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arXiv 1702.07032cs.GTcs.CCcs.DS
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可加性购买者简单最优确定性机制的复杂性

On the Complexity of Simple and Optimal Deterministic Mechanisms for an Additive Buyer

Xi Chen, George Matikas, Dimitris Paparas, Mihalis Yannakakis

摘要:

我们证明了单个可加买方的收入最优确定性机制设计问题是P-困难的,即使分布对每个项目的支持度为2,更重要的是,即使最优解是一个非常简单的类型:卖方为每个单独的项目选择一个价格,为所有项目的大捆绑选择一个价格;购买者可以以其给定价格购买大捆绑或以其总的单独价格购买项目的任何子集。下面的问题也是#P-hard的,作为证明的直接推论:1。确定单个物品定价对于给定实例是否是最优的,2.确定大捆绑定价是否是最优的,以及3.计算最优(确定性)收益。在积极的一面,我们表明,当分布是独立同分布。对于支持大小为2的情况,可以通过上述类型的简单解决方案(具有用于大捆绑的折扣价格的单个项目定价)来实现通过任何机制(甚至是随机机制)可获得的最优收益,并且此外,可以在多项式时间内计算该最优收益。当项目数为常数时,该问题也可以在多项式时间内求解。

英文摘要:

We show that the Revenue-Optimal Deterministic Mechanism Design problem for a single additive buyer is #P-hard, even when the distributions have support size 2 for each item and, more importantly, even when the optimal solution is guaranteed to be of a very simple kind: the seller picks a price for each individual item and a price for the grand bundle of all the items; the buyer can purchase either the grand bundle at its given price or any subset of items at their total individual prices. The following problems are also #P-hard, as immediate corollaries of the proof: 1. determining if individual item pricing is optimal for a given instance, 2. determining if grand bundle pricing is optimal, and 3. computing the optimal (deterministic) revenue. On the positive side, we show that when the distributions are i.i.d. with support size 2, the optimal revenue obtainable by any mechanism, even a randomized one, can be achieved by a simple solution of the above kind (individual item pricing with a discounted price for the grand bundle) and furthermore, it can be computed in polynomial time. The problem can be solved in polynomial time too when the number of items is constant.

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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