Arbeitspapier
From imitation to collusion: Long-run learning in a low-information environment
We study long-run learning in an experimental Cournot game with no explicit information about the payoff function. Subjects see only the quantities and payoffs of each oligopolist after every period. In line with theoretical predictions and previous experimental findings, duopolies and triopolies both reach highly competitive levels, with price approaching marginal cost within 50 periods. Using the new ConG software, we extend the horizon to 1,200 periods, far beyond that previously investigated. Already after 100 periods we observe a qualitative change in behavior, and quantity choices start to drop. Without pausing at the Cournot-Nash level quantities continue to drop, eventually reaching almost fully collusive levels in duopolies and often reaching deep into collusive territory for triopolies. Fitted models of individual adjustment suggest that subjects switch from imitation of the most profitable rival to other behavior that, intentionally or otherwise, facilitates collusion via effective punishment and forgiveness. Remarkably, subjects never learn the best-reply correspondence of the one-shot game. Our results suggest a new explanation for the emergence of cooperation.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 701
- Klassifikation
-
Wirtschaft
Stochastic and Dynamic Games; Evolutionary Games; Repeated Games
Design of Experiments: Laboratory, Individual
Market Structure, Pricing, and Design: Oligopoly and Other Forms of Market Imperfection
- Thema
-
cournot oligopoly
imitation
learning dynamics
cooperation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Friedman, Daniel
Huck, Steffen
Oprea, Ryan
Weidenholzer, Simon
- Ereignis
-
Veröffentlichung
- (wer)
-
University of California, Economics Department
- (wo)
-
Santa Cruz, CA
- (wann)
-
2012
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Friedman, Daniel
- Huck, Steffen
- Oprea, Ryan
- Weidenholzer, Simon
- University of California, Economics Department
Entstanden
- 2012
Ähnliche Objekte (12)
From imitation to collusion: Long-run learning in a low-information environment
From imitation to collusion: long-run learning in a low-information environment
Through trial & error to collusion
Merger and collusion in contests
Merger and collusion in contests
On the profitability of collusion in location games
On the profitability of collusion in location games
Through Trial & Error to Collusion - The Discrete Case
On the profitability of collusion in location games
Imitation - Theory and Experimental Evidence
Imitation - Theory and Experimental Evidence
Imitation - Theory and Experimental Evidence
From imitation to collusion: Long-run learning in a low-information environment
From imitation to collusion: long-run learning in a low-information environment
Through trial & error to collusion
Merger and collusion in contests
Merger and collusion in contests
On the profitability of collusion in location games
On the profitability of collusion in location games
Through Trial & Error to Collusion - The Discrete Case
On the profitability of collusion in location games
Imitation - Theory and Experimental Evidence
Imitation - Theory and Experimental Evidence
Imitation - Theory and Experimental Evidence
From imitation to collusion: Long-run learning in a low-information environment
From imitation to collusion: long-run learning in a low-information environment
Through trial & error to collusion
Merger and collusion in contests
Merger and collusion in contests
On the profitability of collusion in location games
On the profitability of collusion in location games
Through Trial & Error to Collusion - The Discrete Case
On the profitability of collusion in location games
Imitation - Theory and Experimental Evidence
Imitation - Theory and Experimental Evidence