Work

Algorithmic Collusion by Large Language Models

Year: 2024

Type: preprint

Abstract: We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). In oligopoly settings, LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits. Variation in seemingly innocuous phrases in LLM instructions ("prompts") substantially influence the degree of supracompetitive pricing. We develop novel techniques for behavioral analysis of LLMs and use them to uncover price-war concerns as a contributing factor. Our results extend to auction settings. Our findings uncover unique challenges to any future regulation of LLM-based pricing agents, and AI-based pricing agents more broadly.

Source: RePEc: Research Papers in Economics

DOI: 10.48550/arxiv.2404.00806

Authors: Fish, Sara, Gonczarowski, Yannai A., Shorrer, Ran I.

Institutions: Pennsylvania State University, University of Miami, University of Manchester, Lancaster University

Language: English

Topic: Merger and Competition Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

Funders: National Science Foundation, United States-Israel Binational Science Foundation

Awards: NSF-BSF: Mechanism Design for All, G8504932637

OpenAlex ID: W4393724845

Cites:

Cited by: 10

Related to: 10