Social Decision Making with Multi-Relational Networks and Grammar-Based Particle Swarms

Abstract

Social decision support systems are able to aggregate the local perspectives of a diverse group of individuals into a global social decision. This paper presents a multi-relational network ontology and grammar-based particle swarm algorithm capable of aggregating the decisions of millions of individuals. This framework supports a diverse problem space and a broad range of vote aggregation algorithms. These algorithms account for individual expertise and representation across different domains of the group problem space. Individuals are able to pose and categorize problems, generate potential solutions, choose trusted representatives, and vote for particular solutions. Ultimately, via a social decision making algorithm, the system aggregates all the individual votes into a single collective decision.

References in corpus (3)

• A Social Network for Societal-Scale Decision-Making Systems
• Simulating Network Influence Algorithms Using Particle-Swarms: PageRank and PageRank-Priors
• Advances towards a General-Purpose Societal-Scale Human-Collective Problem-Solving Engine

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• Edge Union of Networks on the Same Vertex Set
• Revisiting the Age of Enlightenment from a Collective Decision Making Systems Perspective