dc.contributor | Quesada, Lucía | |
dc.date.accessioned | 2022-07-28T17:10:47Z | |
dc.date.available | 2022-07-28T17:10:47Z | |
dc.date.created | 2022-07-28T17:10:47Z | |
dc.date.issued | 2021-11 | |
dc.identifier | http://hdl.handle.net/10908/19655 | |
dc.description.abstract | Firms increasingly delegate their strategic decisions to algorithms. A potential concern
is that algorithms may undermine competition by leading to pricing outcomes that are
collusive, even without having been designed to do so. This paper investigates whether
Q-learning algorithms can learn to collude in a setting with sequential price competition and stochastic marginal costs adapted from Maskin and Tirole (1988). By extending a
previous model developed in Klein (2021), I find that sequential Q-learning algorithms
leads to supracompetitive profits despite they compete under uncertainty and this finding
is robust to various extensions. The algorithms can coordinate on focal price equilibria or
an Edgeworth cycle provided that uncertainty is not too large. However, as the market
environment becomes more uncertain, price wars emerge as the only possible pricing
pattern. Even though sequential Q-learning algorithms gain supracompetitive profits,
uncertainty tends to make collusive outcomes more difficult to achieve. | |
dc.description.abstract | Keywords: Competition Policy, Artificial Intelligence, Pricing Algorithms, Collusion. | |
dc.publisher | Universidad de San Andrés. Departamento de Economía | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.title | Collusion and artificial intelligence : a computational experiment with sequential pricing algorithms under stochastic costs | |
dc.type | Tesis | |
dc.type | info:eu-repo/semantics/masterThesis | |
dc.type | info:ar-repo/semantics/tesis de maestría | |
dc.type | info:eu-repo/semantics/updatedVersion | |