Stochastic game theory: Introspection, evolution, and equilibrium

Charles A. Holt

Chapter 8 in The Origins and Evolution of Experimental Economics, 2026, pp 131-163 from Edward Elgar Publishing

Abstract: This chapter introduces modeling approaches that have been used to organize observed data patterns in game experiments. These approaches include models of introspection like level K (for games played once), evolution and learning (for considering dynamic adjustments), and equilibrium like Nash or quantal response (for predicting stable behavior after adjustments). In each case, the best-response and noisy-response approaches are compared. The method of analysis (introspection, learning, or equilibrium) should be geared to the nature of the experiment data (one-shot, early periods of adjustment, or later periods after things settle down). Applications include coordination, guessing, signaling, and social dilemmas. The chapter ends with a discussion of “refinements” used to select among multiple equilibria. The problem with the refinements literature is that people do not start in equilibrium and consider the meaning of an observed deviation. Instead, they remember which “types” previously used the deviation decision during the prior process of adjustment.

Keywords: Level K; Noisy Introspection; Learning; Gradient-Based Evolution; Coordination Games; Traveler's Dilemma Game; Potential Games; Nash Refinement (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035356256
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