Understanding complex systems


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March 28, 2002: "Understanding Complex Systems: Notational Engineering and Ultra-Structure". Talk given at the University of North Carolina, Chapel Hill.

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Understanding complex systems

  1. 1. Cover Page   Understanding  Complex Systems  Author: Jeffrey G. Long (jefflong@aol.com) Date: March 28, 2003 Forum: Talk presented at the University of North Carolina, Chapel Hill.   Contents Pages 1‐23: 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. Understanding ComplexSystems: NotationalEngineering and Ultra-Structure Jeffrey G. Long March 28, 2002 jefflong@aol.com j ffl @ l
  3. 3. P oposed o tlineProposed outline 1: Background on the general problem: representation and notational systems 2: Overview of Ultra Structure: one Ultra-Structure: new approach to complex systems 3: Simple Example of Biology PrototypeMarch 28, 2002 Copyright 2002 Jeff Long 2
  4. 4. 1: h1 The Problem blMarch 28, 2002 Copyright 2002 Jeff Long 3
  5. 5. Many if not most of our current problems arise y o os o ou u p o s sfrom the way we represent them We may have pragmatic competence in using certain y p g p g kinds of complex systems but we still don’t really understand them theoretically  economics, finance, markets , ,  medicine, physiology, biology, ecology This is not because of the nature of the systems but systems, rather because our analytical tools – our notational systems and the abstractions they reify -- are inadequateMarch 28, 2002 Copyright 2002 Jeff Long 4
  6. 6. Complexity is not a property of systems; rather, o p y s o p op y o sys s; ,perplexity is a property of the observer Systems appear complex under certain conditions; when better understood they may still be “complicated” but they are tractable to explanation Using the wrong, or too-limited, an analytical toolset creates these “complexity barriers”; they cannot be breached without a new notational system b h d ith t t ti l t These problems cannot be solved by working harder, using faster computers, or moving to OO techniques; they do not arise due to lack of effort or lack of factual informationMarch 28, 2002 Copyright 2002 Jeff Long 5
  7. 7. So far we have settled maybe y12 major abstraction spacesMarch 28, 2002 Copyright 2002 Jeff Long 6
  8. 8. Notational systems are the primary tool thathuman cognition has d u og o s developed to embody op d o odyabstractions Each primary notational system maps a different “abstraction space”  Abstraction spaces are incommensurable  Perceiving these is a unique human ability Acquiring literacy in a notation is learning how to see a new abstraction space Having H i acquired such literacy, we see the world i d h lit th ld differently and can think about it differentlyMarch 28, 2002 Copyright 2002 Jeff Long 7
  9. 9. This is essentially a broadening of Whorf’s notionof linguistic relativity, Chomsky s notion of an Chomsky’sinnate linguistic capability, and Tolstoy’s theoryof challenge and response by civilizations All higher forms of thinking require the use of one or more notational systems; the facility to perceive these (but not the content) is biologically built in ( ) g y The notational systems we habitually use influence the manner in which we perceive our environment: our picture of the universe shifts as we acquire literacy in new notational systems Notational systems have been central to the evolution of the modern mind and modern civilization March 28, 2002 Copyright 2002 Jeff Long 8
  10. 10. Conclusion to Section 1 Every analytical toolset (which is always based on a y y ( y notational system) has limitations: this appears to us as a “complexity barrier” The problems we face now in biology (and as a civilization!) are, in many cases, notational We need a more systematic way to develop and settle abstraction spaces: notational engineering March 28, 2002 Copyright 2002 Jeff Long 9
  11. 11. 2: One New ApproachMarch 28, 2002 Copyright 2002 Jeff Long 10
  12. 12. Current systems analysis methods work well onlyunder certain conditionsMarch 28, 2002 Copyright 2002 Jeff Long 11
  13. 13. The theory is based upon a different way ofdescribing complex systems and processes observable behaviors surface structure generates rules middle structure constrains form of rules f f l deep structure March 28, 2002 Copyright 2002 Jeff Long 12
  14. 14. Rules are a very powerful way of describingthings Multi-notational: can include all other notational systems Explicitly E li itl contingent ti t Describe both behavior and mechanism Hundreds of thousands can be represented and p executed by a small computer!March 28, 2002 Copyright 2002 Jeff Long 13
  15. 15. Any type of assertion can (evidently) bereformulated into one or more If-Then rules Natural language statements Musical scores Logical arguments Business processes Architectural drawings Mathematical statements But often one “molecular” rule becomes several molecular “atomic” rulesMarch 28, 2002 Copyright 2002 Jeff Long 14
  16. 16. Rules can be represented as data (records)i a relational d t bin l ti l database Ultra-Structure Theory is a general theory of systems representation, developed/tested starting 1985 Focuses on optimal computer representation of F ti l t t ti f complex, conditional and changing rules Based on a new abstraction called ruleforms The breakthrough was to find the unchanging features of changing systemsMarch 28, 2002 Copyright 2002 Jeff Long 15
  17. 17. Rules in Ultra-Structure are Literal Implementations of pIf-Then Statements If X then consider A h id and B d Existential Ruleform TAA (Atomic Weight) If X and Y then consider A and B Compound Translation TAA (Stop Encoding) RuleformMarch 28, 2002 Copyright 2002 Jeff Long 16
  18. 18. Structured and Ultra-Structured dataare different Structured data separates algorithms and data, and is good for data processing and information retrieval tasks,e.g. reports, queries, data entry Ultra-Structured data has only “rules”, formatted in a manner that allows a very small inference engine to reason with them using standard deductive logic “Animation” ft “A i ti ” software h littl or no knowledge of has little k l d f the external worldMarch 28, 2002 Copyright 2002 Jeff Long 17
  19. 19. The Ruleform Hypothesis Complex system structures are created by not- necessarily complex processes; and these il l d th processes are created by the animation of operating rules. Operating rules can be grouped into a small number of classes whose form is i ll b f l h f i prescribed by "ruleforms". While the operating rules of a system change over time, the ruleforms remain constant. A well-designed collection of i ll d i d ll i f ruleforms can anticipate all logically possible operating rules that might apply to the system, and constitutes the deep structure of the system. d h d f hMarch 28, 2002 Copyright 2002 Jeff Long 18
  20. 20. The CoRE Hypothesis We can create “Competency Rule Engines”, or CoREs, C RE consisting of <50 ruleforms, th t are i ti f 50 l f that sufficient to represent all rules found among systems sharing broad family resemblances, e.g. all corporations. Their definitive d ti Th i d fi iti deep structure will b t t ill be permanent, unchanging, and robust for all members of the family, whose differences in manifest structures and b h i d behaviors will b represented entirely ill be d i l as differences in operating rules. The animation procedures for each engine will be relatively simple compared to current applications, requiring less than 100,000 lines of code in a third generation language.March 28, 2002 Copyright 2002 Jeff Long 19
  21. 21. The deep structure of a system p yspecifies its ontology or “genotype” 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 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 real understandingMarch 28, 2002 Copyright 2002 Jeff Long 20
  22. 22. Conclusion to Section 2 One example of a new abstraction is ruleforms To ruleforms. truly understand complex systems such as biological systems, we must get beyond appearances (surface structure) and rules (middle structure) to the stable ruleforms (deep structure). This is the goal of Ultra-Structure Theory.March 28, 2002 Copyright 2002 Jeff Long 21
  23. 23. 3: A simple application exampleMarch 28, 2002 Copyright 2002 Jeff Long 22
  24. 24. References Long, J., and Denning, D., “Ultra-Structure: A design theory for complex systems and processes.” In C l d ” Communications of the i i f h ACM (January 1995) Long, J., “Representing emergence with rules: The limits of addition. addition ” In Lasker, G E. and Farre G L (editors) Advances Lasker G. E Farre, G. L. (editors), in Synergetics, Volume I: Systems Research on Emergence. (1996) Long, J., “A new notation for representing business and other g, , p g 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), S i ti S ( t dit ) Semiotica Special Issue: Notational Engineering, i lI N t ti lE i i Volume 125-1/3 (1999)March 28, 2002 Copyright 2002 Jeff Long 23