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This paper proposes "synthetic counteradaptation" as a framework for understanding the recursive co-evolution between human strategies and AI behaviors in multi-agent environments. By analyzing cases ranging from Go to geopolitical simulations, it highlights how novel AI protocols can drive significant shifts in human decision-making dynamics. The work offers critical insights into the long-term societal implications of increasingly autonomous agent interactions rather than focusing on immediate product capabilities or hardware specs.
Synthetic Counteradaptation: A Principle of Human-AI Co-evolution
arXiv:2606.15503v1 Announce Type: new Abstract: In this paper, we introduce the concept of synthetic counteradaptation, a process where human and AI systems co-evolve by adapting to each other's strategies and behaviors. Synthetic counteradaptation occurs when AI systems develop novel strategies or social protocols, prompting humans to extract insights and adapt their own behaviors in response, leading to the emergence of new agent interaction dynamics. To illustrate these dynamics, we analyze examples from various contexts, including the game of Go, mixed-motive social interactions, and geopolitical simulations. By exploring these cases, we demonstrate how synthetic counteradaptation provides a framework for understanding the recursive and co-evolutionary nature of human-AI interactions in multi-agent environments.
Abstract:In this paper, we introduce the concept of synthetic counteradaptation, a process where human and AI systems co-evolve by adapting to each other's strategies and behaviors. Synthetic counteradaptation occurs when AI systems develop novel strategies or social protocols, prompting humans to extract insights and adapt their own behaviors in response, leading to the emergence of new agent interaction dynamics. To illustrate these dynamics, we analyze examples from various contexts, including the game of Go, mixed-motive social interactions, and geopolitical simulations. By exploring these cases, we demonstrate how synthetic counteradaptation provides a framework for understanding the recursive and co-evolutionary nature of human-AI interactions in multi-agent environments.
15 pages, 1 figure. Published in Antikythera (MIT Press), February 2025
Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Multiagent Systems (cs.MA); Neural and Evolutionary Computing (cs.NE)
arXiv:2606.15503 [cs.AI]
(or arXiv:2606.15503v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.15503 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1162/ANTI.5CZJ Focus to learn more DOI(s) linking to related resources
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