2015/12/21 by Pedro A. Ortega, Daniel A. Braun, Ortega, Pedro A. +7 · 1 citation
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Computability, Logic, AI Algorithms #Decision-Making and Behavioral Economics #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1512.06789
openalex publication_date 2015/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.