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arXiv 2009.02053math.OC
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非对称信息获取博弈

Asymmetric Information Acquisition Games

Vartika Singh, Veeraruna Kavitha

摘要:

我们考虑一个随机对策与部分,非对称和非经典信息,代理人试图获得尽可能多的可用机会/锁。代理人只能访问本地信息,信息更新是异步的,我们的目标是获得相关的均衡政策。我们的方法是考虑最佳的开环控制,直到信息更新,这使得管理的信念更新结构化的方式。代理不断控制其泊松搜索时钟的速率以获取锁,并且它们在每次成功获取时都获得奖励;如果所有先前阶段都成功并且代理是第一个完成的,则获取成功。然而,他们中的任何一个都无法获得其他代理人的收购状态,导致一个不对称的信息博弈。利用最优控制理论的标准工具和马尔可夫决策过程(MDP),我们解决了一个双层控制问题,MDP的动态规划方程的每一阶段都用最优控制工具求解。最后,我们将具有无穷多个状态和无穷维行动的博弈简化为具有一维行动的有限状态博弈。在某些特殊情况下,给出了纳什均衡解的闭式表达式,并导出了更多情况下纳什均衡解的渐近表达式。

英文摘要:

We consider a stochastic game with partial, asymmetric and non-classical information, where the agents are trying to acquire as many available opportunities/locks as possible. Agents have access only to local information, the information updates are asynchronous and our aim is to obtain relevant equilibrium policies. Our approach is to consider optimal open-loop control until the information update, which allows managing the belief updates in a structured manner. The agents continuously control the rates of their Poisson search clocks to acquire the locks, and they get rewards at every successful acquisition; an acquisition is successful if all the previous stages are successful and if the agent is the first one to complete. However, none of them have access to the acquisition status of the other agents, leading to an asymmetric information game. Using standard tools of optimal control theory and Markov decision process (MDP) we solved a bi-level control problem; every stage of the dynamic programming equation of the MDP is solved using optimal control tools. We finally reduced the game with an infinite number of states and infinite-dimensional actions to a finite state game with one-dimensional actions. We provided closed-form expressions for Nash Equilibrium in some special cases and derived asymptotic expressions for some more.

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