Artificial Intelligence, Communication, and Strategic Pricing: Evidence from Large Language Models

Abstract

We analyze the behavior of large language model (LLM) agents as strategic actors in a price-setting environment. In an experimental market based on a repeated Bertrand pricing game, we find that LLM agents generate coordinated pricing outcomes. Allowing agents to communicate increases prices and profits while reducing variation in both outcomes. Moreover, communication increases the likelihood that agents coordinate on identical prices. Analyzing communication patterns shows that more frequent, longer, and more explicit exchanges reinforce these outcomes. A detailed analysis of agents' decision rationales suggests that communication affects pricing less through explicit game-theoretic reasoning than through reinforcement by cooperative language and pattern completion. Further tests involving different LLM temperature settings, varying the number of interacting agents, and various LLM architectures show that the results hold across these conditions. This consistency can be attributed mainly to language adjustments across different conditions. Overall, these findings highlight how language-based artificial intelligence systems can generate coordinated market outcomes even without specialized training in pricing or strategic interaction. 

Keywords: Artificial Intelligence, Large Language Models, Pricing Decisions, Communication, Coordination, Collusion

JEL Classification: L13, L41, C91, D83