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BEGIN:VEVENT
UID:e56a17367a3d395caf1215abc78e7534
CATEGORIES:Seminars
CREATED:20181114T191818
SUMMARY:Lunch Seminar: Emilio Calvano- Università degli Studi di Bologna
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:<p><strong><span style="font-size: 11pt; font-family: 'Calibri','sans-serif
 ';">Artificial Intelligence and Collusion</span></strong><span style="font-
 size: 11pt; font-family: 'Calibri','sans-serif';"> (joint with G. Calzolari
 , S. Pastorello and V. Denicolo)</span></p><p><strong><span style="font-siz
 e: 11pt; font-family: 'Calibri','sans-serif';">Abstract: </span></strong></
 p><p style="text-align: justify;"><span style="font-size: 11pt; font-family
 : 'Calibri','sans-serif';">AI algorithms are increasingly replacing human d
 ecision making in real marketplaces. To inform the debate on potential cons
 equences, we run experiments with AI agents powered by reinforcement learni
 ng in controlled environments (computer simulations). In particular, we stu
 dy interaction in the context of a workhorse oligopoly model: price competi
 tion with Logit demand and constant marginal costs. We show that independen
 t Q-learners interacting repeatedly with no previous knowledge consistently
  learn to charge supra-competitive prices. They do so by successfully coord
 inating on classic collusive strategies. That is schemes providing incentiv
 es to cooperate through off-path, incentive compatible and thus credible pu
 nishments. We show that this finding is robust to asymmetries in cost or de
 mand structure and to changes in the number of players, product differentia
 tion and demand level. </span></p>
DTSTAMP:20260405T203832Z
DTSTART:20181119T130000Z
DTEND:20181119T140000Z
SEQUENCE:0
TRANSP:OPAQUE
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