Observability, Dominance, and Induction in Learning Models

Daniel Clark (), Drew Fudenberg and Kevin He
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Daniel Clark: MIT

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: Learning models do not in general imply that weakly dominated strategies are irrelevant or justify the related concept of “forward induction,” because rational agents may use dominated strategies as experiments to learn how opponents play, and may not have enough data to rule out a strategy that opponents never use. Learning models also do not support the idea that the selected equilibria should only depend on a game’s normal form, even though two games with the same normal form present players with the same decision problems given ?xed beliefs about how others play. However, playing the extensive form of a game is equivalent to playing the normal form augmented with the appropriate terminal node partitions so that two games are information equivalent, i.e., the players receive the same feedback about others’ strategies.

Keywords: learning in games; equilibrium re?nements; iterated dominance; forward induction (search for similar items in EconPapers)
Pages: 44 pages
Date: 2022-01-03
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Observability, dominance, and induction in learning models (2022)
Working Paper: Observability, Dominance, and Induction in Learning Models (2022)
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