Some Questions At what level(s) do we define an “organism”? Does it matter? How about systems in general? If it is true that there is a “universal law of vivisystems” -- no matter how deeply we investigate a subunit of a systems, we cannot infer the whole -- what does this imply for educational research and educational improvement? How important is it that many phenomena in the world can be described by the “S-curve”?

Grand Aspirations Kelly: looking for unifying principles off all large vivisystems calling them the “laws of god” Casti: trying to develop the “science of surprise” Waldrop: telling the story of researchers hoping to find commonality between physics and economics (and other fields)

Clockwork Logic vs. Bio-logic Kelly – Chapter 1 Bio-logic being transferred mechanical systems: –Self-replication –Self-governance –Limited self-repair –Mild evolution –Partial learning Clockwork logic being transferred to biology –I.e., bioengineering

Characteristics of “Swarm” systems Also called: networks, complex adaptive systems, vivisystems, collective systems, (dynamical systems?) The absence of imposed centralized control The autonomous nature of subunits The high connectivity between the subunits The webby nonlinear causality of peers and influencing peers

Examples of “Swarm” Systems Bee hives Conference attending playing Pong and flying aircraft with green and red lights The mind “recreating” memory N-body problem? Voting / societal preference aggregation?

Advantages of Swarm Systems Adaptable Evolvable Resilient Boundless Produce Novelty

Disadvantages of Swarm Systems Non-optimal Non-controllable Non-predictable Non-understandable Non-immediate

Attractors However, Casti says that some of these systems may have attractors 3 types: –Fixed point –Periodic (limit cycle) –Strange attractors (“unstable” periodic patterns) Domain of attraction

Features of Dynamical Systems From Casti p. 35 –Small changes in system can lead to a large divergence in outcomes –Randomness does not have to come from uncertainty. It can come from deterministic rules. –Unstable equilibria, or “instability of itineraries”

An Empirical Observation Dynamics of many things in the world follow the pattern below (Marchetti): Time Amount of “X” F/(1-F) Time F=fraction of final amount

Casti’s Attempt at a “Science of Surprise” What are the implications of all this complex systems stuff? Hard to know exactly, but Casti takes a crack at identifying common “surprise generators”: –Logical tangles (e.g., Arrow’s Impossibility Theorem) –Instability (e.g., agglomeration of high-tech firms) –Uncomputability (e.g., wave motion) –Irreducibility (e.g, N-body problem) –Emergence (e.g., Kauffman nets)

Interesting Implications (for me) Caused Brian Arthur a lot of anguish. –He realized that many more economic phenomena are better thought of as complex adaptive systems than economists were willing to admit. (Chap 1, Waldrop.) “Organisms” can be defined at multiple levels Cannot make inferences about the whole only from deep investigation of individual parts Where supreme control is needed, use clockware; where supreme adaptability is needed, use swarmware Sensitivity to initial conditions – even the most deterministic of systems can display wildly divergent behavior with small changes (e.g., Circle- 10 system)