955-958.

Sanfey, A. Rilling, J. Aronson, J. Nystom, L. Cohen, J. (2003): ?? Science Vol. 300. 1755-1758.

Scherer, K. (2005): What are emotions? And how can they be measured? Social Science Information.

Vol. 44. No. 4. 695-729.

Shiller, R. J. és Akeloff, G. A. (2008): Animal Spirit: How Human Psychology Drives the Economy,

and Why It Matters for Global Capitalism”. Princeton University Press Princeton.

Simon, H. (1955): A Behavioral Model of Rational Choice. Quarterly Journal of Economics. 69. No.

1. 99-118.

Sutter, M. (2009): Individual Behavior and Group Membership: Comment. American Economic

Review. Vol. 99. No.5 2247-2257.

Thaler, R. H. Sunstein, C. R. (2008): Nudge. Improving Decisions About Health, Wealth, and

Happiness. Yale University Press.

Tulring, E. Schacter, D. L. (1990): Priming and the human memory system. Science. Vol: 247. 301-

306.

Vohs, K. Mead, N. Goode, M. (2006): The Psychological Consequences of Money. Science. Vol. 314.

Nov. 17. 1154-1156.

Zaki, J. Mitchell, J. (2011): Equitable decision making is associated with neural marker of intrinsic

values. PNAS. Vol. 108. No. 49. 19.761-19.766

Ziegler, R. (2008): What makes the Kula go around? Social Networks. Vol: 30. 107-126.

Waal, F. de. (2006): Primate and Philosophers. (How Morality Evolved). Princeton University Press.

Wen-Jui K. W. Sjösröm, T. Chen, Y. Wang, Y. Huang, Ch. (2009) Intuition and Deliberation: Two

Systems for Strategizing in the Brain. Science. Vol. 324. Apr. 24. 519-522.

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