学习在小型市场中最大化贸易收益
Learning to Maximize Gains From Trade in Small Markets
AI总结：
针对双边市场贸易收益最大化问题，研究发现价值相关分布下一小多买场景存在普遍不可能性，而独立分布下可基于新型最优机制计算法实现高效学习。
摘要：
我们研究设计双边市场（双向拍卖）以在（占优策略）激励相容性和预算平衡约束下最大化贸易收益（社会福利）的问题。我们的目标是针对未知分布实现这一目标，我们可获得该分布的多项式数量的样本。我们的第一个结果是，即使仅在一个卖家和两个买家的情况下，对于价值相关分布也存在普遍的不可能性，这与一个卖家和一个买家（双边贸易）的情况形成对比，后者是可以实现的。我们的第二个结果是针对独立分布下一个卖家和两个买家场景的高效学习算法，该算法基于一种用于计算有限支撑且显式给出的独立分布的最优机制的新颖算法。两项结果都严重依赖于强预算平衡的（占优策略）激励相容机制的特征刻画。
英文摘要：
We study the problem of designing a two-sided market (double auction) to maximize the gains from trade (social welfare) under the constraints of (dominant-strategy) incentive compatibility and budget-balance. Our goal is to do so for an unknown distribution from which we are given a polynomial number of samples. Our first result is a general impossibility for the case of correlated distributions of values even between just one seller and two buyers, in contrast to the case of one seller and one buyer (bilateral trade) where this is possible. Our second result is an efficient learning algorithm for one seller and two buyers in the case of independent distributions which is based on a novel algorithm for computing optimal mechanisms for finitely supported and explicitly given independent distributions. Both results rely heavily on characterizations of (dominant-strategy) incentive compatible mechanisms that are strongly budget-balanced.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
