Abstract (may include machine translation)
Due to the rising availability and adoption of Artificial Intelligence in e-commerce, many of the online-prices are not set by humans, but by algorithms. The consequence is an opaque pricing situation that raises the potential of concealed, unfair competition by means of collusion. To examine this phenomenon, we study deep-Reinforcement-learning-based pricing algorithms by conducting an experiment involving an oligopoly model of repeated price competition. Our market model facilitates a variable environment spanning from economic theory to more realistic consumer demand models. We find that the algorithms learn to enter a collusive state and charge supra-competitive prices, without explicitly communicating with one another, and even without seeing each other's prices.
Original language | English |
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Pages (from-to) | 2748-2750 |
Number of pages | 3 |
Journal | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
Volume | 2023-May |
State | Published - 2023 |
Externally published | Yes |
Event | 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom Duration: 29 May 2023 → 2 Jun 2023 |
Keywords
- Algorithmic Pricing
- Collusion
- Deep Reinforcement Learning