Choosing a good toolkit. II: Bayes-rule based heuristics

DOI10.1016/J.JEDC.2019.103814OpenAlexW2995352786WikidataQ126584620 ScholiaQ126584620MaRDI QIDQ2291802

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.2019.103814

zbMATH Keywords

heuristicsmulti-armed banditsbehavioral decision making

Mathematics Subject Classification ID

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

Related Items (3)

Player-compatible learning and player-compatible equilibrium ⋮ Choosing a good toolkit. I: Prior-free heuristics ⋮ Satisficing in Time-Sensitive Bandit Learning

Cites Work

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• Asymptotically efficient adaptive allocation rules
• Spreading and predictable sampling in exchangeable sequences and processes
• Player-compatible learning and player-compatible equilibrium
• Bayesian inference does not lead you astray\dots on average
• The Knowledge Gradient Algorithm for a General Class of Online Learning Problems

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