  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.
