摘要翻译：
完全理性的决策者最大化期望效用，但至关重要的是忽略了在确定最优行动时所产生的资源成本。在这里，我们采用了一个有限理性决策的公理框架，它基于资源成本作为信息成本的热力学解释。这导致了一个变分的“自由效用”原理，类似于热力学自由能，它权衡了效用和信息成本。我们证明了从这个变分原理可以得到有界最优控制解，从而得到一般的随机策略。此外，我们还证明了当环境被视为有限理性和完全理性的对手时，风险敏感和鲁棒（minimax）控制方案自然地从该框架中分离出来。当忽略资源成本时，恢复最大期望效用原则。
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英文标题：
《Information, Utility & Bounded Rationality》
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作者：
Pedro A. Ortega and Daniel A. Braun
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we employ an axiomatic framework for bounded rational decision-making based on a thermodynamic interpretation of resource costs as information costs. This leads to a variational "free utility" principle akin to thermodynamical free energy that trades off utility and information costs. We show that bounded optimal control solutions can be derived from this variational principle, which leads in general to stochastic policies. Furthermore, we show that risk-sensitive and robust (minimax) control schemes fall out naturally from this framework if the environment is considered as a bounded rational and perfectly rational opponent, respectively. When resource costs are ignored, the maximum expected utility principle is recovered.
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PDF链接：
https://arxiv.org/pdf/1107.5766