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Related Experiment Videos

Decision making under uncertainty: a comparison of simple scalability, fixed-sample, and sequential-sampling models.

J R Busemeyer

    Journal of Experimental Psychology. Learning, Memory, and Cognition
    |July 1, 1985
    PubMed
    Summary

    This study explores how people learn and remember when making decisions with uncertain outcomes. New models were developed to explain these learning and memory processes in decision-making.

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    Area of Science:

    • Cognitive Psychology
    • Decision Science
    • Behavioral Economics

    Background:

    • Decision-making under uncertainty is a fundamental cognitive process.
    • Existing models often fail to capture the complexities of learning and memory in uncertain environments.

    Purpose of the Study:

    • To investigate learning and memory processes in decision-making under uncertainty.
    • To evaluate the efficacy of traditional algebraic-deterministic models and propose new ones.

    Main Methods:

    • Two experiments were conducted involving choices between certain and uncertain payoff alternatives.
    • Learning occurred through decision feedback (Experiment 1) or sample observation (Experiment 2).
    • Response deadlines and observation costs constrained decision and sampling processes, respectively.

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    Main Results:

    • Factorial manipulation of mean, variance, and certain value affected choice probability, response time, and observations.
    • Algebraic-deterministic theories did not adequately explain the observed decision-making patterns.
    • New fixed- and sequential-sampling models were developed and tested.

    Conclusions:

    • Traditional models are insufficient for explaining decision-making under uncertainty.
    • Proposed fixed- and sequential-sampling models offer a better framework for understanding learning and memory in these contexts.
    • Further research into cognitive processes underlying uncertain decision-making is warranted.