Summary of Tacit Algorithmic Collusion in Deep Reinforcement Learning Guided Price Competition: a Study Using Ev Charge Pricing Game, by Diwas Paudel and Tapas K. Das
Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game
by Diwas Paudel, Tapas K. Das
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); General Economics (econ.GN); Systems and Control (eess.SY)
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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 examines the concern for tacit algorithmic collusion among EV charging hubs that use AI-aided learning algorithms to make pricing decisions. The authors develop a two-step data-driven methodology to analyze the pricing strategies of these hubs, which source power from day-ahead and real-time electricity markets as well as in-house battery storage systems. Using a competitive Markov decision process model with a multi-agent deep reinforcement learning (MADRL) framework, the authors evaluate the resulting pricing strategies and find that they exhibit a low to moderate level of collusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how artificial intelligence helps companies that charge electric vehicles decide on prices to make more money. They’re worried that AI might help these companies work together in secret to keep prices high. The authors test this idea by looking at how different companies would price their charging services if they used AI to figure out what to do. They find that the companies’ pricing strategies show some level of cooperation, but it’s not very strong. |
Keywords
* Artificial intelligence * Reinforcement learning
