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摘要翻译：
在本文中，我们提出了一个博弈模型来分析类似于2009\EMPH{DARPA网络挑战}的事件，该事件是由国防高级研究计划局(DARPA)组织的，旨在探索互联网和社交网络在激励广域合作中的作用。挑战是组建一个小组，第一个找到美国十个停泊气象气球的位置。我们考虑了一个模型，其中$N$people（他们可以组成组）位于某个拓扑中，每个人的地理位置周围有固定的覆盖体积。我们考虑了玩家可以位于的各种拓扑，如欧几里得D$维空间和图的顶点。一个气球被放置在空间中，如果第一个报告气球位置的小组获胜。一个较大的团队找到气球的概率较高，但我们假设奖金平分给团队成员。因此，有一个竞争紧张，以保持团队尽可能小。风险厌恶是指一个人不愿意接受一个收益不确定的交易，而不愿意接受另一个预期收益更确定但可能更低的交易。在我们的模型中，我们考虑了从相对风险厌恶的Arrow-Pratt测度导出的\emph{等弹性}效用函数。我们的主要目的是分析纳什均衡中群的结构。对于$d$-维欧氏空间($d\geq1$)和一类有界度正则图，我们证明了在任何Nash均衡中，每个人的期望效用最大的\emph{最富}群覆盖了总体积的一个常分数。
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英文标题：
《A Game-Theoretic Model Motivated by the DARPA Network Challenge》
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作者：
Rajesh Chitnis, MohammadTaghi Hajiaghayi, Jonathan Katz, Koyel
Mukherjee
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最新提交年份：
2013
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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：Computer Science 计算机科学
二级分类：Multiagent Systems 多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
In this paper we propose a game-theoretic model to analyze events similar to the 2009 \emph{DARPA Network Challenge}, which was organized by the Defense Advanced Research Projects Agency (DARPA) for exploring the roles that the Internet and social networks play in incentivizing wide-area collaborations. The challenge was to form a group that would be the first to find the locations of ten moored weather balloons across the United States. We consider a model in which $N$ people (who can form groups) are located in some topology with a fixed coverage volume around each person's geographical location. We consider various topologies where the players can be located such as the Euclidean $d$-dimension space and the vertices of a graph. A balloon is placed in the space and a group wins if it is the first one to report the location of the balloon. A larger team has a higher probability of finding the balloon, but we assume that the prize money is divided equally among the team members. Hence there is a competing tension to keep teams as small as possible. \emph{Risk aversion} is the reluctance of a person to accept a bargain with an uncertain payoff rather than another bargain with a more certain, but possibly lower, expected payoff. In our model we consider the \emph{isoelastic} utility function derived from the Arrow-Pratt measure of relative risk aversion. The main aim is to analyze the structures of the groups in Nash equilibria for our model. For the $d$-dimensional Euclidean space ($d\geq 1$) and the class of bounded degree regular graphs we show that in any Nash Equilibrium the \emph{richest} group (having maximum expected utility per person) covers a constant fraction of the total volume.
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PDF链接：
https://arxiv.org/pdf/1204.6552
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