Dynamic Mechanism Design: Incentive Compatibility, Profit Maximization and Information Disclosure

A. Pavan, Ilya Segal, Juuso Toikka
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引用次数: 108

Abstract

We examine the design of incentive-compatible screening mechanisms for dynamic environ- ments in which the agents'types follow a (possibly non-Markov) stochastic process, decisions may be made over time and may aect the type process, and payos need not be time-separable. We derive a formula for the derivative of an agent's equilibrium payowith respect to his cur- rent type in an incentive-compatible mechanism, which summarizes all …rst-order conditions for incentive compatibility and generalizes Mirrlees's envelope formula of static mechanism design. We provide conditions on the environment under which this formula must hold in any incentive- compatible mechanism. When specialized to quasi-linear environments, this formula yields a dynamic "revenue-equivalence"result and an expression for dynamic virtual surplus, which is instrumental for the design of optimal mechanisms. We also provide some su¢ cient conditions for incentive compatibility, and for its robustness to an agent's observation of the other agents' past and future types. We apply these results to a number of novel settings, including the de- sign of pro…t-maximizing auctions and durable-good selling mechanisms for buyers whose values
动态机制设计:激励相容、利润最大化与信息披露
我们研究了动态环境中激励兼容筛选机制的设计,在这种环境中,agent的类型遵循(可能是非马尔可夫的)随机过程,决策可能随着时间的推移而做出,并且可能影响类型过程,并且支付不需要是时间可分的。导出了激励相容机制下主体均衡报酬对其当前类型的导数公式,总结了激励相容的所有一阶条件,推广了静态机制设计的Mirrlees包络公式。我们提供了在任何激励相容机制中该公式必须成立的环境条件。在拟线性环境下,该公式可得到动态的“收益等价”结果和动态虚拟剩余的表达式,为优化机制的设计提供了理论依据。我们还为激励兼容性提供了一些充分的条件,并为其对代理对其他代理过去和未来类型的观察的鲁棒性提供了一些条件。我们将这些结果应用于一些新颖的设置,包括设计收益最大化拍卖和耐用品销售机制的买家的价值
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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