RI-Small: Robust Game Theory and Decision Theory with Resource-Bounded Agents

RI-Small：具有资源有限代理的鲁棒博弈论和决策论

• 批准号: 0812045
• 负责人: Joseph Halpern
• 金额: $ 40.32万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812045&HistoricalAwards=false
• 关键词: RI; Small; Robust; Game; Theory

Decision theory and game theory have proved to be remarkably useful as guides to decision making and strategic thinking. However, people often behave in predictable and systematic ways that are inconsistent with utility maximization models of decision theory. Moreover, players who follow the recommendations of game theory will do far worse than those who do not. These predictive failures of current theories stem from a number of sources, including:1) current theories implicitly assume that agents are perfect reasoners, who can compute the consequences of their actions;2) current solution concepts assume that computation is free, so do not take into account the cost of computation;3) equilibrium concepts implicitly assume that agents know what all other agents are doing;4) current approaches do not take language into account - they implicitly assume that all agents playing a game (or making a decision) describe the game (or decision problem) the same way, and have the same theory of the world;5) all agents are assumed to be rational, according to the modeler of the game/decision problem.Most of these observations are not new (references to previous work are provided in later sections). The time is now ripe to put all these observations together and come up with more realistic foundations for game theory and decision theory. The goal of this project is to construct such a foundation, by using a broad range of techniques from logic, cryptography, and robust distributed computing, and then apply the ideas to the problem of robust mechanism design (i.e., rules for making choices).The project explores new research directions in both computer science and game theory, and the resulting synthesis should enrich both fields. It should have a broader societal impact as well, including more robust design of mechanisms for auctions; better design of peer-to-peer networks; more secure wireless networks; better software agents, that make decisions more comprehensible to users and that take into better account the actions of other users; and, more generally, a deeper understanding of the actions of interacting agents.

决策论和博弈论已被证明是非常有用的决策和战略思维的指南。然而，人们通常以可预测的和系统的方式行事，这与决策理论的效用最大化模型不一致。 此外，遵循博弈论建议的玩家会比那些不遵循的玩家表现得更差。当前理论的这些预测失败源于许多来源，包括：1）当前理论隐含地假设代理是完美的推理者，他们可以计算其行为的后果;2）当前的解决方案概念假设计算是免费的，因此不考虑计算成本;3）均衡概念隐含地假设代理知道所有其他代理正在做什么; 4）其他代理可能会被认为是错误的。4）目前的方法没有考虑语言-他们隐含地假设所有的代理人玩游戏，（或做决定）描述游戏（或决策问题）相同的方法，并具有相同的理论世界; 5）根据博弈/决策问题的建模者，所有的代理人都被假设为理性的。这些观察结果中的大多数都不是新的（对以前工作的参考在后面的章节中提供）。现在时机已经成熟，可以把所有这些观察结果放在一起，为博弈论和决策论提供更现实的基础。本项目的目标是通过使用广泛的逻辑、密码学和鲁棒分布式计算技术来构建这样一个基础，然后将这些思想应用于鲁棒机制设计问题（即，该项目探索了计算机科学和博弈论的新研究方向，由此产生的综合成果将丰富这两个领域。它还应具有更广泛的社会影响，包括更健全的拍卖机制设计;更好的对等网络设计;更安全的无线网络;更好的软件代理，使用户更容易理解决策，并更好地考虑到其他用户的行动;更普遍地说，更深入地了解互动代理的行动。