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
Parents
94
Experience-weighted Attraction Learning in Normal Form Games
Colin F. Camerer, Teck‐Hua Ho · Econometrica · 1999 · 1.5K citations
Sanjeev Goyal · The Review of Economic Studies · 1998 · 770 citations
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142
University College London
London, United Kingdom
6 h-index
13 publications
575 citations
University of Oxford
Oxford, United Kingdom
6 h-index
13 publications
651 citations
The University of Texas at Austin
Austin, United States
5 h-index
7 publications
358 citations
California Institute of Technology
Pasadena, United States
5 h-index
5 publications
1.8K citations
University of Pennsylvania
Philadelphia, United States
5 h-index
15 publications
1.6K citations

University of Amsterdam
Amsterdam, The Netherlands
3 h-index
3 publications
107 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
5 publications
102 citations

University of Essex
Colchester, United Kingdom
3 h-index
5 publications
262 citations
1–8 of 142
6 publications · 443 citations
6
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Repository · 6 publications · 149 citations
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3 publications · 457 citations
3
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3 publications · 1.7K citations
3
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3 publications · 724 citations
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