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Firm competition and collusion involve complex dynamics, particularly whenconsidering communication among firms. Such issues can be modeled as problemsof complex systems, traditionally approached through experiments involvinghuman subjects or agent-based modeling methods. We propose an innovativeframework called Smart Agent-Based Modeling (SABM), wherein smart agents,supported by GPT-4 technologies, represent firms, and interact with oneanother. We conducted a controlled experiment to study firm price competitionand collusion behaviors under various conditions. SABM is more cost-effectiveand flexible compared to conducting experiments with human subjects. Smartagents possess an extensive knowledge base for decision-making and exhibithuman-like strategic abilities, surpassing traditional ABM agents. Furthermore,smart agents can simulate human conversation and be personalized, making themideal for studying complex situations involving communication. Our resultsdemonstrate that, in the absence of communication, smart agents consistentlyreach tacit collusion, leading to prices converging at levels higher than theBertrand equilibrium price but lower than monopoly or cartel prices. Whencommunication is allowed, smart agents achieve a higher-level collusion withprices close to cartel prices. Collusion forms more quickly with communication,while price convergence is smoother without it. These results indicate thatcommunication enhances trust between firms, encouraging frequent small pricedeviations to explore opportunities for a higher-level win-win situation andreducing the likelihood of triggering a price war. We also assigned differentpersonas to firms to analyze behavioral differences and tested variant modelsunder diverse market structures. The findings showcase the effectiveness androbustness of SABM and provide intriguing insights into competition andcollusion.

"Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion