Multi-stage generalized deferred acceptance mechanism: Strategyproof mechanism for handling general hereditary constraints

IF 2.6 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Kei Kimura, Kweiguu Liu, Zhaohong Sun, Kentaro Yahiro, Makoto Yokoo
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引用次数: 0

Abstract

The theory of two-sided matching has been extensively developed and applied to many real-life application domains. As the theory has been applied to increasingly diverse types of environments, researchers and practitioners have encountered various forms of distributional constraints. Arguably, the most general class of distributional constraints would be hereditary constraints; if a matching is feasible, then any matching that assigns weakly fewer students at each college is also feasible. However, under general hereditary constraints, it is shown that no strategyproof mechanism exists that simultaneously satisfies fairness and weak nonwastefulness, which is an efficiency (students’ welfare) requirement weaker than nonwastefulness. We propose a new strategyproof mechanism that works for hereditary constraints called the Multi-Stage Generalized Deferred Acceptance mechanism (MS-GDA). It uses the Generalized Deferred Acceptance mechanism (GDA) as a subroutine, which works when distributional constraints belong to a well-behaved class called hereditary M\(^{\natural }\)-convex set. We show that GDA satisfies several desirable properties, most of which are also preserved in MS-GDA. We experimentally show that MS-GDA strikes a good balance between fairness and efficiency (students’ welfare) compared to existing strategyproof mechanisms when distributional constraints are close to an M\(^{\natural }\)-convex set*.

多阶段广义延迟接受机制:处理一般遗传约束的防策略机制
双面匹配理论得到了广泛的发展,并应用于许多实际应用领域。随着该理论应用于日益多样化的环境类型,研究人员和实践者遇到了各种形式的分布约束。可以说,最普遍的一类分布约束是遗传约束;如果匹配是可行的,那么任何在每个学院分配的学生数量较弱的匹配也是可行的。然而,在一般遗传约束下,不存在同时满足公平和弱不浪费的策略证明机制,这是一种效率(学生福利)需求,弱于不浪费。我们提出了一种新的策略证明机制,适用于遗传约束,称为多阶段广义延迟接受机制(MS-GDA)。它使用广义延迟接受机制(GDA)作为子例程,当分布约束属于一个称为遗传M \(^{\natural }\) -凸集的行为良好的类时,该机制起作用。我们证明GDA满足几个理想的性质,其中大部分也保留在MS-GDA中。我们的实验表明,当分布约束接近M \(^{\natural }\) -凸集*时,与现有的策略证明机制相比,MS-GDA在公平和效率(学生福利)之间取得了很好的平衡。
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来源期刊
Autonomous Agents and Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems 工程技术-计算机:人工智能
CiteScore
6.00
自引率
5.30%
发文量
48
审稿时长
>12 weeks
期刊介绍: This is the official journal of the International Foundation for Autonomous Agents and Multi-Agent Systems. It provides a leading forum for disseminating significant original research results in the foundations, theory, development, analysis, and applications of autonomous agents and multi-agent systems. Coverage in Autonomous Agents and Multi-Agent Systems includes, but is not limited to: Agent decision-making architectures and their evaluation, including: cognitive models; knowledge representation; logics for agency; ontological reasoning; planning (single and multi-agent); reasoning (single and multi-agent) Cooperation and teamwork, including: distributed problem solving; human-robot/agent interaction; multi-user/multi-virtual-agent interaction; coalition formation; coordination Agent communication languages, including: their semantics, pragmatics, and implementation; agent communication protocols and conversations; agent commitments; speech act theory Ontologies for agent systems, agents and the semantic web, agents and semantic web services, Grid-based systems, and service-oriented computing Agent societies and societal issues, including: artificial social systems; environments, organizations and institutions; ethical and legal issues; privacy, safety and security; trust, reliability and reputation Agent-based system development, including: agent development techniques, tools and environments; agent programming languages; agent specification or validation languages Agent-based simulation, including: emergent behavior; participatory simulation; simulation techniques, tools and environments; social simulation Agreement technologies, including: argumentation; collective decision making; judgment aggregation and belief merging; negotiation; norms Economic paradigms, including: auction and mechanism design; bargaining and negotiation; economically-motivated agents; game theory (cooperative and non-cooperative); social choice and voting Learning agents, including: computational architectures for learning agents; evolution, adaptation; multi-agent learning. Robotic agents, including: integrated perception, cognition, and action; cognitive robotics; robot planning (including action and motion planning); multi-robot systems. Virtual agents, including: agents in games and virtual environments; companion and coaching agents; modeling personality, emotions; multimodal interaction; verbal and non-verbal expressiveness Significant, novel applications of agent technology Comprehensive reviews and authoritative tutorials of research and practice in agent systems Comprehensive and authoritative reviews of books dealing with agents and multi-agent systems.
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