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Learning with limited memory: Bayesianism vs heuristics

Author

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  • Chatterjee, Kalyan
  • Hu, Tai-Wei

Abstract

Bayesian analysis is considered the optimal way of processing information. However, it often leads to problems for decision-makers with constrained cognitive capacity. Modeling such constrained capacity by finite automata, we answer two questions in the context of Wald's (1947) sequential analysis, namely in what environments is optimal Bayesian analysis possible even with constraints; also, when it is not possible what simplifications in the analysis enable us to obtain a satisfactory outcome. We identify two features of the simplified analysis: information stickiness (ignoring information) and rule stickiness (ignoring small differences in the environment).

Suggested Citation

  • Chatterjee, Kalyan & Hu, Tai-Wei, 2023. "Learning with limited memory: Bayesianism vs heuristics," Journal of Economic Theory, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:jetheo:v:209:y:2023:i:c:s0022053123000388
    DOI: 10.1016/j.jet.2023.105642
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    References listed on IDEAS

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    More about this item

    Keywords

    Imperfect recall; Bounded rationality; Bounded memory; Heuristics; Behavioral biases;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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