citations.bib

@article{calvanoArtificialIntelligenceAlgorithmic2020b,
  title = {Artificial {{Intelligence}}, {{Algorithmic Pricing}}, and {{Collusion}}},
  author = {Calvano, Emilio and Calzolari, Giacomo and Denicol{\`o}, Vincenzo and Pastorello, Sergio},
  year = {2020},
  month = oct,
  journal = {American Economic Review},
  volume = {110},
  number = {10},
  pages = {3267--3297},
  issn = {0002-8282},
  doi = {10.1257/aer.20190623},
  abstract = {Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty. (JEL D21, D43, D83, L12, L13)},
  langid = {english},
}

@misc{calvanoDataCodeArtificial2020,
  title = {Data and {{Code}} for: "{{Artificial}} Intelligence, Algorithmic Pricing and Collusion"},
  shorttitle = {Data and {{Code}} For},
  author = {Calvano, Emilio and Calzolari, Giacomo and Denicol{\`o}, Vincenzo and Pastorello, Sergio},
  year = {2020},
  publisher = {{ICPSR - Interuniversity Consortium for Political and Social Research}},
  doi = {10.3886/E119462V1},
  abstract = {This material provides the codes, the executables and the scripts for the simulations in the paper ``Artificial intelligence, algorithmic pricing and collusion,'' by Emilio Calvano, Giacomo Calzolari, Vincenzo Denicol\`o, Sergio Pastorello.}
}

@misc{Tian2020Reinforcement,
  title = {{{ReinforcementLearning}}.Jl: {{A}} Reinforcement Learning Package for the Julia Programming Language},
  author = {Tian, Jun and {contributors}, other},
  year = {2020}
}