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Observability, Dominance, and Induction in Learning Models

Author

Listed:
  • Daniel Clark

    (MIT)

  • Drew Fudenberg

    (MIT)

  • Kevin He

    (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.

Suggested Citation

  • Daniel Clark & Drew Fudenberg & Kevin He, 2022. "Observability, Dominance, and Induction in Learning Models," PIER Working Paper Archive 22-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Handle: RePEc:pen:papers:22-003
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    Cited by:

    1. Ritzberger, Klaus & Weibull, Jörgen W. & Wikman, Peter, 2025. "Solid outcomes in finite games," Journal of Economic Theory, Elsevier, vol. 224(C).
    2. Fudenberg, Drew & Lanzani, Giacomo & Strack, Philipp, 2023. "Pathwise concentration bounds for Bayesian beliefs," Theoretical Economics, Econometric Society, vol. 18(4), November.

    More about this item

    Keywords

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    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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