Researchers simulated a simple exchange populated entirely by autonomous large language model (LLM) agents to study how AI forms price expectations and trades. They report three main findings: the AI agents show well-known human behavioral patterns, these individual behaviors sum up to familiar market dynamics, and carefully written prompt changes can increase or decrease the size of asset-price bubbles.

The experiment used a controlled open-call auction with 20 main trading periods. Each agent started with 100 units of cash and 4 shares of a risky asset. Cash paid a fixed 5% interest per period. The risky asset paid a dividend that was either 0.4 or 1.0 with equal chance, so the expected dividend was 0.7. The designers also included a terminal buyout that kept the asset’s fundamental value constant at 14 units (the expected dividend divided by the 5% interest). The setup made it easier to identify when prices deviated from rational value.

At the individual level, the LLM agents reproduced two classic biases. They displayed a disposition effect — selling assets that had gone up in price while holding on to losers — and they formed recency‑weighted extrapolative beliefs, meaning recent price moves had outsize influence on their forecasts of future prices. Unlike many human studies that find a weak link between stated beliefs and actual trades, these AI agents translated their stated expectations much more directly into trading decisions, because they were not constrained by human frictions like attention lapses or transaction costs.

When many biased agents traded together, market outcomes matched classic experimental results. Measures of excess demand predicted future price changes, and higher disagreement across agents’ beliefs was associated with more trading volume. The researchers could read the agents’ internal reasoning text and found that, during bubble episodes, agents explicitly described speculative strategies such as momentum chasing and “riding the bubble.” This link between text and action helped connect cognitive mechanisms to price dynamics.