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摘要翻译：
我们研究了少数人博弈的一个变体。有N个探员。每个人每天都要从两个选项中选择一个，小团队的每个成员都有奖励。代理不能相互交流，而是试图猜测其他人将做出的选择，仅基于选择这两个选项的人数的过去历史。我们描述了一个简单的概率策略，使用该策略，智能体独立行动，仍然可以最大化每天受益的平均人数。该策略能够非常有效地利用资源，对于任何$\epsilon>0$，与最大可能值的平均偏差可以按顺序$(n^{\epsilon}）$排列。我们还表明，单个代理并不期望通过不遵循策略而获得收益。
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英文标题：
《Cooperation amongst competing agents in minority games》
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作者：
Deepak Dhar, V. Sasidevan, Bikas K. Chakrabarti
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最新提交年份：
2011
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
We study a variation of the minority game. There are N agents. Each has to choose between one of two alternatives everyday, and there is reward to each member of the smaller group. The agents cannot communicate with each other, but try to guess the choice others will make, based only the past history of number of people choosing the two alternatives. We describe a simple probabilistic strategy using which the agents acting independently, can still maximize the average number of people benefitting every day. The strategy leads to a very efficient utilization of resources, and the average deviation from the maximum possible can be made of order $(N^{\epsilon})$, for any $\epsilon >0$. We also show that a single agent does not expect to gain by not following the strategy.
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PDF链接：
https://arxiv.org/pdf/1102.4230
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