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This paper proposes ProSE-Plan, a Bayes-adaptive planner that improves AI assistance by considering user evaluability under bounded rationality, using proposals as probes to learn preferences and enhance decision-making.
This paper extends Herbert Simon's bounded rationality to moral cognition, formalizing a tradeoff between moral breadth and moral depth for finite agents. It argues that ethical theories are locally optimal strategies within a constrained space, with implications for AI alignment.
This paper studies generalization in learning through the lens of bounded-rational decision theory, where the learner's response law induces a tradeoff between training loss and sample dependence. The authors show that this tradeoff is governed by an f-divergence regularizer and that generalization can be certified from the learner's hedging behavior.
This paper proposes an attention-guided decision framework for hospital pharmacists managing drug shortages, modeling bounded rationality by dynamically decomposing drugs into urgent and monitoring subsets, and shows that selective attention enables stable decision-making without full state reasoning.