Formal verification and synthesis of mechanisms for social choice

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-12-10 DOI:10.1016/j.artint.2024.104272
Munyque Mittelmann, Bastien Maubert, Aniello Murano, Laurent Perrussel
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Abstract

Mechanism Design (MD) aims at defining resources allocation protocols that satisfy a predefined set of properties, and Auction Mechanisms are of foremost importance. Core properties of mechanisms, such as strategy-proofness or budget balance, involve: (i) complex strategic concepts such as Nash equilibria, (ii) quantitative aspects such as utilities, and often (iii) imperfect information, with agents' private valuations. We demonstrate that Strategy Logic provides a formal framework fit to model mechanisms and express such properties, and we show that it can be used either to automatically check that a given mechanism satisfies some property (verification), or automatically produce a mechanism that does (synthesis). To do so, we consider a quantitative and variant of Strategy Logic. We first show how to express the implementation of social choice functions. Second, we show how fundamental mechanism properties can be expressed as logical formulas, and thus evaluated by model checking. We then prove that model checking for this particular variant of Strategy Logic can be done in polynomial space. Next, we show how MD can be rephrased as a synthesis problem, where mechanisms are automatically synthesized from a partial or complete logical specification. We solve the automated synthesis of mechanisms in two cases: when the number of actions is bounded, and when agents play in turns. Finally, we provide examples of auction design based for each of these two cases. The benefit of our approach in relation to classical MD is to provide a general framework for addressing a large spectrum of MD problems, which is not tailored to a particular setting or problem.
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机制设计(MD)旨在定义满足一系列预定属性的资源分配协议,其中拍卖机制最为重要。机制的核心属性,如策略防错或预算平衡,涉及:(i) 复杂的策略概念,如纳什均衡;(ii) 定量方面,如效用;(iii) 不完全信息,包括代理人的私人估值。我们证明,策略逻辑提供了一个正式的框架,适合对机制进行建模并表达此类属性,我们还证明,策略逻辑可用于自动检查给定机制是否满足某些属性(验证),或自动生成满足某些属性的机制(合成)。为此,我们考虑了策略逻辑的定量和变体。我们首先展示如何表达社会选择功能的实现。其次,我们展示了如何将基本机制属性表达为逻辑公式,并通过模型检查进行评估。然后,我们证明这种特定的策略逻辑变体的模型检查可以在多项式空间内完成。接下来,我们展示了如何将 MD 重新表述为一个合成问题,即从部分或完整的逻辑规范自动合成机制。我们解决了两种情况下的机制自动合成问题:当行动数量有限制时,以及当代理轮流参与游戏时。最后,我们分别提供了基于这两种情况的拍卖设计实例。与经典 MD 相比,我们的方法的优势在于为解决大量 MD 问题提供了一个通用框架,而不是针对特定环境或问题量身定制的。
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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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