Learning, risk attitude and hot stoves in restless bandit problems

Guido Biele*, Ido Erev, Eyal Ert

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

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.

Original languageAmerican English
Pages (from-to)155-167
Number of pages13
JournalJournal of Mathematical Psychology
Volume53
Issue number3
DOIs
StatePublished - Jun 2009
Externally publishedYes

Keywords

  • Case-based reasoning
  • Dynamic decision making
  • Probability matching
  • The recency/hot stove paradox
  • Underweighting of rare events

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