Choosing a good toolkit. I: Prior-free heuristics

DOI10.1016/J.JEDC.2019.103813OpenAlexW2994947046WikidataQ126594429 ScholiaQ126594429MaRDI QIDQ2291801

Alejandro Francetich, David M. Kreps

Publication date: 31 January 2020

Published in: Journal of Economic Dynamics \& Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jedc.2019.103813

zbMATH Keywords

heuristicsreinforcement learningmulti-armed banditsbehavioral decision makingprior free

Mathematics Subject Classification ID

Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Related Items (2)

Player-compatible learning and player-compatible equilibrium ⋮ Satisficing in Time-Sensitive Bandit Learning

Cites Work

• Unnamed Item
• Adaptive and sophisticated learning in normal form games
• Satisficing
• Why imitate, and if so, how? A boundedly rational approach to multi-armed bandits
• Optimal properties of stimulus-response learning models.
• Choosing a good toolkit. II: Bayes-rule based heuristics
• Optimal guessing: choice in complex environments
• Choice Without Beliefs

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