Artificial Swarms find Social Optima : (Late Breaking Report)

摘要

In the natural world, many social species amplify their collective intelligence by forming real-time closed-loop systems. Referred to as Swarm Intelligence (SI), this phenomenon has been rigorously studied in schools of fish, flocks of birds, and swarms of bees. In recent years, technology has enabled human groups to form real-time closed-loop systems modeled after natural swarms and moderated by AI algorithms. Referred to as Artificial Swarm Intelligence (ASI), these methods have been shown to enable human groups to reach optimized decisions. The present research explores this further, testing if ASI enables groups with conflicting views to converge on socially optimal solutions. Results showed that “swarming” was significantly more effective at enabling groups to converge on the Social Optima than three common voting methods: (i) Plurality voting (i) Borda Count and (iii) Condorcet pairwise voting. While traditional voting methods converged on socially optimal solutions with 60% success across a test set of 100 questions, the ASI system converged on socially optimal solutions with 82% success (p<;0.001).