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1 Complex systems: How to think like nature Unintended consequences. Emergence: what’s right and what’s wrong with reductionism. Design: levels of abstraction/platforms.

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Presentation on theme: "1 Complex systems: How to think like nature Unintended consequences. Emergence: what’s right and what’s wrong with reductionism. Design: levels of abstraction/platforms."— Presentation transcript:

1 1 Complex systems: How to think like nature Unintended consequences. Emergence: what’s right and what’s wrong with reductionism. Design: levels of abstraction/platforms. Evolutionary processes, genetic algorithms, and entities as nature’s memes. Wisdom of crowds: hierarchy, autonomy, commons, C2. Externalizing thought Feasibility ranges (vs. engineering margins) Simulations: floor and noticing emergence. Computer Science vs. Engineering

2 2 Try it out File > Models Library > Biology > Evolution > Peppered Moths Click Open

3 3 Peppered moths model At each time tick, a moth’s probability of survival—not being eaten by predators (not shown)—depends on –How close its color (1-9) is to the background color (0-8). –The “Selection” slider, which controls the impact of the environment. The higher the slider, the more important the environment. Moths both reproduce and die (of old age). –They may mutate, i.e., have offspring of a different color. Illustrates the nature of evolution. –Moth (and their colors) are rivals, not competitors. Nature is not “red in tooth and claw” (in this model). –The moths and their colors don’t compete with each other directly. –Colors confer survival value (fitness) depending on the environment. More like a race than a boxing match.

4 4 Traveling salesman problem (TSP) Connect the cities with a path that Starts and ends at the same city. Includes all cities. Includes no city twice. The obvious routes all include the sequence: ACED-54 (or its reverse). The question is where to put B: ABCED-55, ACBED-57, ACEBD-56. B B A A D D E E C C 20 13 12 14 12 7 9 4 24

5 5 Genetic algorithm Create a population of possible paths. AEBCD-59, ACBED-57, ADCBE-59, ACDEB-71, … In this case there are only 4! = 24 possible routes. Could examine them all. Usually that’s not possible. Repeat Select one or two tours as parents. Better tours are more likely to be selected. Generate offspring using genetic operators. (Re)combine two tours: ACBED-57 & ADCBE → ACBED-57. Exchange two cities: ACDEB-71 → ACBED-57 Reverse a subtour: ACBED-57 → AEBCD-59 Possibly mutate the result: ADCBE-59 → ACBDE-70 B B A A D D E E C C 20 13 12 14 12 7 9 4 24 A second exchange solves the problem. ACBED-57 → ABCED-55


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