Presents an algorithm-to-contract framework that lifts FPTASes to approximately incentive-compatible contracts for budgeted combinatorial problems without demand oracles, applying to matroids, matchings, and multi-agent non-additive rewards.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Applies instrumental regression and GMM to learn contracts under moral hazard in multitasking principal-agent problems and characterizes uniformity of optimal contract shapes.
citing papers explorer
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An Algorithm-to-Contract Framework without Demand Queries
Presents an algorithm-to-contract framework that lifts FPTASes to approximately incentive-compatible contracts for budgeted combinatorial problems without demand oracles, applying to matroids, matchings, and multi-agent non-additive rewards.
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Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Moments
Applies instrumental regression and GMM to learn contracts under moral hazard in multitasking principal-agent problems and characterizes uniformity of optimal contract shapes.