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Thermodynamics as a Theory of Decision-Making with Information Processing Costs

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Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we propose an information-theoretic formalization of bounded rational decision-making where decision-makers trade off expected utility and information processing costs. As a result, the decision-making problem can be rephrased in terms of well-known concepts from thermodynamics and statistical physics, such that the same exponential family distributions that govern statistical ensembles can be used to describe the stochastic choice behavior of bounded decision-makers. Furthermore, this framework allows rederiving a number of decision-making schemes including risk-sensitive and robust (minimax) decision-making as well as more recent approximately optimal schemes that are based on the relative entropy. In the limit when resource costs are ignored, the maximum expected utility principle is recovered. Since most of the mathematical machinery can be borrowed from statistical physics, the main contribution is to show how a thermodynamic model of bounded rationality can provide a unified view of diverse decision-making phenomena.

This talk is part of the Machine Learning @ CUED series.

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