Firms (businesses, service providers, entertainment organizations, political parties, etc.) advertise on social networks to draw people's attention and improve their awareness of the brands of the firms. In all such cases, the competitive nature of their engagements gives rise to a game where the firms need to decide how to distribute their budget over the consumers on a network to maximize their brand's awareness. The firms (players) need to optimize the budget allocation to the vertices (consumers) of the network so that the spread improves via direct (e.g., advertisements or free promotional offers) and indirect marketing (e.g., word-of-mouth). We propose a two-timescale model of decisions, where communication between vertices occurs on a faster timescale and the strategy update of firms occurs on a slower timescale. We show that under fairly standard conditions, the best response dynamics of the firms converge to a pure strategy Nash equilibrium. However, such equilibria can be away from a socially optimal one. We provide a characterization of the contest success functions and provide examples for the designers of such contests (e.g., regulators, social network providers, etc.) such that the Nash equilibrium becomes unique and social welfare maximizing. Our experiments show that for realistic scenarios, such contest success functions perform fairly well.
This is a joint work with Sayantika Mandal and Harman Agarwal.
Short bio: Swaprava is a faculty member in the Department of Computer Science and Engineering at IIT Bombay, and an associated faculty member of the Centre for Machine Intelligence and Data Science (CMInDS) and the IIT Bombay Trust Lab. He is the founding faculty member of the Computational Economics Group (CompEcon) at IIT Bombay, and previously served as a faculty member at IIT Kanpur. He has held postdoctoral positions at Carnegie Mellon University and the Indian Statistical Institute, New Delhi, and received his PhD from the Indian Institute of Science, Bangalore. His research lies at the intersection of economics and computation, with applications to Internet economics, auctions, matching markets, resource allocation, crowdsourcing, online advertising, and social networks.
