🤖 AI Summary
This study investigates how large language model–driven AI traders form expectations and influence asset market bubbles. By constructing a multi-agent simulation system within an open-outcry market environment, the research simulates trading behaviors and, for the first time, leverages interpretable textual reasoning to uncover the underlying decision-making mechanisms of AI agents. Through prompt engineering, the study implements causal interventions targeting specific behavioral biases. The framework successfully replicates established experimental market phenomena—such as the predictive power of excess demand on price dynamics and the positive correlation between belief dispersion and trading volume—and demonstrates effective control over bubble intensity. These findings offer a novel methodological approach and empirical evidence for understanding market dynamics in settings where AI agents actively participate.
📝 Abstract
We study how AI agents form expectations and trade in experimental asset markets. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we document three main findings. First, AI agents exhibit classic behavioral patterns: a pronounced disposition effect and recency-weighted extrapolative beliefs. Second, these individual-level patterns aggregate into equilibrium dynamics that replicate classic experimental findings (Smith et al., 1988), including the predictive power of excess demand for future prices and the positive relationship between disagreement and trading volume. Third, by analyzing the agents' reasoning text through a twenty-mechanism scoring framework, we show that targeted prompt interventions causally amplify or suppress specific behavioral mechanisms, significantly altering the magnitude of market bubbles.