Decision-Making under Combinatorial Risk
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Computer Science > Machine Learning
Title:Decision-Making under Combinatorial Risk
Abstract:Decision-making under risk is typically studied through single-shot lottery choices. Yet many real decisions involve combinatorial risk, where risk arises from multiple risky components, so the lottery over outcomes is induced rather than given outright and can be costly to evaluate exactly. We introduce an investment-allocation task to study decision under combinatorial risk, where investing in a component raises its success probability and thereby reshapes the outcome distribution. Participants favor the option with the larger probability increment, and, when increments are equal, the option with the higher initial success probability. Revealing the induced probability mass function (PMF) substantially changes behavior, making participants less responsive to combinatorial-risk features and reducing choice variance. To explain these patterns, we move beyond standard benchmarks and hand-crafted hypotheses with symbolic regression to discover compact descriptive models. The discovered models rely mainly on combinatorial-risk features, such as the after-investment success probability, rather than exact evaluation of the full induced distribution. Behavior under the displayed PMF is then well explained by augmenting this model with a prospect-theoretic residual model. The results show that people navigate combinatorial risk primarily through its core features, shifting toward lottery valuation only when the induced PMF is displayed.
| Subjects: | Machine Learning (cs.LG); General Economics (econ.GN) |
| Cite as: | arXiv:2606.10092 [cs.LG] |
| (or arXiv:2606.10092v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10092
arXiv-issued DOI via DataCite (pending registration)
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