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John Holland

Modeling Complex Adaptive Systems

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October 30th, 2008

Peter B. Lewis Bldg., Room 258: 4:00 - 5:00 p.m.

Professor Holland (University of Michigan) is a pioneer in computer science and the originator of genetic algorithms.

Abstract

Many of our most difficult problems center on complex adaptive systems (cas): markets, biological cells, and ecosystems are familiar examples. Cas consist of many agents (processors) that interact in conditional (nonlinear) ways and adapt (learn) as they interact. Innovation, emergence, and diversity are common, important features of these systems. The nonlinearities in cas are severe enough that most theorems of traditional mathematics are of limited help.

It is a commonplace that we understand the world around us—be it proteins, internal combustion engines, or languages—by discovering the relevant building blocks—atoms, components, phonemes. Indeed most innovation comes from combining well-known building blocks in new ways. To understand innovation and emergence in cas, then, we must understand the ways in which adaptation recombines building blocks. This lecture will outline use of building blocks to construct cas models.

Biographical Sketch

Dr. Holland's main research interests are genetic algorithms, complex adaptive systems (natural and artificial), computer-based models of cognitive processes, and the construction of models for computer-based thought experiments. 49 students have completed doctorates under his direction or co-direction. He is a member of the Board of Trustees of the Santa Fe Institute and a board member of the International Society for Genetic and Evolutionary Computation. He has been named a MacArthur Fellow and is a Fellow of the World Economic Forum. His two most recent books are Hidden Order: How Adaptation Builds Complexity and Emergence: From Chaos to Order.

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