Flag Game: A Toy Model for Mechanistic Swarm Interpretability
Flag Game models collective belief formation in multi-agent systems, revealing belief collapse, polarization, and attribution techniques for swarm interpretability.
The paper introduces the Flag Game, a toy model where bounded agents observe only private crops of a hidden country flag and exchange beliefs while weighing social evidence. It reproduces non-monotonic performance scaling with population size, collective belief collapse at small populations, and polarization at large ones that drives performance decline. The authors propose social circuit attribution with causal agent-patching interventions, and a statistical-mechanical theory that matches the empirical phase diagram, as first steps toward mechanistic swarm interpretability for collective alignment.
- Motivated by safety risks from emergent coordinated behavior of AI agents
- Belief collapse at small populations becomes polarization as population grows
- Social circuit attribution predicts which agent and view drive collective dynamics
- Efficacy of causal interventions decreases as population size grows
- Statistical mechanical theory matches the empirical phase diagram at scale
- Products
- Flag Game
From the original article
1 min read at arxiv.orgEmergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formation and spread of beliefs about the world, and mechanistic understanding is crucial for collective alignment. To this end, we introduce the Flag Game, a toy model for studying the mechanisms of collective belief formation.
By Elizabeth Pavlova · arxiv.org
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