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Information theory and behavior

Duncan Foley

No 1731, Working Papers from New School for Social Research, Department of Economics

Abstract: The quantal response behavior widely observed in experiments and observations of human and animal behavior can be derived as expected payoff maximization subject to a constraint on the entropy of the subject's behavior mixed strategy. The Lagrange multiplier corresponding to the entropy constraint is an agent's "behavior temperature". Entropy-constrained behavior approximates payoff-maximizing behavior, but in many contexts exhibits qualitatively different outcomes. The "endowment effect" and other instances of "loss-aversion", for example, can be seen as a consequence of entropy-constrained behavior. Identical entropy-constrained agents with the same value for a good or asset will exhibit spontaneous "noise trading". An entropy-constrained agent with a lower behavior temperature will systematically take economic surplus away from an agent with the same valuation of a good but a higher behavior temperature in bilateral transactions. The equilibrium of a standard supply-demand models with entropy-constrained agents is a non-degenerate frequency distribution of transaction prices rather than a single equilibrium price. Changes in behavior temperature can transorm social interaction games from prisoners' dilemmas to assurance games. Entropy-constrained quantal responses allow quantitative inferences about payoff changes and distribution stronger than qualitative Pareto comparisons.

Keywords: Entropy constraints; behavior temperature; statistical equilibrium; noise trading; market equilibrium (search for similar items in EconPapers)
JEL-codes: A1 C0 C50 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2017-12
New Economics Papers: this item is included in nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://www.economicpolicyresearch.org/econ/2017/NSSR_WP_312017.pdf First version, 2017 (application/pdf)

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