Robust Algorithmic Collusion
Type
conference paper
Author(s)
Abstract (De)
This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcement-learning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe static Nash play. We then show that restricting algorithms’ strategy space can make algorithmic collusion robust, because it limits overfitting to rival strategies. Our findings suggest that policy-makers should focus on firm behavior aimed at coordinating algorithm design in order to make collusive policies robust.
Language
English
HSG Classification
None
HSG Profile Area
SEPS - Quantitative Economic Methods
Subject(s)
Division(s)
Eprints ID
265810
File(s)![Thumbnail Image]()
Name
2201.00345.pdf
Size
571.71 KB
Format
Adobe PDF
Checksum (MD5)
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