A Reputation System for Artificial Societies
This addresses the challenge of consensus in hybrid collective intelligences, including humans and AI, which is incremental as it builds on existing reputation systems.
The paper tackles the problem of achieving reliable consensus in decentralized networks of AI agents by proposing a reputation-based consensus system with greater resistance to reputation gaming, providing initial practical results.
One approach to achieving artificial general intelligence (AGI) is through the emergence of complex structures and dynamic properties arising from decentralized networks of interacting artificial intelligence (AI) agents. Understanding the principles of consensus in societies and finding ways to make consensus more reliable becomes critically important as connectivity and interaction speed increase in modern distributed systems of hybrid collective intelligences, which include both humans and computer systems. We propose a new form of reputation-based consensus with greater resistance to reputation gaming than current systems have. We discuss options for its implementation, and provide initial practical results.