二进制竞赛中的竞赛分区

IF 2.6 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Priel Levy, Yonatan Aumann, David Sarne
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引用次数: 0

摘要

在这项工作中,我们探讨了将竞赛中的参赛者划分为不同小组所带来的机遇,每个小组都在独立的竞赛中竞争,并获得各自的奖项。这与大多数关于竞赛设计的文献不同,后者侧重于单一 "盛大"(可能是多阶段)竞赛的设置,其中所有潜在参赛者最终角逐相同的奖项,但也有少数文献例外地考虑了竞赛分区,但在使用最佳结构方面却出现了相互冲突的偏好结果。我们将重点放在二元竞赛上,在这种竞赛中,参赛者提交的参赛作品的质量是内生决定的,我们的研究表明,在某些条件下,竞赛分区确实是有益的,例如,当参赛者人数或奖金数额 "足够大 "时,确切的规模要求是分区成本的函数。当分区不需要任何成本时,我们证明它是一种支配或弱支配策略,这取决于确定组织者预期收益的方式。我们还进一步扩展了分析范围,考虑了某些子竞赛只包含一名参赛者(单人)的分区情况。我们的结论是,竞赛分区是竞赛设计者在追求利益最大化时可以而且应该考虑的一个途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Contest partitioning in binary contests

Contest partitioning in binary contests

In this work we explore the opportunities presented by partitioning contestants in contest into disjoint groups, each competing in an independent contest, with its own prize. This, as opposed to most literature on contest design, which focuses on the setting of a single “grand” (possibly multi-stage) contest, wherein all potential contestants ultimately compete for the same prize(s), with few exceptions that do consider contest partitioning, yet with conflicting preference results concerning the optimal structure to be used. Focusing on binary contests, wherein the quality of contestants’ submissions are endogenously determined, we show that contest partitioning is indeed beneficial under some condition, e.g., whenever the number of contestants, or the prize amount, are “sufficiently large”, where the exact size requirements are a function of the partitioning cost. When partitioning does not entail any cost, we show that it is either a dominating or weakly dominating strategy, depending on the way the organizer’s expected benefit is determined. The analysis is further extended to consider partitioning where some of the sub-contests used contain a single contestant (a singleton). We conclude that contest partitioning is an avenue that contest designers can and should consider, when aiming to maximize their profit.

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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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