Why we dont understand complex systems


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May 21-25, 2000: "Why We Don't Understand Complex Systems". Poster session, presented at the International Conference on Complex Systems, sponsored by the New England Complex Systems Institute.

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Why we dont understand complex systems

  1. 1. Cover Page   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  License This work is licensed under the Creative Commons Attribution‐NonCommercial 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by‐nc/3.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA.  Uploaded June 27, 2011 
  2. 2. Abstract Title: Why We Don’t Understand Complex SystemsAuthor: Jeffrey G. LongPhysics has sought to understand physical systems that once were considered baffling in theirbehavior, and by the discovery of new abstractions – initially the creation of a new descriptivelanguage, the infinitesimal calculus – was able to help provide theoretical explanations that haveled to one revolution after another in the past 300 years. But as is usually the case, prior to thedevelopment of any real understanding of (say) thermodynamics, humanity was able tosuccessfully harness the power of steam to launch the industrial revolution. This is characteristicof the successes we have had with many complex systems: humanity’s successes in dealing withthese systems, great as they have been in some cases, have occurred more by trial and errorexploration than by the application of any fundamental organizing principles. Extending thisclassic model of progress to other kinds of complex system, this paper presents two fundamentaltheses.The first principal thesis is that complexity is in the eye of the beholder, and is a euphemism forperplexity. Seeming complexity can be dissolved with appropriate new ways of looking atcomplex phenomena, leading to the corollary that in order to understand complex systems, wewill need to develop wholly new abstractions. Humanity has typically come across these byaccident rather than systematically, so the hunt for new abstractions could be greatly facilitatedby 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 neednew 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 ofmathematics, 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 traditionalscience, scientists look at the complex behavior of a system and try to develop a few simple rulesthat account for that behavior. With seemingly-complex systems, there will be many rules thatmust be defined. I call the complex behavior of a system its “surface structure”, and thethousands of rules that govern it “middle structure”. These rules in turn can be grouped by theirform, and these the “deep structure” of the system. Families of systems share the same deepstructure. This process-oriented metaphysics permits a very practical, highly abstract and formalway of organizing and representing rules. I call this approach “Ultra-Structure”, and have appliedit to a number of types of systems. It permits the creation of “spreadsheets” (called “CompetencyRule Engines) for families of systems, where one needs only to enter the rules of a system asdata into the spreadsheet to make the system accurately model the behavior of a complexsystem. I think this may be a serious candidate for a new general approach to representing anykind of complex, or seemingly-complex, system.
  3. 3. Why We Don’t Understand Complex S t C l Systems Jeffrey G. Long ICCS Conference, May 2000 jefflong@aol.com
  4. 4. Complexity is a Euphemism for PerplexityWe may have competence in using complex systemsbut we still don’t “understand” complex systemsThis is not because of the nature of the systems, butrather because our notational systems – ourabstractions -- are inadequateThese problems cannot be solved by working harderor using faster computers
  5. 5. Complexity is not a property of systems; rather, perplexityis a property of the observerMany if not most problems today are fundamentallyrepresentational in characterWe don’t go sailing in automobiles; we shouldn’t (e.g.) g g ; ( g)use mathematics for complex conditional rulesUsing the wrong, or too-limited, a notational system isinescapably self-defeating
  6. 6. We Have Never Really Studied Notational SystemsThere are four kinds of sign system: Formal: syntax only, e.g. formal logic and language, pure mathematics Informal: semantics only, e.g. art, advertising, politics, religious symbols Notational: have both syntax and semantics, e.g. natural semantics e g language, musical notation, money, cartography Subsymbolic: neither syntax nor semantics, e.g. natural systemsOf these, notational systems are probably the least-explored
  7. 7. Each primary notational system maps a different“abstraction space” Abstraction spaces are incommensurable Perceiving these is a unique human abilityAbstraction spaces are discoveries, not inventions Abstraction spaces are realAcquiring literacy in a notation is learning how to seea new abstraction space
  8. 8. All higher forms of thinking are dependent upon theuse of one or more notational systemsThe notational systems one habitually uses influencesthe manner in which one perceives his environment:the picture of the universe shifts from notation tonotationNotational systems have been central to theevolution of civilization
  9. 9. Every notational system has limitations: a y y“complexity barrier”The problems we face now as a civilization are, inmany cases, notationalWe need a more systematic way to develop andsettle abstraction spaces
  10. 10. So Far We Have Settled Maybe 12 Major Abstraction Spaces
  11. 11. Current Analysis Methods WorkOnly Under Certain Conditions
  12. 12. Even Mathematics Has LimitationsOffers conciseness of description, and rigorButB t equations represent b h i ti t behavior, not mechanism t h iShorthand obscures mechanism (e.g. multiplication,exponentiation to show repeated addition)Deals only with entities capable of being the subjectof theorems, i.e. entities that behave additively,without emergent properties h
  13. 13. Rules are a Broader Way of Describing ThingsMulti-notational, including (e.g.) qualities as well asqquantitiesExplicitly contingentDescribe both behavior and mechanismThousands or millions can be assembled and actedupon by computerShed light on ontology or basic nature of systems
  14. 14. Ultra-Structure Theory Was Created to Represent Systems in Terms of Complex and Changing RulesNew theory of systems design, developed 1985Focuses on optimal computer representation ofcomplex, conditional and changing rulesBased on a new abstraction called ruleformsThe breakthrough was to find the unchangingfeatures of changing systems
  15. 15. The Theory Offers a Different Way toLook at Complex Syste s and Processes oo Co p e Systems a d ocesses observable behaviors surface structure generates rules middle structure constrainsform of rulesf f l deep structure
  16. 16. Any Type of Statement Can Be fReformulated into an If-Then Rule Format Natural language statements Musical scores Logical arguments Business processes B i Architectural drawings Mathematical statements
  17. 17. Rules Can be Represented in Place-Value (Tabular) FormPlace value assigns meaning based on content andlocation In Hindu-Arabic numerals, this is column position In ruleforms, this is column positionThousands of rules can fit in same ruleformThere are multiple basic ruleforms, not just one But the t t l B t th total number i still small (<100?) b is till ll ( 100?)
  18. 18. This Creates New Levels for Analysis and Representation Standard Terminology (if any) Ultra-Structure Instance Ultra-Structure Level U-S Implementation Name Name behavior, physical entities particular(s) surface structure system behavior and relationships, processes rules, laws, constraints, rule(s) middle structure data and some guidelines, rules of thumb software (animation procedures) (no standard or common ruleform(s) deep structure tables term) (no standard or common universal(s) sub-structure attributes, fields term) tokens, signs or symbols token(s) notational structure character set
  19. 19. The Ruleform HypothesisComplex system structures are created b not-C l t t t t d by tnecessarily complex processes; and theseprocesses are created by the animation ofoperating rules. Operating rules can be grouped ti l O ti l b dinto a small number of classes whose form isprescribed by "ruleforms". While the operatingrules of a system change over time, th ruleforms l f t h ti the l fremain constant. A well-designed collection ofruleforms can anticipate all logically possibleoperating rules that might apply to the system, ti l th t i ht l t th tand constitutes the deep structure of the system.
  20. 20. The CoRE HypothesisWeW can create “Competency Rule E i t “C t R l Engines”, or ”CoREs, consisting of <50 ruleforms, that aresufficient to represent all rules found among systemssharing b d f il resemblances, e.g. all h i broad family bl llcorporations. Their definitive deep structure will bepermanent, unchanging, and robust for all membersof the family, whose diff f th f il h differences in manifest i if tstructures and behaviors will be represented entirelyas differences in operating rules. The animationprocedures f each engine will b relatively simple d for h i ill be l ti l i lcompared to current applications, requiring less than100,000 lines of code in a third generation language.
  21. 21. The Deep Structure of a System Specifies its OntologyWhat is common among all systems of type X?What is the fundamental nature of type X systems?What are the primary processes and entities involvedin type X systems?What makes systems of type X different fromsystems of type Y?If we can answer these questions about a system,then we have achieved understanding
  22. 22. ConclusionTo truly understand complex systems,we must get beyond appearances g y pp(surface structure) and rules (middlestructure) to the ruleforms (deep ) ( pstructure).
  23. 23. R f ReferencesLong, J., and Denning, D., “Ultra-Structure: A design theory forcomplex systems and processes.” In Communications of the processesACM (January 1995)Long, J., “Representing emergence with rules: The limits ofaddition.addition ” In Lasker, G E. and Farre G L (eds) Advances in Lasker G. E Farre, G. L. (eds),Synergetics, Volume I: Systems Research on Emergence. (1996)Long, J., “A new notation for representing business and otherrules ” In Long, J. (guest editor) Semiotica Special Issue:rules. Long J editor),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 editor), Engineering,Volume 125-1/3 (1999)