Information Sharing in Networks of Strategic Agents
Jie Xu, Yangbo Song, Mihaela van der Schaar

TL;DR
This paper introduces a distributed rating protocol framework to incentivize strategic agents in social networks to share information, ensuring efficient collaboration despite costs and self-interest, even in dynamic network topologies.
Contribution
The paper presents a novel distributed, online rating protocol that accounts for network heterogeneity and time-varying topologies, improving incentives for information sharing among strategic agents.
Findings
The proposed protocol achieves a price of anarchy of one in many scenarios.
It outperforms existing mechanisms like Tit-for-Tat in non-ideal settings.
The protocol remains effective in dynamic, evolving networks.
Abstract
To ensure that social networks (e.g. opinion consensus, cooperative estimation, distributed learning and adaptation etc.) proliferate and efficiently operate, the participating agents need to collaborate with each other by repeatedly sharing information. However, sharing information is often costly for the agents while resulting in no direct immediate benefit for them. Hence, lacking incentives to collaborate, strategic agents who aim to maximize their own individual utilities will withhold rather than share information, leading to inefficient operation or even collapse of networks. In this paper, we develop a systematic framework for designing distributed rating protocols aimed at incentivizing the strategic agents to collaborate with each other by sharing information. The proposed incentive protocols exploit the ongoing nature of the agents' interactions to assign ratings and through…
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