Arbeitspapier
Algorithmic and human collusion
As self-learning pricing algorithms become popular, there are growing concerns among academics and regulators that algorithms could learn to collude tacitly on non-competitive prices and thereby harm competition. I study popular reinforcement learning algorithms and show that they develop collusive behavior in a simulated market environment. To derive a counterfactual that resembles traditional tacit collusion, I conduct market experiments with human participants in the same environment. Across different treatments, I vary the market size and the number of firms that use a self-learned pricing algorithm. I provide evidence that oligopoly markets can become more collusive if algorithms make pricing decisions instead of humans. In two-firm markets, market prices are weakly increasing in the number of algorithms in the market. In three-firm markets, algorithms weaken competition if most firms use an algorithm and human sellers are inexperienced.
- ISBN
-
978-3-86304-371-1
- Sprache
-
Englisch
- Erschienen in
-
Series: DICE Discussion Paper ; No. 372
- Klassifikation
-
Wirtschaft
Design of Experiments: General
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Oligopoly and Other Imperfect Markets
Monopolization; Horizontal Anticompetitive Practices
- Thema
-
Artificial Intelligence
Collusion
Experiment
Human-Machine Interaction
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Werner, Tobias
- Ereignis
-
Veröffentlichung
- (wer)
-
Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE)
- (wo)
-
Düsseldorf
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Werner, Tobias
- Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE)
Entstanden
- 2021
Ähnliche Objekte (12)
Algorithmic and Human Collusion
Algorithmic and Human Collusion
Hybrid collusion: Algorithmic pricing in human-computer laboratory markets
Algorithmic price recommendations and collusion: Experimental evidence
Assessing Autonomous Algorithmic Collusion: Q-Learning Under Short-Run Price Commitments
Gender and collusion
Decentralization and Collusion
Gender and Collusion
Collusion and Durability
Private Observation, Tacit Collusion and Collusion with Communication
Human vs. machine: Disposition effect among algorithmic and human day traders
Excess Capacity and Collusion
Algorithmic and Human Collusion
Algorithmic and Human Collusion
Hybrid collusion: Algorithmic pricing in human-computer laboratory markets
Algorithmic price recommendations and collusion: Experimental evidence
Assessing Autonomous Algorithmic Collusion: Q-Learning Under Short-Run Price Commitments
Gender and collusion
Decentralization and Collusion
Gender and Collusion
Collusion and Durability
Private Observation, Tacit Collusion and Collusion with Communication
Human vs. machine: Disposition effect among algorithmic and human day traders
Excess Capacity and Collusion
Algorithmic and Human Collusion
Algorithmic and Human Collusion
Hybrid collusion: Algorithmic pricing in human-computer laboratory markets
Algorithmic price recommendations and collusion: Experimental evidence
Assessing Autonomous Algorithmic Collusion: Q-Learning Under Short-Run Price Commitments
Gender and collusion
Decentralization and Collusion
Gender and Collusion
Collusion and Durability
Private Observation, Tacit Collusion and Collusion with Communication
Human vs. machine: Disposition effect among algorithmic and human day traders