摘要翻译：
在大多数现代决策方法中，一个决策问题是由一组状态和一组结果以及一组丰富的行为来描述的，这些行为是从状态到结果的函数，决策者对此有偏好。然而，大多数有趣的决策问题都没有状态空间和结果空间。事实上，在复杂的问题中，状态和结果空间往往远不清楚。我们提出了一个可供选择的决策基础，其中选择的原始对象是句法程序。用标准表示定理的精神证明了一个表示定理，表明如果DM对选择对象的偏好关系满足适当的公理，那么存在一个状态集S，一个结果集O，一种将选择对象解释为从S到O的函数的方法，S上的概率和O上的效用函数，使得DM更喜欢选择A而不是选择b，当且仅当A的期望效用高于b的期望效用。因此，状态空间和结果空间是主观的，就像概率和效用一样；它们不是问题描述的一部分。原则上，建模者可以在不访问状态或结果的情况下测试SEU行为。我们通过表明它可以捕捉受框架效应影响的决策者来说明我们的方法的力量。
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英文标题：
《Constructive Decision Theory》
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作者：
Lawrence Blume, David Easley, and Joseph Y. Halpern
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最新提交年份：
2021
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分类信息：

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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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：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
  In most contemporary approaches to decision making, a decision problem is described by a sets of states and set of outcomes, and a rich set of acts, which are functions from states to outcomes over which the decision maker (DM) has preferences. Most interesting decision problems, however, do not come with a state space and an outcome space. Indeed, in complex problems it is often far from clear what the state and outcome spaces would be. We present an alternative foundation for decision making, in which the primitive objects of choice are syntactic programs. A representation theorem is proved in the spirit of standard representation theorems, showing that if the DM's preference relation on objects of choice satisfies appropriate axioms, then there exist a set S of states, a set O of outcomes, a way of interpreting the objects of choice as functions from S to O, a probability on S, and a utility function on O, such that the DM prefers choice a to choice b if and only if the expected utility of a is higher than that of b. Thus, the state space and outcome space are subjective, just like the probability and utility; they are not part of the description of the problem. In principle, a modeler can test for SEU behavior without having access to states or outcomes. We illustrate the power of our approach by showing that it can capture decision makers who are subject to framing effects.
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PDF链接：
https://arxiv.org/pdf/0906.4316