In recent years, a range of online applications have facilitated resource sharing among users, resulting in a significant increase in resource utilization. In all such applications, sharing one’s resources or skills with other agents increases social welfare. In general, each agent will look for other agents whose available resources complement hers, thereby forming natural sharing groups. In this paper, we study settings where a large population selforganizes into sharing groups. In many cases, centralized optimization approaches for creating an optimal partition of the user population are infeasible because either the central authority does not have the necessary information to compute an optimal partition, or it does not have the power to enforce a partition. Instead, the central authority puts in place an incentive structure in the form of a utility sharing method, before letting the participants form the sharing groups by themselves. We first analyze a simple equalsharing method, which is the one most typically encountered in practice and show that it can lead to highly inefficient equilibria. We then propose a Shapleysharing method and show that it significantly improves overall social welfare.
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Information Design in Spatial Resource Competition
We consider information design in spatial resource competition, motivated by ride sharing platforms sharing information with drivers about rider demand. Each of N colocated agents (drivers) decides whether to move to another location with an uncertain and possibly higher resource level (rider demand), where the utility for moving increases in the resource level and decreases in the number of other agents that move. A principal who can observe the resource level wishes to share this information in a way that ensures a welfaremaximizing number of agents move. Analyzing the principal’s information design problem using the Bayesian persuasion framework, we study both private signaling mechanisms, where the principal sends personalized signals to each agent, and public signaling mechanisms, where the principal sends the same information to all agents. We show:
1) For private signaling, computing the optimal mechanism using the standard approach leads to a linear program with 2 N variables, rendering the computation challenging. We instead describe a computationally efficient twostep approach to finding the optimal private signaling mechanism. First, we perform a change of variables to solve a linear program with O(N^2) variables that provides the marginal
probabilities of recommending each agent move. Second, we describe an efficient sampling procedure over sets of agents consistent with these optimal marginal probabilities; the optimal private mechanism
then asks the sampled set of agents to move and the rest to stay.
2) For public signaling, we first show the welfaremaximizing equilibrium given any common belief has a threshold structure. Using this, we show that the optimal public mechanism with respect to the senderpreferred equilibrium can be computed in polynomial time.
3) We support our analytical results with numerical computations that show the optimal private and public signaling mechanisms achieve substantially higher social welfare when compared with noinformation and fullinformation benchmarks.
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 NSFPAR ID:
 10128623
 Date Published:
 Journal Name:
 Lecture notes in computer science
 ISSN:
 16113349
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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