Q-Learning algorithms in a Hotelling model

Lucila Porto

No 4587, Asociación Argentina de Economía Política: Working Papers from Asociación Argentina de Economía Política

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.

JEL-codes: L1 L4 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2022-11
New Economics Papers: this item is included in nep-cmp, nep-com, nep-gth and nep-ind
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://aaep.org.ar/works/works2022/4587.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aep:anales:4587

Access Statistics for this paper

More papers in Asociación Argentina de Economía Política: Working Papers from Asociación Argentina de Economía Política Contact information at EDIRC.
Bibliographic data for series maintained by Juan Manuel Quintero ().