Designing Experiments for External Decision Makers

Abstract:Standard Bayesian experimental design (BED) methods assume the experimenter alone will act on the data, choosing the best action given their own beliefs and goals. In practice, data are often acted on by external parties whose procedures the experimenter cannot change and may not know, particularly when the data are released publicly. To extend BED to such settings, we introduce usage-aware experimental design, a framework in which an adaptive design policy is trained to minimise the usage risk: the experimenter's expected loss when one or more external rules act on the collected data. These rules may be non-Bayesian or black-box, and when several may apply, we either average over them or take the worst case. We show that, unlike in standard BED, more informative data can be harmful under such rules, so the usage risk cannot in general be expressed as an expected uncertainty reduction; our framework thus strictly generalises standard BED. We then use it to tackle a range of problems that are difficult to address with existing approaches.

Machine Learning (stat.ML); Machine Learning (cs.LG); Theoretical Economics (econ.TH)

arXiv:2610.22032 [stat.ML]

(or arXiv:2610.22032v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.22032 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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