Learning From Payoffs

Learning From Payoffs is a theoretical framework in experimental learning that models how agents update their beliefs or strategies based on observed payoff outcomes, distinguishing itself by focusing on the inferential use of payoff information to guide adaptive behavior in repeated decision-making environments.

94

Publications

8K

Citations

207

Authors

142

Institutions

Publications per year

2017–2026

26

Authors

207

  1. CF
    Colin F. Camerer

    California Institute of Technology, United States

    4 publications · 1.8K citations

    4

    h-index

  2. SA
    Stuart A. West

    University of Oxford, United Kingdom

    3 publications · 216 citations

    3

    h-index

  3. TH
    Teck‐Hua Ho

    University of California, Berkeley, United States

    3 publications · 1.7K citations

    3

    h-index

  4. MN
    Maxwell N. Burton-Chellew

    University of Oxford, United Kingdom

    3 publications · 216 citations

    3

    h-index

  5. JK
    Jotte Kuilder

    University of Amsterdam, Netherlands

    2 publications · 36 citations

    2

    h-index

1–5 of 207

Venues

  1. Games and Economic Behavior

    6 publications · 443 citations

    6

    h-index

  2. SSRN Electronic Journal

    Repository · 6 publications · 149 citations

    6

    h-index

  3. Psychological Science

    3 publications · 457 citations

    3

    h-index

  4. Econometrica

    3 publications · 1.7K citations

    3

    h-index

  5. American Economic Review

    3 publications · 724 citations

    3

    h-index

1–5