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About this paper
Osvaldo Simeone
The authors organize decision-making under hidden states around uncertainty summaries matched to the objective and knowledge of the environment. They show that posteriors support risk-neutral expected-utility choices, while covered prediction sets yield risk-averse max–min utility floors; optimal actions require optimally chosen sets. For unknown environments, calibration, distributionally robust optimization, and Bayesian inference offer conditional guarantees, not experimental comparisons.
Summary by alphaXiv.
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