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Decision Making: P-value Method

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  3. Probabilistic Forecasting Guides Dynamic Decisions.
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  3. Probabilistic Forecasting Guides Dynamic Decisions.

Related Experiment Videos

Probabilistic forecasting guides dynamic decisions.

Shuze Liu1, Yang Xiang2, Samuel J Gershman2

  • 1Program in Neuroscience, Harvard University.

Psychological Review
|May 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

People make investment decisions by learning underlying asset dynamics, not just recent performance. A Bayesian forecasting model accurately predicts how humans choose and switch assets for optimal long-term outcomes.

Related Experiment Videos

Area of Science:

  • Cognitive Science
  • Decision Science
  • Behavioral Economics

Background:

  • Real-world assets like skills or projects evolve endogenously with investment.
  • Deciding optimal investment and divestment timing is a significant challenge.
  • Understanding human decision-making in dynamic asset management is crucial.

Purpose of the Study:

  • To propose and validate a normative model for asset selection and switching.
  • To investigate how individuals infer underlying asset dynamics for decision-making.
  • To compare a Bayesian forecasting approach against myopic strategies.

Main Methods:

  • Developed a Bayesian function learning model for asset performance forecasting.
  • Conducted four experiments (N=460) manipulating selection vs. switching, time horizons, and performance trajectories.
  • Compared the Bayesian model's predictions against foraging and study time allocation models.

    • Main Results:

    • The Bayesian forecasting model significantly outperformed myopic alternatives.
    • Human decision-making aligns with inferring invariant structural parameters of asset performance.
    • Participants forecasted final asset performances based on task-specific time horizons.

    Conclusions:

    • Humans exhibit future-looking cognition, anticipating how actions shape future asset performance.
    • Underlying structural dynamics representations inform human choices in dynamic environments.
    • The Bayesian probabilistic forecasting model provides a computational framework for decisions with lasting consequences.