Understanding complex systems

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Uploaded June 27, 2011

Understanding

Complex Systems

Author: Jeffrey G. Long ([email protected])

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

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Understanding ComplexUnderstanding Complex Systems: Notational Engineering and Ultra-StructureStructure Jeffrey G. Long

March 28, 2002j ffl @ [email protected]

P oposed o tlineProposed outline

1: Background on the general problem: representation and notational systems

2: Overview of Ultra-Structure: one2: Overview of Ultra Structure: one new approach to complex systems

3: Simple Example of Biology Prototype

March 28, 2002 Copyright 2002 Jeff Long 2

1 h bl1: The Problem


Many if not most of our current problems arise

We may have pragmatic competence in using certain

y o os o ou u p o s sfrom the way we represent them

y p g p gkinds 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 butThis is not because of the nature of the systems, but rather because our analytical tools – our notational systems and the abstractions they reify -- are inadequateinadequate


Complexity is not a property of systems; rather,

Systems appear complex under certain conditions; when

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 b h d ith t t ti l tbreached without a new notational system

These problems cannot be solved by working harder,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 information


information

So far we have settled maybey12 major abstraction spaces


Notational systems are the primary tool that human cognition has developed to embody

Each primary notational system maps a different

u og o s d op d o odyabstractions

“abstraction space” Abstraction spaces are incommensurable Perceiving these is a unique human ability Perceiving these is a unique human ability

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

H i i d h lit th ldHaving acquired such literacy, we see the world differently and can think about it differently


This is essentially a broadening of Whorf’s notion of linguistic relativity, Chomsky’s notion of anof linguistic relativity, Chomsky s notion of an innate linguistic capability, and Tolstoy’s theory of 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: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


Every analytical toolset (which is always based on a

Conclusion to Section 1

y y ( ynotational 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, notationalcivilization!) are, in many cases, notational

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


2: One New Approach


Current systems analysis methods work well onlyCurrent systems analysis methods work well only under certain conditions


The theory is based upon a different way ofThe theory is based upon a different way of describing complex systems and processes

observablebehaviors surface structure

generatesrules

f f l

middle structure

constrainsform of rules deep structure


Rules are a very powerful way of describing thingsthings

Multi-notational: can include all other notational systems

E li itl ti tExplicitly contingent

Describe both behavior and mechanismDescribe both behavior and mechanism

Hundreds of thousands can be represented and pexecuted by a small computer!


Any type of assertion can (evidently) be

Natural language statements

reformulated into one or more If-Then rules

Musical scoresLogical argumentsBusiness processes Architectural drawingsMathematical statementsMathematical statements

But often one “molecular” rule becomes severalBut often one molecular rule becomes several “atomic” rules


Rules can be represented as data (records) i l ti l d t bin a relational database

Ultra-Structure Theory is a general theory of systems representation, developed/tested starting 1985

F ti l t t ti fFocuses on optimal computer representation of complex, conditional and changing rules

Based on a new abstraction called ruleforms

The breakthrough was to find the unchanging features of changing systems


Rules in Ultra-Structure are Literal Implementations of pIf-Then Statements

If X h id A d BIf X then consider A and B Existential Ruleform

TAA (Atomic Weight)

If X and Y then consider A and BIf X and Y then consider A and B Compound RuleformTAATranslation (Stop Encoding)


Structured and Ultra-Structured data are 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

“A i ti ” ft h littl k l d f“Animation” software has little or no knowledge of the external world


The Ruleform HypothesisComplex system structures are created by not-

il l d thnecessarily complex processes; and these processes are created by the animation of operating rules. Operating rules can be grouped i ll b f l h f iinto a small number of classes whose form is prescribed by "ruleforms". While the operating rules of a system change over time, the ruleforms

i ll d i d ll i fremain constant. A well-designed collection of ruleforms can anticipate all logically possible operating rules that might apply to the system,

d h d f hand constitutes the deep structure of the system.


The CoRE HypothesisWe can create “Competency Rule Engines”, or C RE i ti f 50 l f th tCoREs, consisting of <50 ruleforms, that are sufficient to represent all rules found among systems sharing broad family resemblances, e.g. all

ti Th i d fi iti d t t ill bcorporations. Their definitive deep structure will be permanent, unchanging, and robust for all members of the family, whose differences in manifest

d b h i ill b d i lstructures and behaviors will be represented entirely 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.


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


Conclusion to Section 2

One example of a new abstraction is ruleforms ToOne example of a new abstraction is ruleforms. To 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.


3: A simple application example


ReferencesLong, J., and Denning, D., “Ultra-Structure: A design theory for

l d ” C i i f hcomplex 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 (editors) Advancesaddition. In Lasker, G. E. and Farre, G. L. (editors), Advances in Synergetics, Volume I: Systems Research on Emergence. (1996)Long, J., “A new notation for representing business and other g, , p grules.” 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. ( t dit ) S i ti S i l I N t ti l E i i(guest editor), Semiotica Special Issue: Notational Engineering, Volume 125-1/3 (1999)


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Understanding complex systems