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Complexity, Emergence, and Chaos:

Complexity, Emergence, and Chaos: . Application to Regional Industrial Systems. Geog 220: Geosimulation Lisa Murawski 1/31/05. Complexity. Has many guises Information processing, physical systems, computer science/computation, psychology

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Complexity, Emergence, and Chaos:

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  1. Complexity, Emergence, and Chaos: Application to Regional Industrial Systems Geog 220: Geosimulation Lisa Murawski 1/31/05

  2. Complexity • Has many guises • Information processing, physical systems, computer science/computation, psychology • Our concern: the study of the emergence of macro-properties from the micro-properties • Complex systems have field-specificity (economics, biology, physical science) • Consist of agents • Agents interact in non-linear ways • Exhibit properties of emergence

  3. Emergence • Slippery fish • But not quite “eye-of-the-beholder”! • Properties: • Repeating patterns in a system that exhibits perpetual novelty (regularities) • Building block-type organization • Interactions are bottom-up and top-down • Whole is more than the sum of its parts

  4. Regional Economies as Complex Systems • Regional economies exhibit regularities • Definite building-block structure • Feedback-type interaction at many levels • Regional economy has properties that individual parts of the system don’t have • Bottom line: regional economies can be viewed as self-organizing, complex adaptive systems composed of agents that exhibit emergence and chaotic behavior Government decisions Business decisions Individual decisions

  5. So What? • R. W. White. Transitions to chaos with increasing system complexity: the case of regional industrial systemsEnvironment and Planning A, 1985, v.17, p. 387-396

  6. Economic Model • Objective: • Represent onset of chaotic behavior in a spatially distributed economic system • Method: • Simulation models economies and diseconomies of scale and aggregation • Profit = Revenue – Cost Revenue = f (output, price (in all centers)) Cost = f (economies and diseconomies of scale and urbanization)

  7. Methodology Simulate happenings for t=1-1000, for one- and two-sector cases, for one or more centers Find the intrinsic growth rate r where chaotic behavior begins • Chaos • Formal mathematical definition (unlike emergence?) • Chaotic behavior is: • Aperiodic • Extremely sensitive to initial conditions

  8. Results • Greater complexity  systems exhibit chaotic behavior for a lower intrinsic growth rate r • Greater complexity  harder to tell exactly where chaotic behavior begins • Chaotic systems do exhibit some periodicity, predictable to some degree

  9. Discussion • Relevance to real economic behavior • Realistic interpretation of growth rate • Chaotic behavior in one industry may induce same in others • Evolution of economic system may be chaotic, not stochastic • Business cycle itself may be one chaotic oscillation!

  10. Doggie Bag of Ideas • Emergence, complexity and chaos are not just abstract ideas • Chaotic behavior of self-organizing systems can be a useful explanatory (and exploratory) tool • Complex systems thinking is a framework, a different approach

  11. Journal of Complexity

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