具有战略资源的市场动态机制设计

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摘要翻译：
当任务所有者和资源都是战略性的时，将任务分配给多个资源成为一个有趣的博弈论问题。在经典的非策略环境下，当控制器可以观察到任务和资源的状态时，这个问题就是为马尔可夫决策过程(MDP)寻找最优策略的问题。当状态由战略主体控制时，有效的任务分配问题就超出了求解MDP的问题，而成为设计机制的问题。在此基础上，我们提出了一个通用机制，该机制决定了任务和资源的分配规则以及激励代理参与和真实报告的支付规则。与最近文献中研究的相关动态战略控制问题相比，本文研究的问题具有相互依存的价值：分配对任务所有者的利益不仅是任务本身和分配特性的函数，也是资源状态的函数。我们引入了Mezzetti两阶段机制的动态扩展，以求相互依赖的估值。在这种变化的环境下，所提出的动力机制是有效的、期内事后激励相容的、期内事后个别合理的。
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英文标题：
《Dynamic Mechanism Design for Markets with Strategic Resources》
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作者：
Swaprava Nath, Onno Zoeter, Yadati Narahari, Christopher R. Dance
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最新提交年份：
2012
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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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英文摘要：
  The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.
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PDF链接：
https://arxiv.org/pdf/1202.3751

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关键词：机制设计 Environments Presentation Applications Coordination 机制 efficient owner 环境 rule