Q-Learning algorithms in a hotelling model

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dc.contributor.MentorQuesada, Lucía
dc.creator.AutorPorto, Lucila
dc.date.accessioned2022-11-09T15:30:35Z
dc.date.available2022-11-09T15:30:35Z
dc.date.issued2022-11
dc.descriptionFil: Porto, Lucila. Universidad de San Andrés. Departamento de Economía; Argentina.
dc.description.abstractWhat 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 supra-competitive 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.
dc.description.abstractKeywords: Algorithmic Collusion, Reinforcement Learning, Q-Learning, Hotelling.
dc.formatapplication/pdf
dc.identifier.citationPorto, L. (2022). Q-Learning algorithms in a hotelling model. [Tesis de maestría, Universidad de San Andrés. Departamento de Economía]. Repositorio Digital San Andrés. http://hdl.handle.net/10908/22800
dc.identifier.urihttp://hdl.handle.net/10908/22800
dc.languageeng
dc.publisherUniversidad de San Andrés. Departamento de Economía
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleQ-Learning algorithms in a hotelling model
dc.typeTesis
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typeinfo:ar-repo/semantics/tesis de maestría
dc.typeinfo:eu-repo/semantics/updatedVersion
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