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

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
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2. Understanding Complex
Systems: Notational
Engineering and Ultra-
Structure Jeffrey G. Long
March 28, 2002
jefflong@aol.com
j ffl @ l

3. P oposed o tline
Proposed 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 Prototype
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4. 1: h
1 The Problem
bl
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5. Many if not most of our current problems arise
y o os o ou u p o s s
from 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
inadequate
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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
information
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7. So far we have settled maybe
y
12 major abstraction spaces
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8. Notational systems are the primary tool that
human cognition has d
u og o s developed to embody
op d o ody
abstractions
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 differently
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9. This is essentially a broadening of Whorf’s notion
of linguistic relativity, Chomsky s notion of an
Chomsky’s
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:
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
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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
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11. 2: One New Approach
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12. Current systems analysis methods work well only
under certain conditions
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13. The theory is based upon a different way of
describing complex systems and processes
observable
behaviors surface structure
generates
rules middle structure
constrains
form of rules
f f l deep structure
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14. Rules are a very powerful way of describing
things
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!
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15. Any type of assertion can (evidently) be
reformulated 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” rules
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16. Rules can be represented as data (records)
i a relational d t b
in 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 systems
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17. Rules in Ultra-Structure are Literal Implementations of
p
If-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) Ruleform
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18. 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
“Animation” ft
“A i ti ” software h littl or no knowledge of
has little k l d f
the external world
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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 h
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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.
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21. The deep structure of a system
p y
specifies 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 understanding
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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.
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23. 3: A simple application example
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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)
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