While the fundamental premise of peer-to-peer (P2P) systems is that of voluntary resource sharing among individual peers, there is an inherent tension between individual rationality and collective welfare that threatens the viability of these systems. This paper surveys recent research at the intersection of economics and computer science that targets the design of distributed systems consisting of rational participants with diverse and selfish interests. In particular, we discuss major findings and open questions related to free-riding in P2P systems: factors affecting the degree of free-riding, incentive mechanisms to encourage user cooperation, and challenges in the design of incentive mechanisms for P2P systems.

Mechanism design (MD) provides a useful method to implement outcomes with desirable properties in systems with self-interested computational agents. One drawback, however, is that computation is implicitly centralized in MD theory, with a central planner taking all decisions. We consider distributed implementations, in which the outcome is determined by the self-interested agents themselves. Clearly this introduces new opportunities for manipulation. We propose a number of principles to guide the distribution of computation, focusing in particular on Vickrey-Clarke-Groves mechanisms for implementing outcomes that maximize total value across agents. Our solutions bring the complete implementation into an ex post Nash equilibrium.

In any multi-hop routing scheme, cooperation by the intermediate nodes are essential for the succesful delivery of traffic. However, the effort exerted by the intermediate nodes are often unobservable by the source and/or destination nodes. We show it is possible to overcome this problem of hidden action by designing contracts, in the form of payments, to induce cooperation from the intermediate nodes. Interestingly, the ability to monitor per-hop or per-path outcomes, even if costless to implement, may not improve the welfare of the participants or the performance of the network.