Learning, risk attitude and hot stoves in restless bandit problems

被引：55

作者：

Biele, Guido ^{[1
]}

Erev, Ido ^{[2
]}

Ert, Eyal ^{[2
]}

机构：

[1] Ctr Adapt Behav & Cognit, Max Planck Inst Human Dev, D-14195 Berlin, Germany

[2] Technion Israel Inst Technol, IL-32000 Haifa, Israel

来源：

JOURNAL OF MATHEMATICAL PSYCHOLOGY | 2009年 / 53卷 / 03期

关键词：

Dynamic decision making; Probability matching; Underweighting of rare events; The recency/hot stove paradox; Case-based reasoning; MARKOV DECISION-PROCESSES; EXPERIENCE; GAMES; MODELS; INFORMATION; ADAPTATION; STRATEGY; FORGONE; PAYOFFS; EVENTS;

D O I：

10.1016/j.jmp.2008.05.006

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This research examines decisions from experience in restless bandit problems. Two experiments revealed four main effects. (1) Risk neutrality: the typical participant did not learn to become risk averse, a contradiction of the hot stove effect. (2) Sensitivity to the transition probabilities that govern the Markov process. (3) Positive recency: the probability of a risky choice being repeated was higher after a win than after a loss. (4) Inertia: the probability of a risky choice being repeated following a loss was higher than the probability of a risky choice after a safe choice. These results can be described with a simple contingent sampler model, which assumes that choices are made based on small samples of experiences contingent on the current state. (C) 2008 Elsevier Inc. All rights reserved.

引用

页码：155 / 167

页数：13

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