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Deviations from rational decision-making due to limited computationalresources have been studied in the field of bounded rationality, originallyproposed by Herbert Simon. There have been a number of different approaches tomodel bounded rationality ranging from optimality principles to heuristics.Here we take an information-theoretic approach to bounded rationality, whereinformation-processing costs are measured by the relative entropy between aposterior decision strategy and a given fixed prior strategy. In the case ofmultiple environments, it can be shown that there is an optimal prior renderingthe bounded rationality problem equivalent to the rate distortion problem forlossy compression in information theory. Accordingly, the optimal prior andposterior strategies can be computed by the well-known Blahut-Arimoto algorithmwhich requires the computation of partition sums over all possible outcomes andcannot be applied straightforwardly to continuous problems. Here we derive asampling-based alternative update rule for the adaptation of prior behaviors ofdecision-makers and we show convergence to the optimal prior predicted by ratedistortion theory. Importantly, the update rule avoids typical infeasibleoperations such as the computation of partition sums. We show in simulations aproof of concept for discrete action and environment domains. This approach isnot only interesting as a generic computational method, but might also providea more realistic model of human decision-making processes occurring on a fastand a slow time scale.

Adaptive information-theoretic bounded rational decision-making with parametric priors