Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?
Name
SSRN-id3261273.pdf
Description
Submitted version
Size
330.89 KB
Format
Adobe PDF
Checksum (MD5)
489dfd929b3046b1a436cb1063e95dc7
Author(s)
Miklós-Thal, Jeanine
Tucker, Catherine
Date Issued
2019
Journal
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Version
Original manuscript
Abstract
© 2019 INFORMS. We build a game-theoretic model to examine how better demand forecasting resulting from algorithms, machine learning, and artificial intelligence affects the sustainability of collusion in an industry. We find that, although better forecasting allows colluding firms to better tailor prices to demand conditions, it also increases each firm's temptation to deviate to a lower price in time periods of high predicted demand. Overall, our research suggests that, despite concerns expressed by policy makers, better forecasting and algorithms can lead to lower prices and higher consumer surplus.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/MNSC.2019.3287
