Handbook of Research on Complexity
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Handbook of Research on Complexity

Edited by J. Barkley Rosser Jr.

Complexity research draws on complexity in various disciplines. This Handbook provides a comprehensive and current overview of applications of complexity theory in economics. The 15 chapters, written by leading figures in the field, cover such broad topic areas as conceptual issues, microeconomic market dynamics, aggregation and macroeconomics issues, econophysics and financial markets, international economic dynamics, evolutionary and ecological–environmental economics, and broader historical perspectives on economic complexity.
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Chapter 5: Bounded Rationality and Learning in Complex Markets

Cars H. Hommes


* Cars H. Hommes 5.1 Introduction There are two opposing views concerning the expectations hypothesis in economics and finance. According to the traditional, neoclassical view, propagated by Muth (1961) and Lucas (1972), agents form rational expectations (RE) without any systematic forecasting mistakes. In the rational framework it is often assumed that agents have full knowledge of their economic environment, and use all available information from economic theory to compute rational forecast. Moreover, typically it is assumed that all agents are fully rational, leading to the representative rational agent benchmark. Friedman (1953) provided an early argument in support of the representative rational agent framework, namely that irrational agents would be driven out of the market, since rational agents earn higher profits or utility. Stated differently, evolutionary selection prevents irrational behavior and the economy may be described as if all agents are perfectly rational. Simon (1957) already criticized this view, arguing that deliberation and information gathering costs should be taken into account. More recently, work on bounded rationality in the 1990s, surveyed, for example, in Sargent (1993) and Conlisk (1996), has challenged the traditional view, emphasizing that the extreme assumptions concerning perfect knowledge of the economy and infinite computing capacities are highly unrealistic and in sharp contrast with observed behavior in laboratory experiments with human subjects (for example, Tversky and Kahnemann, 1974). In macroeconomics, much work has been done on adaptive learning, as surveyed, for example, in Evans and Honkapohja (2001). A key underlying assumption is that agents do not know the underlying...

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