Fair Allocation in Crowd-Sourced Systems

Mishal Assif, William Kennedy and Iraj Saniee ()
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Mishal Assif: Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
William Kennedy: Mathematics & Algorithms Research Group, AI Lab, Bell Labs, Nokia, Murray Hill, NJ 07974, USA
Iraj Saniee: Mathematics & Algorithms Research Group, AI Lab, Bell Labs, Nokia, Murray Hill, NJ 07974, USA

Games, 2023, vol. 14, issue 4, 1-18

Abstract: In this paper, we address the problem of fair sharing of the total value of a crowd-sourced network system between major participants (founders) and minor participants (crowd) using cooperative game theory. We use the framework of a Shapley allocation which is regarded as a fundamental method of computing the fair share of all participants in a cooperative game when the values of all possible coalitions could be quantified. To quantify the value of all coalitions, we define a class of value functions for crowd-sourced systems which capture the contributions of the founders and the crowd plausibly and derive closed-form expressions for Shapley allocations to both. These value functions are defined for different scenarios, such as the presence of oligopolies or geographic spread of the crowd, taking network effects, including Metcalfe’s law, into account. A key result we obtain is that under quite general conditions, the crowd participants are collectively owed a share between 1 2 and 2 3 of the total value of the crowd-sourced system. We close with an empirical analysis demonstrating the consistency of our results with the compensation offered to the crowd participants in some public internet content sharing companies.

Keywords: fair allocation; cooperative games; shapley value; network effect; social media; value function (search for similar items in EconPapers)
JEL-codes: C C7 C70 C71 C72 C73 (search for similar items in EconPapers)
Date: 2023
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