Download Grand Aspirations

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:
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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
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Adaptable
Evolvable
Resilient
Boundless
Produce Novelty
Disadvantages of Swarm Systems
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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
F/(1-F)
Amount of “X”
• Dynamics of many things in the world
follow the pattern below (Marchetti):
Time
F=fraction of final
amount
Time
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”:
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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., Circle10 system)