Multi-Agent LLMs

LLM: Large Language Model

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22 papers in the last four weeks, up 144% on the four weeks before. 0.2% of all new papers.

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Algorithmic Collusion by Large Language Models
Mar 31, 2024Sara Fish, Yannai A. Gonczarowski, Ran I. ShorrerPrompt Sensitivity Algorithmic Collusion
†School of Engineering and Applied Sciences, Harvard University · ‡Department of Economics and Department of Computer Science, Harvard University · §Department of Economics, Penn State University
A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design
Date pendingWanting Wang, Xiye Ma, Yuyang He +1Large Language Model-Guided Optimization Multi-Agent LLMs
College of Civil Engineering, Hunan University, Changsha, 410082, China

Mar 31, 2024, econ.GNJ/K move · Enter open · S save

Algorithmic Collusion by Large Language Models

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

†School of Engineering and Applied Sciences, Harvard University · ‡Department of Economics and Department of Computer Science, Harvard University · §Department of Economics, Penn State University

Prompt Sensitivity Algorithmic Collusion Dynamic Pricing Multi-Agent LLMs

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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.

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Latest papers 212

Algorithmic Collusion by Large Language Models

A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design