Autonomous algorithmic collusion: economic research and policy implications

C-Tier

Journal: Oxford Review of Economic Policy

Year: 2021

Volume: 37

Issue: 3

Pages: 459-478

Authors (11)

Stephanie Assad (not in RePEc) Emilio Calvano (Centro Studi di Economia e Fin...) Giacomo Calzolari (Alma Mater Studiorum - Univers...) Robert Clark (not in RePEc) Vincenzo Denicolò (not in RePEc) Daniel Ershov (not in RePEc) Justin Johnson (not in RePEc) Sergio Pastorello (not in RePEc) Andrew Rhodes (Toulouse School of Economics (...) Lei Xu (not in RePEc) Matthijs Wildenbeest (University of Arizona)

Score contribution per author:

0.091 = (α=2.01 / 11 authors) × 0.5x C-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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Abstract

Markets are being populated with new generations of pricing algorithms, powered with artificial intelligence (AI), that have the ability to autonomously learn to operate. This ability can be both a source of efficiency and cause of concern for the risk that algorithms autonomously and tacitly learn to collude. In this paper we explore recent developments in the economic literature and discuss implications for policy.

Autonomous algorithmic collusion: economic research and policy implications

Authors (11)

Abstract

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