摘要翻译：
经典决策理论是基于最大期望效用(MEU)原则，但关键是忽略了确定最优决策时所产生的资源成本。本文提出了一个考虑资源成本的有界决策公理框架。Agent被形式化为输入输出流上的概率度量。基于三个公理，我们假定任何这样的概率测度都可以被赋予一个相应的共轭效用函数：效用应该是概率的实值映射、加性映射和单调映射。我们表明，这些公理在效用和概率（从而也就是信息）之间强制执行一个唯一的转换律。此外，我们还证明了这种关系可以刻画为一个变分原理：给定一个效用函数，它的共轭概率测度使一个自由效用泛函最大化。然后，由于目标效用函数表示的新约束的增加，概率度量的转换可以形式化为自由效用的变化。因此，我们得到一个准则来选择一个概率度量，该度量在目标效用函数的最大化和偏离参考分布的代价之间进行权衡。我们证明了最优控制、自适应估计和自适应控制问题可以以资源有效的方式得到解决。当忽略资源成本时，恢复MEU原则。因此，我们的形式化可能会为有限理性提供一个原则性的方法，它与信息论建立了密切的联系。
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英文标题：
《An axiomatic formalization of bounded rationality based on a
  utility-information equivalence》
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作者：
Pedro A. Ortega, Daniel A. Braun
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最新提交年份：
2010
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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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一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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英文摘要：
  Classic decision-theory is based on the maximum expected utility (MEU) principle, but crucially ignores the resource costs incurred when determining optimal decisions. Here we propose an axiomatic framework for bounded decision-making that considers resource costs. Agents are formalized as probability measures over input-output streams. We postulate that any such probability measure can be assigned a corresponding conjugate utility function based on three axioms: utilities should be real-valued, additive and monotonic mappings of probabilities. We show that these axioms enforce a unique conversion law between utility and probability (and thereby, information). Moreover, we show that this relation can be characterized as a variational principle: given a utility function, its conjugate probability measure maximizes a free utility functional. Transformations of probability measures can then be formalized as a change in free utility due to the addition of new constraints expressed by a target utility function. Accordingly, one obtains a criterion to choose a probability measure that trades off the maximization of a target utility function and the cost of the deviation from a reference distribution. We show that optimal control, adaptive estimation and adaptive control problems can be solved this way in a resource-efficient way. When resource costs are ignored, the MEU principle is recovered. Our formalization might thus provide a principled approach to bounded rationality that establishes a close link to information theory.
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PDF链接：
https://arxiv.org/pdf/1007.0940