Incentivized Symbiosis: A Paradigm for Human-Agent Coevolution
This addresses the challenge of enabling cooperative human-agent coevolution in integrated systems, but it is incremental as it builds on existing concepts like contract theory and Web3 without introducing a fundamentally new method.
The paper tackles the problem of fostering cooperation between humans and AI agents by proposing Incentivized Symbiosis, a social contract paradigm inspired by Web3 and blockchain technology to define and enforce rules and incentives, aiming to catalyze new research at the intersection of AI, Web3, and society.
Cooperation is vital to our survival and progress. Evolutionary game theory offers a lens to understand the structures and incentives that enable cooperation to be a successful strategy. As artificial intelligence agents become integral to human systems, the dynamics of cooperation take on unprecedented significance. The convergence of human-agent teaming, contract theory, and decentralized frameworks like Web3, grounded in transparency, accountability, and trust, offers a foundation for fostering cooperation by establishing enforceable rules and incentives for humans and AI agents. We conceptualize Incentivized Symbiosis as a social contract between humans and AI, inspired by Web3 principles and encoded in blockchain technology, to define and enforce rules, incentives, and consequences for both parties. By exploring this paradigm, we aim to catalyze new research at the intersection of systems thinking in AI, Web3, and society, fostering innovative pathways for cooperative human-agent coevolution.