A Voting Aggregation Algorithm for Optimal Social Satisfaction

Abstract

Multi-agent System is a hot topic of Artificial Intelligence, and it is extensively used to complete some tasks among different agents. While voting is often used for this purpose because it aggregates individual preferences into a collective decision. However, the winner determination problem has seriously hindered the development of the voting theory, then we innovatively introduce the concept of “satisfaction degree” to solve the problem. In this paper, we propose a formula for agents to express satisfaction degree of candidates, which we call Social Satisfaction Degree (SSD). To find the winners from candidates, we then design Single-winner Determination Algorithm (SWDA) and Multi-winner Determination Algorithm (MWDA) for single-winner and multi-winner based on SSD, respectively. The empirical results from the PrefLib data set show that our new algorithms can produce the winner set with optimal SSD more accurately than other voting rules.