000			01132nam a22001457a 4500
999			_c517268 _d517268
008			210707b ||||| |||| 00| 0 eng d
100			_aCalvano, E. et al _926238
245			_aArtificial Intelligence, algorithmic pricing, and collusion
260			_aThe American Economic Review
300			_a110(10), Oct, 2020: p.3267-3297
520			_aIncreasingly, 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. – Reproduced
773			_aThe American Economic Review
906			_aARTIFICIAL INTELLIGENCE
942			_cAR