Transparency and Control in Platforms for Networked Markets

John Pang (), Weixuan Lin (), Hu Fu (), Jack Kleeman (), Eilyan Bitar () and Adam Wierman ()
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John Pang: Computing and Mathematical Sciences Department, California Institute of Technology, Pasadena, California 91125
Weixuan Lin: School of Electrical and Computer Engineering, Cornell University, Ithaca, New York 14853
Hu Fu: Department of Computer Science, University of British Columbia, Vancouver BC V6T 1Z4, Canada
Jack Kleeman: Faculty of Economics, University of Cambridge, Cambridge CB3 9DD, UK
Eilyan Bitar: School of Electrical and Computer Engineering, Cornell University, Ithaca, New York 14853
Adam Wierman: Computing and Mathematical Sciences Department, California Institute of Technology, Pasadena, California 91125

Operations Research, 2022, vol. 70, issue 3, 1665-1690

Abstract: In this paper, we analyze the worst-case efficiency loss of online platform designs under a networked Cournot competition model. Inspired by some of the largest platforms in operation today, we study a variety of platform designs to examine the impacts of market transparency and control on the worst-case efficiency loss of Nash equilibria in networked Cournot games. Our results show that open access designs incentivize increased production toward perfectly competitive levels and limit efficiency loss, while controlled allocation designs lead to producer-platform incentive misalignment, resulting in low participation rates and unbounded efficiency loss. We also show that discriminatory access designs balance transparency and control, achieving the best of both worlds by maintaining high participation rates while limiting efficiency loss.

Keywords: Market Analytics and Revenue Management; games/group decisions; networks/graphs (search for similar items in EconPapers)
Date: 2022
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