We show that if they are allowed enough time to complete the learning, Q-learning algorithms can learn to collude in an environment with imperfect monitoring adapted from Green and Porter (1984), without having been instructed to do so, and without communicating with one another. Collusion is sustained by punishments that take the form of “price wars” triggered by the observation of low prices. The punishments have a finite duration, being harsher initially and then gradually fading away. Such punishments are triggered both by deviations and by adverse demand shocks.
Calvano, E., Calzolari, G., Denicolo, V., Pastorello, S. (2021). Algorithmic collusion with imperfect monitoring. INTERNATIONAL JOURNAL OF INDUSTRIAL ORGANIZATION, 79, 1-11 [10.1016/j.ijindorg.2021.102712].
Algorithmic collusion with imperfect monitoring
Calvano E.;
2021-01-01
Abstract
We show that if they are allowed enough time to complete the learning, Q-learning algorithms can learn to collude in an environment with imperfect monitoring adapted from Green and Porter (1984), without having been instructed to do so, and without communicating with one another. Collusion is sustained by punishments that take the form of “price wars” triggered by the observation of low prices. The punishments have a finite duration, being harsher initially and then gradually fading away. Such punishments are triggered both by deviations and by adverse demand shocks.File | Dimensione | Formato | |
---|---|---|---|
IJIO editorial in press.pdf
solo utenti autorizzati
Licenza:
Copyright dell'editore
Dimensione
1.2 MB
Formato
Adobe PDF
|
1.2 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
post print IJIO.pdf
solo utenti autorizzati
Licenza:
Creative commons
Dimensione
680.86 kB
Formato
Adobe PDF
|
680.86 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.