Information Aggregation with AI Agents
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 in environments where more than two levels of interactive reasoning are required, a ceiling close to the one documented in human subjects. Consistent with o...
| Authors | Spyros Galanis |
|---|---|
| Primary category | econ.GN |
| Published | 21 April 2026 |
| Last updated at source | 25 September 2026 |
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