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Practical strategic reasoning with applications in market games.

机译:在市场游戏中应用的实用战略推理。

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摘要

Strategic reasoning is part of our everyday lives: we negotiate prices, bid in auctions, write contracts, and play games. We choose actions in these scenarios based on our preferences, and our beliefs about preferences of the other participants. Game theory provides a rich mathematical framework through which we can reason about the influence of these preferences. Clever abstractions allow us to predict the outcome of complex agent interactions, however, as the scenarios we model increase in complexity, the abstractions we use to enable classical game-theoretic analysis lose fidelity. In empirical game-theoretic analysis, we construct game models using empirical sources of knowledge---such as high- fidelity simulation. However, utilizing empirical knowledge introduces a host of different computational and statistical problems.;I investigate five main research problems that focus on efficient selection, estimation, and analysis of empirical game models. I introduce a flexible modeling approach, where we may construct multiple game-theoretic models from the same set of observations. I propose a principled methodology for comparing empirical game models and a family of algorithms that select a model from a set of candidates.;I develop algorithms for normal-form games that efficiently identify formations---sets of strategies that are closed under a (correlated) best-response correspondence. This aids in problems, such as finding Nash equilibria, that are key to analysis but hard to solve. I investigate policies for sequentially determining profiles to simulate, when constrained by a budget for simulation. Efficient policies allow modelers to analyze complex scenarios by evaluating a subset of the profiles. The policies I introduce outperform the existing policies in experiments.;I establish a principled methodology for evaluating strategies given an empirical game model. I employ this methodology in two case studies of market scenarios: first, a case study in supply chain management from the perspective of a strategy designer; then, a case study in Internet ad auctions from the perspective of a mechanism designer. As part of the latter analysis, I develop an ad-auctions scenario that captures several key strategic issues in this domain for the first time.
机译:战略推理是我们日常生活的一部分:我们协商价格,竞标,签订合同和玩游戏。我们根据自己的偏好以及对其他参与者的偏好的信念来选择在这些情况下采取的行动。博弈论提供了一个丰富的数学框架,通过该框架我们可以推理这些偏好的影响。聪明的抽象使我们能够预测复杂的智能体交互的结果,但是,随着我们对场景进行建模的复杂性的增加,用于进行经典博弈论分析的抽象就失去了保真度。在实证博弈论分析中,我们使用实证知识源(例如高保真模拟)构建博弈模型。但是,利用经验知识会引入许多不同的计算和统计问题。我研究了五个主要研究问题,这些问题集中在经验博弈模型的有效选择,估计和分析上。我介绍一种灵活的建模方法,在这种方法中,我们可以根据同一组观察结果构建多个博弈论模型。我提出了一种用于比较经验博弈模型的有原则的方法论,并提出了从一组候选者中选择模型的一系列算法。;我开发了用于有效形式博弈的算法,该算法可以有效地识别编队-在((相关)最佳响应对应。这有助于解决诸如纳什均衡之类的问题,这些问题是分析的关键但难以解决。我研究了在受预算预算约束的情况下顺序确定要模拟的配置文件的策略。高效的策略允许建模人员通过评估概要文件的子集来分析复杂的方案。在实验中,我介绍的策略要优于现有策略。;在给定经验博弈模型的情况下,我建立了评估策略的原则方法。我在两个针对市场场景的案例研究中采用了这种方法:首先,从策略设计者的角度对供应链管理进行案例研究;然后,从机制设计者的角度对互联网广告拍卖进行案例研究。作为后面的分析的一部分,我开发了一个拍卖场景,该场景首次捕获了该领域的几个关键战略问题。

著录项

  • 作者

    Jordan, Patrick R.;

  • 作者单位

    University of Michigan.;

  • 授予单位 University of Michigan.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 210 p.
  • 总页数 210
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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