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arXiv 2007.15393cs.AI
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迈向一种新的社会选择理论

Towards a new Social Choice Theory

Independent Researcher, Barcelona, Spain

Andrés García-Camino

AI总结:

本文提出社会选择优化以推广两阶段赞成投票,并以Minimax规则最小化对社会目标的阻碍,同时倡导社会包容的开放标准化。

摘要:

社会选择是关于从个体意见、偏好、利益或福利出发,为实现社会福利而进行集体决策的理论。计算社会福利领域相对较新,并正在人工智能界产生越来越大的影响。经典文献采用单峰偏好假设,即偏好中存在一种顺序,并且在该顺序中存在一个全局最大值。今年,一些关于两阶段赞成投票系统(TAVs)、多胜者选择规则(MWSR)以及不完全偏好(IPs)和循环偏好(CPs)的理论结果得到发表。本文有三个目的:首先,我希望引入社会选择优化,作为TAVs的推广,其中包含一个最大化阶段和一个最小化阶段,从而实现Minimax这一著名的人工智能决策规则,以最小化对某个(社会)目标的阻碍。其次,我希望遵循我在博士论文中付诸实践的开放标准化与开放整合理论(仍处于完善过程中),引入社会包容的开放标准化,将其作为社会选择优化的全球社会目标。

英文摘要:

Social choice is the theory about collective decision towards social welfare starting from individual opinions, preferences, interests or welfare. The field of Computational Social Welfare is somewhat recent and it is gaining impact in the Artificial Intelligence Community. Classical literature makes the assumption of single-peaked preferences, i.e. there exist a order in the preferences and there is a global maximum in this order. This year some theoretical results were published about Two-stage Approval Voting Systems (TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular Preferences (CPs). The purpose of this paper is three-fold: Firstly, I want to introduced Social Choice Optimisation as a generalisation of TAVs where there is a max stage and a min stage implementing thus a Minimax, well-known Artificial Intelligence decision-making rule to minimize hindering towards a (Social) Goal. Secondly, I want to introduce, following my Open Standardization and Open Integration Theory (in refinement process) put in practice in my dissertation, the Open Standardization of Social Inclusion, as a global social goal of Social Choice Optimization.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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