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IEOR Seminar by Dr. Satyanath Bhat

Title: Double Dipping of Two-Sided Platform Economy

Speaker: Dr. Satyanath Bhat, Institute of Operations Research and Analytics, National University of Singapore.

Time and Date: 10 AM-11 AM, on 28th January, 2020 (Tuesday)

Venue: IEOR Teaching Lab (Room 011, Ground Floor, IEOR Building)

Abstract:

Traditional brick and mortar industries across the spectrum of sectors are facing existential crisis due to the spurt of growth seen by their online platform counterparts. Platforms are everywhere from transportation (e.g. Uber, Lyft, DiDi, Grab, and GoJek) to software development (e.g. Android and iOS). The role of platforms in breaking traditional oligopolies and creating new markets is overwhelmingly positive. Furthermore, when compared against the inefficient traditional incumbents, the technologically superior platforms have generated undeniable social welfare to both sides. This has helped platforms to thrive and thereby corner a growing market share. However, it is important to reinspect the social welfare when platforms turn towards profitability. With this goal, we examine a two-sided platform economy against a more stricter benchmark -- an open platform that discloses its proprietary information.

In this work, we study the specific example of a job platform. The job platform has a private inventory of candidates seeking jobs on one of its sides and companies with job openings on the other. We analyze this as a single round static game. The platform is paid a commission equal to the first month's salary for every successful hire.  The jobs and candidates, on the other hand, have idiosyncratic preferences which need not necessarily reflect the potential salary paid on successful hire. As the job openings and the candidates' data is private to the platform, it can carefully control the listings to influence profitable hires. The candidates and job openings are autonomous as they interact within the listing exposed by the platform but this interaction is beyond the control of the platform. Under a listing, we use the standard notion of candidate optimal stable marriage as the eventual outcome. We show that the optimal listing choice by the platform in its quest to maximize its revenue is detrimental to both its sides; this results in significant social welfare losses when compared to the benign open platform.

Speaker Bio: Satya is a Post-doctoral Research Fellow at the Institute of Operations Research and Analytics, National University of Singapore since August 2017. His research is mainly focussed on Game theory and Mechanism design as applied to various practical problems. Currently, he is studying the social welfare losses that emerge due to the profit-making intent of a two-sided platform economy. Satya obtained his PhD and MSc(Engg) degree from the Indian Institute of Science (IISc), Bangalore. During his time at IISc, Satya looked at quality assurance mechanisms as applied to sponsored search auctions and crowdsourcing. During his postgraduate studies, Satya spent a summer as an intern at the Xerox Research Center Europe (XRCE), Grenoble, France. Prior to joining IISc, Satya worked as a software developer for 6 years primarily at Philips Medical Systems, Bangalore.

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