Sources of complexity in human systems [Lucio Biggiero, ISSS 1998 Paper Session, July 23/98]

These notes are a rough transcription, prepared as each individual presenter and/or commentator spoke at the ISSS 1998 conference. Gaps and errors have likely occurred. For more accurate citations, please consult the original presenters. These notes have been contributed to the ISSS by David Ing, of the IBM Advanced Business Institute (sabi@systemicbusiness.org).

Lucio Biggiero

Sustainability creates issues in predictability of behaviour.

• Are there any limits to predictability?
• Argue that complexity is a limit to either complete or strong predictability.

• In a conference at Rome, said "complexity is what you don't understand".
• Related to ignorance.
• The other says that "you don't understand complexity".
• More than collecting and having information.

• Difference is through epistemology.

3. What kinds of sources

4. How do they effect distinctions between artificial, natural and social sciences?

• Yes, done in a quite rigourous way.

• Measures degree of disorder.
• Shannon's measure of entropy is helpful.
• Ashby's variety is isomorphic.

1. Trans-computational number (i.e. 10 to 100) of solutions.
2. Infinite number of solutions: complex problem.
3. Logical impossibility of solutions (which are predictions).

• #3 is more restrictive than #2, which is more restrictive than #1

• Difficulty-variety (until trans-computational)
• then first threshold to Complex (unpredictable);
• second threshold to infinite number of solutions;
• third threshold to logical impossibility.

1. Based on quantitative or qualitative nature of information
2. Based on criteria of inclusion
• e.g. logical or computational efficiency

• Prefer #1

• 1.Logical complexity:
• Related to Chaitin's proof of impossibility to distinguish randomness from order in a series
• Solomonoff's criticisms to inductive inferences
• From Godel's incompleteness theorem: can't distinguish randomness from order
• Consequences in writing software, but also in analysis of wastes in production process (as ordered or random / chance).
• 2.Computational complexity: Intractability issue, in math, of one of two types
• Algorithms with exponential time, e.g. traveling salesman problem, to find the shortest path in a route
• Algorithms with polynomial time of class of (a) NP-complete, or (b) P with high-exponent (tractable, but not practically solvable): e.g. simplex algorithm in linear programming, e.g. Stafford Beer or Forrester computation to solve a black box
• 3. Chaotic complexity: from nonlinear dynamics, strange attractors and fixed points
• 4. Self-organization complexity: (isomorphic to chaotic)
• 5. Evolutionary complexity:
• Distinguish from dynamic, which has deterministic chaos, with all information available at the beginning of the system.
• In evolution, don't have all of the information in advance: could have new companies, new inventions.

• 6. Gnosiological complexity:
• Furster, in cybernetics: first order says system doesn't contain all information, it depends on the observer.
• Can recognized all, some or no information, depending on the observer.
• Based on knowledge of external world.
• 7. Semiotic complexity:
• Make sense of the external world, through signs, because they're not self evident.
• 8. Relational complexity:
• Interaction between human beings.
• In an interaction, we change each other.
• When a consultant enters a company, the company is immediately changed, as a new person has entered a system. (Similarly for psychology).
• Possibility to speak in social science in parallel to the indeterminacy theory in physical sciences (so don't have to explain Heisenberg, which is confusing even for physicists!)
• 9. Semantic complexity:
• Related with the deep ambiguity of language, words, and meanings, particularly verbal.

• Natural and artificial, #1 - #5, i.e. logical, computational, chaotic, self-org, evolutionary.
• Biological add #6, #7, #8
• Human systems show all 9 sources of complexity.

Only weak prediction can be done (i.e. local, short-term, aggregated)

• Natural scientists and (usually) systems thinkers usually over-estimate quantitative sources of complexity, and neglect or underestimate qualitative sources.

Questions

• Simon recognizes rationality, but doesn't recognize ambiguity and sensemaking.
• We can find the solution, but what is the problem?

• Literature is distinct.
• Santa Fe deals with self-organization.
• Math deals with chaos.

• All evolutionary systems show emergent properties, but not vice-versa.
• All evolutionary systems show deterministic chaos, i.e. available at the beginning of the system.

• Chess game where the rules change -- which is what Simon doesn't understand.
• Not bounded only in computer power, but also bounded in the knowledge of rules.