Perfectly rational decision-makers maximize expected utility, but crucially
ignore the resource costs incurred when determining optimal actions. Here we
propose an information-theoretic formalization of bounded rational
decision-making where decision-makers trade off expected utility and
information processing costs. Such bounded rational decision-makers can be
thought of as thermodynamic machines that undergo physical state changes when
they compute. Their behavior is governed by a free energy functional that
trades off changes in internal energy-as a proxy for utility-and entropic
changes representing computational costs induced by changing states. As a
result, the bounded rational decision-making problem can be rephrased in terms
of well-known concepts from statistical physics. In the limit when
computational costs are ignored, the maximum expected utility principle is
recovered. We discuss the relation to satisficing decision-making procedures as
well as links to existing theoretical frameworks and human decision-making
experiments that describe deviations from expected utility theory. Since most
of the mathematical machinery can be borrowed from statistical physics, the
main contribution is to axiomatically derive and interpret the thermodynamic
free energy as a model of bounded rational decision-making.

该论文提出了一个信息论的有界理性决策模型，其中决策者在预期效用和信息处理成本之间进行权衡，被视为物理状态发生变化时的热力学机器，行为受制于自由能泛函。当忽略计算成本时，该模型还原为最大化预期效用原则。