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Biased expectations about future choice options predict sequential economic decisions.

Didrika S van de Wouw1, Ryan T McKay1, Nicholas Furl2

  • 1Royal Holloway, University of London, Egham, UK.

Communications Psychology
|December 18, 2024
PubMed
Summary
This summary is machine-generated.

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People exhibit decision bias in optimal stopping problems, often undersampling options due to pessimistic future expectations. This research identifies mechanisms behind this bias in full-information scenarios.

Area of Science:

  • Cognitive psychology
  • Decision science
  • Computational modeling

Background:

  • Optimal stopping problems involve sequential choices without recall or foresight.
  • Human decision-making in these scenarios often deviates from optimal strategies.
  • Previous research highlights biases in sequential search and selection tasks.

Purpose of the Study:

  • To identify mechanisms driving decision bias in a realistic full-information optimal stopping problem.
  • To investigate the impact of sequence length and payoff schemes on sampling behavior.
  • To develop and test computational models explaining observed biases.

Main Methods:

  • Computational modeling was employed to analyze decision-making processes.
  • Participants engaged in a realistic full-information optimal stopping task.

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  • Sequence length and payoff schemes were manipulated to observe effects on sampling rates.

    • Main Results:

    • Participants showed reluctance to increase sampling rates when optimal, leading to undersampling bias.
    • Suboptimally pessimistic expectations about future options were identified as a key factor.
    • Computational models indicated mis-specified prior distributions contribute to bias.

    Conclusions:

    • Human decision bias in full-information problems stems from pessimistic future expectations.
    • Understanding these biases can improve real-world sequential search strategies.
    • Findings offer insights for optimizing online platforms for option presentation.