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Why We Don’t 

Understand Complex 

Systems  

Author: Jeffrey G. Long (jefflong@aol.com) 

Date: May 21, 2000 

Forum: Poster session presented at the International Conference on Complex 

Systems, sponsored by the New England Complex Systems Institute. 

 

Contents 

Page 1: Abstract 

Pages 2‐22: Slides (but no text) for presentation 

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Abstract Title: Why We Don’t Understand Complex Systems Author: Jeffrey G. Long Physics has sought to understand physical systems that once were considered baffling in their behavior, and by the discovery of new abstractions – initially the creation of a new descriptive language, the infinitesimal calculus – was able to help provide theoretical explanations that have led to one revolution after another in the past 300 years. But as is usually the case, prior to the development of any real understanding of (say) thermodynamics, humanity was able to successfully harness the power of steam to launch the industrial revolution. This is characteristic of the successes we have had with many complex systems: humanity’s successes in dealing with these systems, great as they have been in some cases, have occurred more by trial and error exploration than by the application of any fundamental organizing principles. Extending this classic model of progress to other kinds of complex system, this paper presents two fundamental theses. The first principal thesis is that complexity is in the eye of the beholder, and is a euphemism for perplexity. Seeming complexity can be dissolved with appropriate new ways of looking at complex phenomena, leading to the corollary that in order to understand complex systems, we will need to develop wholly new abstractions. Humanity has typically come across these by accident rather than systematically, so the hunt for new abstractions could be greatly facilitated by the systematic study of the history and evolution of a variety of types of notational systems (not just mathematics). I call this proposed subject “notational engineering”. I believe we need new abstractions in many areas, including (e.g.) new ways of representing value besides money, and new ways of representing large systems of complex rules besides the current tools of mathematics, logic and natural language. The second principal thesis of this talk is that seemingly-complex systems differ from simpler, more understandable systems only in having more rules governing their behavior. In traditional science, scientists look at the complex behavior of a system and try to develop a few simple rules that account for that behavior. With seemingly-complex systems, there will be many rules that must be defined. I call the complex behavior of a system its “surface structure”, and the thousands of rules that govern it “middle structure”. These rules in turn can be grouped by their form, and these the “deep structure” of the system. Families of systems share the same deep structure. This process-oriented metaphysics permits a very practical, highly abstract and formal way of organizing and representing rules. I call this approach “Ultra-Structure”, and have applied it to a number of types of systems. It permits the creation of “spreadsheets” (called “Competency Rule Engines) for families of systems, where one needs only to enter the rules of a system as data into the spreadsheet to make the system accurately model the behavior of a complex system. I think this may be a serious candidate for a new general approach to representing any kind of complex, or seemingly-complex, system.

Why We Don’t Understand C l S tComplex Systems

Jeffrey G. LongICCS Conference, May 2000

jefflong@aol.com

Complexity is a Euphemism for Perplexity

We may have competence in using complex systems but we still don’t “understand” complex systems

This is not because of the nature of the systems, but rather because our notational systems – ourrather because our notational systems our abstractions -- are inadequate

These problems cannot be solved by working harder or using faster computers

Complexity is not a property of systems; rather, perplexity is a property of the observer

Many if not most problems today are fundamentally representational in characterrepresentational in character

We don’t go sailing in automobiles; we shouldn’t (e.g.) g g ; ( g )use mathematics for complex conditional rules

Using the wrong, or too-limited, a notational system is inescapably self-defeating

We Have Never Really Studied Notational Systems

There are four kinds of sign system:Formal: syntax only, e.g. formal logic and language, pure mathematicsmathematics Informal: semantics only, e.g. art, advertising, politics, religious symbolsNotational: have both syntax and semantics e g naturalNotational: have both syntax and semantics, e.g. natural language, musical notation, money, cartography Subsymbolic: neither syntax nor semantics, e.g. natural systemssystems

Of these, notational systems are probably the least-explored

Each primary notational system maps a different “abstraction space”

Abstraction spaces are incommensurablePerceiving these is a unique human abilityPerceiving these is a unique human ability

Abstraction spaces are discoveries, not inventionsAbstraction spaces are real

Acquiring literacy in a notation is learning how to seeAcquiring literacy in a notation is learning how to see a new abstraction space

All higher forms of thinking are dependent upon the use of one or more notational systems

The notational systems one habitually uses influences the manner in which one perceives his environment:the manner in which one perceives his environment: the picture of the universe shifts from notation to notation

Notational systems have been central to the evolution of civilizationevolution of civilization

Every notational system has limitations: a y y“complexity barrier”

The problems we face now as a civilization are, in many cases, notational

We need a more systematic way to develop and settle abstraction spaces

So Far We Have Settled MaybeSo Far We Have Settled Maybe12 Major Abstraction Spaces

Current Analysis Methods Work Only Under Certain ConditionsOnly Under Certain Conditions

Even Mathematics Has Limitations

Offers conciseness of description, and rigorB t ti t b h i t h iBut equations represent behavior, not mechanismShorthand obscures mechanism (e.g. multiplication, exponentiation to show repeated addition)exponentiation to show repeated addition)Deals only with entities capable of being the subject of theorems, i.e. entities that behave additively,

hwithout emergent properties

Rules are a Broader Way of Describing Things

Multi-notational, including (e.g.) qualities as well as quantitiesqExplicitly contingent Describe both behavior and mechanism Thousands or millions can be assembled and acted upon by computerShed light on ontology or basic nature of systemsShed light on ontology or basic nature of systems

Ultra-Structure Theory Was Created to Represent Systems in Terms of

Complex and Changing Rules

New theory of systems design, developed 1985Focuses on optimal computer representation of complex, conditional and changing rulescomplex, conditional and changing rulesBased on a new abstraction called ruleforms

The breakthrough was to find the unchanging features of changing systems

The Theory Offers a Different Way to Look at Complex Systems and Processesoo at Co p e Syste s a d ocesses

observablebehaviors surface structure

generatesrules

f f l

middle structure

constrainsform of rules deep structure

fAny Type of Statement Can Be Reformulated into an If-Then Rule Format

Natural language statements Musical scoresLogical argumentsB iBusiness processes Architectural drawingsMathematical statementsMathematical statements

Rules Can be Represented in Place-Value (Tabular) Form

Place value assigns meaning based on content and locationlocation

In Hindu-Arabic numerals, this is column positionIn ruleforms, this is column position

Thousands of rules can fit in same ruleformThere are multiple basic ruleforms, not just one

B t th t t l b i till ll ( 100?)But the total number is still small (<100?)

This Creates New Levels for AnalysisThis Creates New Levels for Analysis and Representation

Standard Terminology (if any) Ultra-Structure Instance Ultra-Structure Level U-S ImplementationStandard Terminology (if any) Ultra-Structure Instance Name

Ultra-Structure Level Name

U-S Implementation

behavior, physical entities and relationships, processes

particular(s) surface structure system behavior

rules, laws, constraints, guidelines, rules of thumb

rule(s) middle structure data and some software (animation procedures)

(no standard or common term)

ruleform(s) deep structure tables

(no standard or common universal(s) sub-structure attributes, fieldsterm)

tokens, signs or symbols token(s) notational structure character set

The Ruleform Hypothesis

C l t t t t d b tComplex system structures are created by not-necessarily complex processes; and these processes are created by the animation of

ti l O ti l b doperating rules. Operating rules can be grouped into a small number of classes whose form is prescribed by "ruleforms". While the operating

l f t h ti th l frules of a system change over time, the ruleforms remain constant. A well-designed collection of ruleforms can anticipate all logically possible

ti l th t i ht l t th toperating rules that might apply to the system, and constitutes the deep structure of the system.

The CoRE HypothesisW t “C t R l E i ”We can create “Competency Rule Engines”, or CoREs, consisting of <50 ruleforms, that are sufficient to represent all rules found among systems h i b d f il bl llsharing broad family resemblances, e.g. all

corporations. Their definitive deep structure will be permanent, unchanging, and robust for all members f th f il h diff i if tof the family, whose differences in manifest

structures and behaviors will be represented entirely as differences in operating rules. The animation

d f h i ill b l ti l i lprocedures for each engine will be relatively simple compared to current applications, requiring less than 100,000 lines of code in a third generation language.

The Deep Structure of a SystemThe Deep Structure of a System Specifies its Ontology

What is common among all systems of type X?What is the fundamental nature of type X systems?What are the primary processes and entities involvedWhat are the primary processes and entities involved in type X systems?What makes systems of type X different from systems of type Y?

If we can answer these questions about a system, then we have achieved understanding

Conclusion

To truly understand complex systems, we must get beyond appearancesg y pp(surface structure) and rules (middle structure) to the ruleforms (deep ) ( pstructure).

R fReferencesLong, J., and Denning, D., “Ultra-Structure: A design theory for complex systems and processes ” In Communications of thecomplex systems and processes. In Communications of the ACM (January 1995)Long, J., “Representing emergence with rules: The limits of addition ” In Lasker G E and Farre G L (eds) Advances inaddition. In Lasker, G. E. and Farre, G. L. (eds), Advances in Synergetics, Volume I: Systems Research on Emergence. (1996)Long, J., “A new notation for representing business and other rules ” In Long J (guest editor) Semiotica Special Issue:rules. In Long, J. (guest editor), Semiotica Special Issue: Notational Engineering, Volume 125-1/3 (1999)Long, J., “How could the notation be the limitation?” In Long, J. (guest editor) Semiotica Special Issue: Notational Engineering(guest editor), Semiotica Special Issue: Notational Engineering, Volume 125-1/3 (1999)