对抗性双边贸易的α-后悔分析

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yossi Azar , Amos Fiat , Federico Fusco
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引用次数: 0

摘要

我们研究的是卖方和买方估值完全任意(即由对手决定)的连续双边贸易。卖方和买方都是战略代理人,对商品有私人估值,我们的目标是设计一种机制,在激励相容、个体理性和预算平衡的前提下实现效率(或贸易收益)最大化。在本文中,我们考虑的是比社会福利更难近似的贸易收益。我们考虑了各种反馈情况,并区分了机制只公布一个价格和机制可以为买卖双方公布不同价格的情况。我们展示了几种令人惊讶的不同情况下的分离结果。我们特别指出:(a) 对于任何 α<2 都不可能实现亚线性 α-regret ;(b) 但在完全反馈的情况下,亚线性 2-regret 是可以实现的;(c) 在单一价格和部分反馈的情况下,对于任何常数 α,都不可能获得亚线性 α-regret ;(d) 然而,即使是在一位反馈的情况下,公布两个价格也能实现亚线性 2-regret ;(e) 完全反馈和部分反馈之间的 2-regret 边界是可以证明的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An α-regret analysis of adversarial bilateral trade
We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (i.e., determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a mechanism that maximizes efficiency (or gain from trade) while being incentive compatible, individually rational and budget balanced. In this paper we consider gain from trade, which is harder to approximate than social welfare.
We consider a variety of feedback scenarios and distinguish the cases where the mechanism posts one price and when it can post different prices for buyer and seller. We show several surprising results about the separation between the different scenarios. In particular we show that (a) it is impossible to achieve sublinear α-regret for any α<2, (b) but with full feedback sublinear 2-regret is achievable; (c) with a single price and partial feedback one cannot get sublinear α regret for any constant α (d) nevertheless, posting two prices even with one-bit feedback achieves sublinear 2-regret, and (e) there is a provable separation in the 2-regret bounds between full and partial feedback.
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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