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Dynamic oligopoly games with private Markovian dynamics

机译:具有私有马尔可夫动力学的动态寡头博弈

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We analyze a dynamic oligopoly model with strategic sellers and buyers/consumers over a finite horizon. Each seller has private information described by a finite-state Markov process; the Markov processes describing the sellers' information are mutually independent. At the beginning of each time/stage t the sellers simultaneously post the prices for their good; subsequently, consumers make their buying decisions; finally, after the buyers' decisions are made, a public signal, indicating the buyers' consumption experience from each seller's good becomes available and the whole process moves to stage t + 1. The sellers' prices, the buyers' decisions and the signal indicating the buyers' consumption experience are common knowledge among buyers and sellers. This dynamic oligopoly model arises in online shopping and dynamic spectrum sharing markets. The model gives rise to a stochastic dynamic game with asymmetric information. Using ideas from the common information approach (developed in [1] for decentralized decision-making), we prove the existence of common information based equilibria. We obtain a sequential decomposition of the game and we provide a backward induction algorithm to determine common information-based equilibria that are perfect Bayesian equilibria. We illustrate our results with an example.
机译:我们分析了有限范围内战略卖方和买方/消费者的动态寡头模型。每个卖方都有通过有限状态马尔可夫过程描述的私人信息;描述卖方信息的马尔可夫过程是相互独立的。在每个时间/阶段开始时,卖方同时发布其商品的价格;随后,消费者做出购买决定;最终,在做出购买者的决定之后,一个公开信号,表明购买者从每个卖方商品中获得的消费经验,整个过程进入t + 1阶段。卖方的价格,购买者的决定以及表明信号的信号买方的消费经验是买卖双方的常识。这种动态的寡头垄断模型出现在在线购物和动态频谱共享市场中。该模型产生具有不对称信息的随机动态博弈。使用公共信息方法(在[1]中开发用于分散决策)的思想,我们证明了基于公共信息的均衡的存在。我们获得了博弈的顺序分解,并提供了一种后向归纳算法来确定完美的贝叶斯均衡的基于公共信息的均衡。我们用一个例子说明我们的结果。

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