Information-Theoretic Bounded Rationality
By Pedro A. Ortega, Daniel A. Braun, Justin Dyer, Kee-Eung Kim, Naftali Tishby. 2015
59 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, General Decision Sciences
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Citations in the corpus
- Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach (2017, result): “In addition, we see a parallel with recent work on information-theoretic bounded rationality [Ortega et al., 2015] which seeks to formalize decision-making with limited resources such as the time, energy, memory, and computational effort”