Nonparametric learning rules from bandit experiments: The eyes have it!

Arbeitspapier

How do people learn? We assess, in a distribution-free manner, subjects' learning and choice rules in dynamic two-armed bandit (probabilistic reversal learning) experiments. To aid in identification and estimation, we use auxiliary measures of subjects' beliefs, in the form of their eye-movements during the experiment. Our estimated choice probabilities and learning rules have some distinctive features; notably that subjects tend to update in a non-smooth manner following choices made in accordance with current beliefs. Moreover, the beliefs implied by our nonparametric learning rules are closer to those from a (non-Bayesian) reinforcement learning model, than a Bayesian learning model.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Working Paper ; No. 560

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Search; Learning; Information and Knowledge; Communication; Belief; Unawareness ; Design of Experiments: Laboratory, Individual ; Semiparametric and Nonparametric Methods: General
• Thema: Learning ; experiments ; eye-tracking ; Bayesian vs. non-Bayesian learning ; nonparametric estimation ; Schätztheorie ; Lernen ; Nichtparametrisches Verfahren ; Simulation ; Dynamisches Modell

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Hu, Yingyao ; Kayaba, Yutaka ; Shum, Matt
• Ereignis: Veröffentlichung
• (wer): The Johns Hopkins University, Department of Economics
• (wo): Baltimore, MD
• (wann): 2010

Weitere Informationen

• Handle: http://hdl.handle.net/10419/49877
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ