arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.07503cs.GTcs.DS
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有限信息下的高效双边市场

Efficient Two-Sided Markets with Limited Information

Paul Dütting, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser

AI总结:

研究双边市场中设计近优近似机制所需的最少先验信息量,证明无先验信息时无法实现有意义近似,并证明仅需每个卖方一个样本即可实现高效近似,同时设计了计算高效的 blackbox reductions。

摘要:

Myerson 和 Satterthwaite (1983) 提出的一个著名的 impossibility result 表明,任何最大化社会福利的双边市场 truthful mechanism 都必然出现亏损,这导致有必要放松福利效率并使用 approximation mechanisms。此类机制通常广泛使用 Bayesian priors。在这项工作中,我们研究了一个在理论和实践中日益重要的问题:设计具有近优近似的机制需要多少先验信息?\n 我们的第一个贡献是一个更一般的 impossibility result,指出在没有任何先验信息的情况下不可能实现有意义的近似,扩展了 Myerson 和 Satterthwaite 的著名 impossibility result。\n 我们的第二个贡献是,从每个卖方分布中获取一个 single sample(每件商品一个数字),这可以说是最少的先验信息量,对于一大类双边市场是足够的。我们证明了对于 subadditive buyers 和 additive sellers,在不考虑计算因素的情况下,使用一个 single sample 所能获得的最佳近似的 matching upper and lower bounds。\n 我们的第三个贡献是设计了计算高效的 blackbox reductions,将任何 one-sided mechanism 转换为 two-sided mechanism,近似损失很小,且仅使用每个卖方的一个 single sample。在此过程中,我们的 blackbox-type mechanisms 本身也产生了几个有趣的正面结果,通常甚至超越了使用完整先验信息的 state of the art。

英文摘要:

A celebrated impossibility result by Myerson and Satterthwaite (1983) shows that any truthful mechanism for two-sided markets that maximizes social welfare must run a deficit, resulting in a necessity to relax welfare efficiency and the use of approximation mechanisms. Such mechanisms in general make extensive use of the Bayesian priors. In this work, we investigate a question of increasing theoretical and practical importance: how much prior information is required to design mechanisms with near-optimal approximations? Our first contribution is a more general impossibility result stating that no meaningful approximation is possible without any prior information, expanding the famous impossibility result of Myerson and Satterthwaite. Our second contribution is that one {\em single sample} (one number per item), arguably a minimum-possible amount of prior information, from each seller distribution is sufficient for a large class of two-sided markets. We prove matching upper and lower bounds on the best approximation that can be obtained with one single sample for subadditive buyers and additive sellers, regardless of computational considerations. Our third contribution is the design of computationally efficient blackbox reductions that turn any one-sided mechanism into a two-sided mechanism with a small loss in the approximation, while using only one single sample from each seller. On the way, our blackbox-type mechanisms deliver several interesting positive results in their own right, often beating even the state of the art that uses full prior information.

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。

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