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Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting

Zhang, Shunan ; Yu, Angela J (2013)
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting.
NeurIPS 2013. Harrahs and Harveys (05.12.2013-10.12.2013)
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

How humans achieve long-term goals in an uncertain environment, via repeated trials and noisy observations, is an important problem in cognitive science. We investigate this behavior in the context of a multi-armed bandit task. We compare human behavior to a variety of models that vary in their representational and computational complexity. Our result shows that subjects' choices, on a trial-to-trial basis, are best captured by a forgetful" Bayesian iterative learning model in combination with a partially myopic decision policy known as Knowledge Gradient. This model accounts for subjects' trial-by-trial choice better than a number of other previously proposed models, including optimal Bayesian learning and risk minimization, epsilon-greedy and win-stay-lose-shift. It has the added benefit of being closest in performance to the optimal Bayesian model than all the other heuristic models that have the same computational complexity (all are significantly less complex than the optimal model). These results constitute an advancement in the theoretical understanding of how humans negotiate the tension between exploration and exploitation in a noisy, imperfectly known environment."

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2013
Autor(en): Zhang, Shunan ; Yu, Angela J
Art des Eintrags: Bibliographie
Titel: Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting
Sprache: Englisch
Publikationsjahr: 2013
Ort: Lake Tahoe; USA
Verlag: Curran Associates, Inc.
Buchtitel: Advances in Neural Information Processing Systems
Band einer Reihe: 26
Veranstaltungstitel: NeurIPS 2013
Veranstaltungsort: Harrahs and Harveys
Veranstaltungsdatum: 05.12.2013-10.12.2013
URL / URN: https://proceedings.neurips.cc/paper/2013/hash/6c14da109e294...
Kurzbeschreibung (Abstract):

How humans achieve long-term goals in an uncertain environment, via repeated trials and noisy observations, is an important problem in cognitive science. We investigate this behavior in the context of a multi-armed bandit task. We compare human behavior to a variety of models that vary in their representational and computational complexity. Our result shows that subjects' choices, on a trial-to-trial basis, are best captured by a forgetful" Bayesian iterative learning model in combination with a partially myopic decision policy known as Knowledge Gradient. This model accounts for subjects' trial-by-trial choice better than a number of other previously proposed models, including optimal Bayesian learning and risk minimization, epsilon-greedy and win-stay-lose-shift. It has the added benefit of being closest in performance to the optimal Bayesian model than all the other heuristic models that have the same computational complexity (all are significantly less complex than the optimal model). These results constitute an advancement in the theoretical understanding of how humans negotiate the tension between exploration and exploitation in a noisy, imperfectly known environment."

Fachbereich(e)/-gebiet(e): 03 Fachbereich Humanwissenschaften
03 Fachbereich Humanwissenschaften > Institut für Psychologie
Hinterlegungsdatum: 10 Jan 2024 18:27
Letzte Änderung: 10 Jan 2024 18:27
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