{"title":"关于排名的偏好和奖励政策","authors":"Marco Faella, Luigi Sauro","doi":"10.1007/s10458-024-09656-7","DOIUrl":null,"url":null,"abstract":"<div><p>We study the rational preferences of agents participating in a mechanism whose outcome is a ranking (i.e., a weak order) among participants. We propose a set of self-interest axioms corresponding to different ways for participants to compare rankings. These axioms vary from minimal conditions that most participants can be expected to agree on, to more demanding requirements that apply to specific scenarios. Then, we analyze the theories that can be obtained by combining the previous axioms and characterize their mutual relationships, revealing a rich hierarchical structure. After this broad investigation on preferences over rankings, we consider the case where the mechanism can distribute a fixed monetary reward to the participants in a fair way (that is, depending only on the anonymized output ranking). We show that such mechanisms can induce specific classes of preferences by suitably choosing the assigned rewards, even in the absence of tie breaking.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09656-7.pdf","citationCount":"0","resultStr":"{\"title\":\"On preferences and reward policies over rankings\",\"authors\":\"Marco Faella, Luigi Sauro\",\"doi\":\"10.1007/s10458-024-09656-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We study the rational preferences of agents participating in a mechanism whose outcome is a ranking (i.e., a weak order) among participants. We propose a set of self-interest axioms corresponding to different ways for participants to compare rankings. These axioms vary from minimal conditions that most participants can be expected to agree on, to more demanding requirements that apply to specific scenarios. Then, we analyze the theories that can be obtained by combining the previous axioms and characterize their mutual relationships, revealing a rich hierarchical structure. After this broad investigation on preferences over rankings, we consider the case where the mechanism can distribute a fixed monetary reward to the participants in a fair way (that is, depending only on the anonymized output ranking). We show that such mechanisms can induce specific classes of preferences by suitably choosing the assigned rewards, even in the absence of tie breaking.</p></div>\",\"PeriodicalId\":55586,\"journal\":{\"name\":\"Autonomous Agents and Multi-Agent Systems\",\"volume\":\"38 2\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10458-024-09656-7.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Autonomous Agents and Multi-Agent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10458-024-09656-7\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autonomous Agents and Multi-Agent Systems","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10458-024-09656-7","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
We study the rational preferences of agents participating in a mechanism whose outcome is a ranking (i.e., a weak order) among participants. We propose a set of self-interest axioms corresponding to different ways for participants to compare rankings. These axioms vary from minimal conditions that most participants can be expected to agree on, to more demanding requirements that apply to specific scenarios. Then, we analyze the theories that can be obtained by combining the previous axioms and characterize their mutual relationships, revealing a rich hierarchical structure. After this broad investigation on preferences over rankings, we consider the case where the mechanism can distribute a fixed monetary reward to the participants in a fair way (that is, depending only on the anonymized output ranking). We show that such mechanisms can induce specific classes of preferences by suitably choosing the assigned rewards, even in the absence of tie breaking.
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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