Tag
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.