Abstract
Referral-based peer-to-peer networks have a wide range of applications. They provide a natural framework in which agents can help each other. This paper studies the trade-off between social welfare and fairness in referral networks. The traditional, naive mechanism yields high social welfare but at the cost of some agents—in particular, the “best” ones—being exploited. Autonomous agents would obviously not participate in such networks. An obvious mechanism such as reciprocity improves fairness but substantially lowers welfare. A more general incentive mechanism yields high fairness with only a small loss in welfare. This paper considers substructures of the network that emerge and cause the above outcomes.
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Manavalan, P., Singh, M.P. (2012). Emerging Properties of Knowledge Sharing Referral Networks: Considerations of Effectiveness and Fairness. In: Beneventano, D., Despotovic, Z., Guerra, F., Joseph, S., Moro, G., de Pinninck, A.P. (eds) Agents and Peer-to-Peer Computing. AP2PC AP2PC 2009 2008. Lecture Notes in Computer Science(), vol 6573. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31809-2_2
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DOI: https://doi.org/10.1007/978-3-642-31809-2_2
Publisher Name: Springer, Berlin, Heidelberg
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