Information Aggregation with AI Agents
Quick summary
arXiv:2604.20050v4 Announce Type: replace-cross Abstract: Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements? We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals, across four information structures of increasing complexity. We find that although the median market is effective at aggregating information in the easy information structures, performance deteriorates in the harder structures, suggesting that AI agents struggle i
Key takeaways
- arXiv:2604.20050v4 Announce Type: replace-cross Abstract: Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements?
- We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals, across four information structures of increasing complexity.
- We find that although the median market is effective at aggregating information in the easy information structures, performance deteriorates in the harder structures, suggesting that AI agents struggle i
Why it matters
The importance of “Information Aggregation with AI Agents” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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