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 our theoretical predictions, market accuracy does not improve from allowing cheap talk communication, changing the duration of the market, or strategic prompting; initial price has little average effect but matters in the very hard structure. We also find that ``smarter'' AI agents perform better at aggregation and are more profitable. Surprisingly, giving them feedback about past performance does not improve aggregation. A further wave of markets, run three months later with capability-frontier models, aggregates information more often in the three easier structures but not in the hardest one, where higher capability replaces markets that are confidently wrong with markets that hedge near 0.5.