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1st Speaker: Sara Fish (Harvard)
Title: Algorithmic Collusion by Large Language Models
Abstract: The rise of algorithmic pricing raises concerns that algorithms might collude to maximize firm profits, to the detriment of consumers. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs), and specifically on OpenAI's GPT-4. We find that (1) LLM-based pricing agents are adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in oligopoly settings, and (3) variation in seemingly innocuous phrases in LLM instructions ("prompts") may lead to substantially higher levels of collusion. Our findings underscore the need for antitrust regulation regarding algorithmic pricing, and uncover novel regulatory challenges unique to LLM-based pricing agents.
2nd Speaker: Safwan Hossain (Harvard)
Title: Equilibrium of Data Markets with Externality
Abstract: We model real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers, as a simultaneous game. A key component here is the negative externality buyers induce on one another due to data purchases. Starting with a simple setting where buyers know their valuations a priori, we characterize both the existence and welfare properties of the pure Nash equilibrium in the presence of such externality. While the outcomes are bleak without any intervention, mirroring the limitations of current data markets, we prove that for a standard class of externality functions, platforms intervening through a transaction cost can lead to a pure equilibrium with strong welfare guarantees. We next consider a more realistic setting where buyers learn their valuations over time through market interactions. Our intervention is feasible here as well, and we consider learning algorithms to achieve low regret concerning both individual and cumulative utility metrics. Lastly, we analyze the promises of this intervention under a much richer externality model.