Algorithmic Collusion And The Minimum Price Markov Game

2024-07-03

arXiv.org

AI Summary

This paper addresses fairness and regulatory challenges arising from algorithmic tacit collusion in online markets—particularly first-price auctions governed by a lowest-price rule. To formalize this setting, we propose the Minimum-Price Markov Game (MPMG), the first theoretical framework modeling multi-agent dynamic interactions under the lowest-price rule. Integrating game-theoretic modeling, multi-agent reinforcement learning, and mechanism design, our analysis reveals that the lowest-price rule inherently suppresses spontaneous collusion absent coordinated intent; collusion emergence is primarily driven by self-reinforcing dynamics rather than explicit communication or external coordination; and MPMG faithfully reproduces real-world first-price auction dynamics. These findings provide a verifiable theoretical foundation and quantitative evaluation tools for algorithmic oversight in critical domains such as public procurement.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Multiagent Systems: Mechanism Design
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper introduces the Minimum Price Markov Game (MPMG), a theoretical model that reasonably approximates real-world first-price markets following the minimum price rule, such as public auctions. The goal is to provide researchers and practitioners with a framework to study market fairness and regulation in both digitized and non-digitized public procurement processes, amid growing concerns about algorithmic collusion in online markets. Using multi-agent reinforcement learning-driven artificial agents, we demonstrate that (i) the MPMG is a reliable model for first-price market dynamics, (ii) the minimum price rule is generally resilient to non-engineered tacit coordination among rational actors, and (iii) when tacit coordination occurs, it relies heavily on self-reinforcing trends. These findings contribute to the ongoing debate about algorithmic pricing and its implications.

Problem

• Modeling first-price markets with minimum price rule
• Studying market fairness and regulation in procurement
• Investigating algorithmic collusion and tacit coordination

Innovation

• Minimum Price Markov Game model
• Multi-agent reinforcement learning agents
• Study of market fairness and regulation