Template-Type: ReDIF-Paper 1.0
Author-Name: Lucila Porto
Author-Name-First: Lucila
Author-Name-Last: Porto
Title: Q-Learning algorithms in a Hotelling model
Abstract: What if Q-Learning algorithms set not only prices but also the degree of differentiation between them? In this paper, I tackle this question by analyzing the competition between two Q-Learning algorithms in a Hotelling setting. I find that most of the simulations converge to a Nash Equilibrium where the algorithms are playing non-competitive strategies. In most simulations, they optimally learn not to differentiate each other and to set a collusive price. An underlying deviation and punishment scheme sustains this implicit agreement. The results are robust to the enlargement of the action space and the introduction of relocalization costs.
Length:  48 pages
Creation-Date:  2022-11
File-URL: https://aaep.org.ar/works/works2022/4587.pdf
File-Format: Application/pdf
Number: 4587
Classification-JEL: L1, L4
Handle: RePEc:aep:anales:4587