Abstract
This chapter will critically examine the non-equilibrium-based complexity theory approach to model building. Complexity theory replaces equilibrium-based models with algorithm-based models. Attention will be paid to the work of W. Brian Arthur and the other researchers at the Santa Fe Institute, with a particular assessment of their approach to including knowledge and learning recognition in their alternative to equilibrium models. Topics discussed include complexity economics, technology, increasing returns, diversity, learning, path dependency and evolution. Particular attention is given to the Santa Fe Institutes use of inductive learning to characterize how a market participant acts in the face of incomplete and uncertain information.
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