arXiv:2606.13038v1 Announce Type: new Abstract: As LLM agents proliferate in prediction markets and collective decision-making, they risk a cognitive monoculture: agents built on shared foundation models produce correlated forecasts, and recent measurement finds frontier-model errors correlated at r ~ 0.77. We ask whether human cognitive diversity can be recovered from behavior and transferred to LLM agents. Nous extracts a structured eight-dimension behavioral profile from real Polymarket trading activity and injects it into agents through prompts. Our central finding is a dissociation betwee
As LLM agents become increasingly prevalent in prediction markets, the risk of cognitive monoculture due to shared foundation models is becoming a critical concern.
This research addresses a fundamental vulnerability in collective AI decision-making by seeking to reintroduce human cognitive diversity, which could prevent systemic biases and improve forecast accuracy.
The ability to extract human behavioral profiles and inject them into AI agents offers a path to more robust and diverse AI-driven prediction systems, reducing correlation errors.
- · AI safety researchers
- · Prediction market operators
- · AI agent developers
- · High-stakes decision-making industries
- · Monolithic AI models
- · Undifferentiated AI agents
AI agents in prediction markets will exhibit more diverse and less correlated forecast behaviors.
Improved predictive accuracy and resilience in markets and decision-making processes utilizing these diversified AI agents.
New standards and regulations may emerge for 'cognitive diversity' in deployed AI systems, particularly in sensitive sectors.
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Read at arXiv cs.AI