@article{Ortega2015,
abstract = {Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics. This paper offers a consolidated presentation of a theory of bounded rationality based on information-theoretic ideas. We provide a conceptual justification for using the free energy functional as the objective function for characterizing bounded-rational decisions. This functional possesses three crucial properties: it controls the size of the solution space; it has Monte Carlo planners that are exact, yet bypass the need for exhaustive search; and it captures model uncertainty arising from lack of evidence or from interacting with other agents having unknown intentions. We discuss the single-step decision-making case, and show how to extend it to sequential decisions using equivalence transformations. This extension yields a very general class of decision problems that encompass classical decision rules (e.g. EXPECTIMAX and MINIMAX) as limit cases, as well as trust- and risk-sensitive planning.},
archivePrefix = {arXiv},
arxivId = {1512.06789},
author = {Ortega, Pedro A. and Braun, Daniel A. and Dyer, Justin and Kim, Kee-Eung and Tishby, Naftali},
doi = {10.3390/e16084662},
eprint = {1512.06789},
file = {:Users/brekels/Documents/Mendeley Desktop/Information-Theoretic Bounded Rationality - Ortega et al.pdf:pdf},
issn = {1099-4300},
number = {December 2015},
title = {{Information-Theoretic Bounded Rationality}},
url = {http://arxiv.org/abs/1512.06789},
year = {2015}
}

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