<ul><li>The entropic risk measure is commonly used in high-stakes decision making to account for uncertain losses.</li><li>An empirical entropic risk estimator is often biased and underestimates the true risk with limited data.</li><li>A bootstrapping procedure is proposed to debias the empirical entropic risk estimator, improving risk estimation.</li><li>The approach is applied to distributionally robust entropic risk minimization and insurance contract design problems.</li></ul>

Data-driven decision-making under uncertainty with entropic risk measure

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