arXiv:2606.26139v1 Announce Type: cross Abstract: This paper develops a multiscale model of coalition formation in which strategic exit-and-join decisions are coupled with tactical consensus dynamics inside coalitions. Coalition value is generated endogenously from within-coalition information aggregation, while Aumann-Dreze payoffs, switching frictions, and acceptance rules govern strategic reconfiguration. The framework introduces a fast-slow architecture in which transferable coalition value emerges from DeGroot-style consensus processes, while coalition structures evolve through incentive-
This research is emerging as AI agent systems become more sophisticated, requiring advanced models for complex, multi-agent interactions and coalition formation.
Understanding the dynamics of self-organizing intelligent systems is crucial for designing and governing future AI ecosystems, influencing everything from economic models to geopolitical strategy.
This framework offers a more nuanced way to model AI interactions, moving beyond simple game theory to incorporate dynamic consensus and strategic reconfigurations within AI agent groups.
- · AI ethicists
- · Multi-agent system developers
- · Game theorists
- · AI governance researchers
- · Overly simplistic AI interaction models
Improved theoretical understanding of how AI agents might form and dissolve alliances based on internal consensus and external incentives.
Development of more robust and adaptive multi-agent AI systems capable of strategic cooperation and competition.
Potential for AI systems to autonomously form and reconfigure organizational structures, impacting human-led decision-making processes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI