Towards a new Social Choice Theory

TL;DR

This paper introduces a new framework called Social Choice Optimization, generalizing existing voting systems, and emphasizes open standardization of social inclusion as a key social goal within AI decision-making.

Contribution

It proposes Social Choice Optimization as a generalization of Two-stage Approval Voting, integrating Minimax decision rules and promoting open standardization of social inclusion.

Findings

Introduced Social Choice Optimization as a new decision-making framework.

Connected social choice theory with AI decision rules like Minimax.

Emphasized the importance of open standardization for social inclusion.

Abstract

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.