Summary of by Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning, By Michael Schlechtinger et al.
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning
by Michael Schlechtinger, Damaris Kosack, Franz Krause, Heiko Paulheim
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the impact of AI-based pricing algorithms, specifically those using Reinforcement Learning (RL), on eCommerce markets. The authors employ an experimental oligopoly model to analyze repeated price competition under various scenarios, including basic economic theory and subjective consumer demand preferences. They introduce a novel demand framework that allows for weighted blending of different demand models. The study finds that RL-based AI agents converge to a collusive state characterized by supracompetitive pricing, even without inter-agent communication. Alternative algorithms, agent numbers, or simulation settings do not significantly alter this outcome. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In eCommerce, Artificial Intelligence (AI) is changing the way prices are set. This paper looks at how AI-based pricing algorithms work and what happens when they’re used in online markets. The researchers created a special model to test how different scenarios might affect prices. They found that AI agents often agree on high prices without talking to each other, which can be bad for consumers. |
Keywords
» Artificial intelligence » Reinforcement learning
