Algorithmic Collusion: Insights from Deep Learning
SSRN, ISSN: 1556-5068
2021
- 17Citations
- 3,385Usage
- 26Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Most Recent News
Pricing Algorithms Aren’t Colluding, Yet
Axel Gautier, Ashwin Ittoo, and Pieter van Cleynenbreugel write that the practice of pricing algorithms tacitly colluding remains theoretical for now, and technological obstacles render
Article Description
Increasingly, firms use algorithms powered by artificial intelligence to set prices. Previous research studies interactions among Q-learning algorithms in a simulated oligopoly model of price competition. The algorithms learn collusive strategies but require a long time that corresponds to several years to do so. Besides, the limited computational resources of Q-learning restrict the simulations to markets with up to 4 firms. I show that pricing algorithms using deep learning (DQN) collude significantly faster. Collusion disappears in wide oligopolies with up to 10 firms but increases with a reformulated state representation. Contrary, heterogeneity among pricing algorithms hinders collusion.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85110752738&origin=inward; http://dx.doi.org/10.2139/ssrn.3785966; https://www.ssrn.com/abstract=3785966; https://dx.doi.org/10.2139/ssrn.3785966; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3785966; https://ssrn.com/abstract=3785966
Elsevier BV
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