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• W2934590717 abstract "In its most basic form, decision-making can be viewed as a computational process that progressively eliminates alternatives, thereby reducing uncertainty. Such processes are generally costly, meaning that the amount of uncertainty that can be reduced is limited by the amount of available computational resources. Here, we introduce the notion of elementary computation based on a fundamental principle for probability transfers that reduce uncertainty. Elementary computations can be considered as the inverse of Pigou–Dalton transfers applied to probability distributions, closely related to the concepts of majorization, T-transforms, and generalized entropies that induce a preorder on the space of probability distributions. Consequently, we can define resource cost functions that are order-preserving and therefore monotonic with respect to the uncertainty reduction. This leads to a comprehensive notion of decision-making processes with limited resources. Along the way, we prove several new results on majorization theory, as well as on entropy and divergence measures." @default.
• W2934590717 created "2019-04-11" @default.
• W2934590717 creator A5000197565 @default.
• W2934590717 creator A5085415063 @default.
• W2934590717 date "2019-04-06" @default.
• W2934590717 modified "2023-10-06" @default.
• W2934590717 title "Bounded Rational Decision-Making from Elementary Computations That Reduce Uncertainty" @default.
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• W2934590717 doi "https://doi.org/10.3390/e21040375" @default.
• W2934590717 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7514859" @default.
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