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Probabilistic forecasting guides dynamic decisions
Shuze Liu1, Yang Xiang2, Samuel J Gershman2
1Program in Neuroscience, Harvard University.
Psychological Review
|May 11, 2026
Summary
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.
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.
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