Distributed systems without a central authority, such as peer-to-peer (P2P) systems, employ incentives to encourage nodes to follow the prescribed protocol. Game theoretic analysis is often used to evaluate incentives in such systems. However, most game-theoretic analyses of distributed systems do not adequately model the repeated interactions of nodes inherent in such systems. We present a game-theoretic analysis of a popular P2P protocol, Bit-Torrent, that models the repeated interactions in such protocols. We also note that an analytical approach for modeling incentives is often infeasible given the complicated nature of most deployed protocols. In order to comprehensively model incentives in complex protocols, we propose a simulation-based method, which we call Design Space Analysis (DSA). DSA provides a tractable analysis of competing protocol variants within a detailed design space. We apply DSA to P2P file swarming systems. With extensive simulations we analyze a wide-range of protocol variants and gain insights into their robustness and performance. To validate these results and to demonstrate the efficacy of DSA, we modify an instrumented BitTorrent client and evaluate protocols discovered using DSA. We show that they yield higher system performance and robustness relative to the reference implementation.

Most P2P systems that have some kind of incentive mechanism reward peers according to their contribution, i.e. total bandwidth offered to the system. Due to the disparity in bandwidth capacity between P2P users on the Internet, the common effect of such mechanisms is that the fastest peers reap the highest benefits. We take a different approach and study how to incentivize cooperation in P2P systems based on effort, i.e. contribution relative to capacity. We make the following contributions: 1) we argue that contribution-based incentive schemes in P2P systems unnecessarily disfavor slow peers and decrease overall system performance; 2) we advocate the use of principles from an alternative economic vision, Participatory Economics (Parecon), to inspire systems to be fair and to ensure maximization of the social welfare while being efficient at the same time, and 3) we present the results of simulations in which we apply principles from Parecon to two popular real life systems: a) the popular file sharing BitTorrent protocol; b) a generic credit based sharing ratio enforcement scheme. Our approach yields a higher system performance and fairness and offers new insights into P2P incentive design.

The explosion of freely available media content through BitTorrent file sharing networks over the Internet means that users need guides or recommendations to find the right, high quality, content. Current systems rely on centralized servers to aggregate, rate and moderate metadata for this purpose. We present the design and simulations, using real BitTorrent traces, for a method combining fully decentralized metadata dissemination, vote sampling and ranking for deployment in the Tribler.org BitTorrent media client. Our design provides robustness to spam attacks, where metadata does not reflect the content it is attached to, by controlling metadata spreading and by vote sampling based on a collusion proof experience function. Our design is light-weight, fully decentralized and offers good performance and robustness under realistic conditions.

Peer-to-peer applications can benefit from human friendship networks (e.g., e-mail contacts or instant message buddy lists). However these are not always available. We propose an algorithm, called SLACER that allows peer nodes to create their own friendship networks, through randomized interactions, producing an artificial social network (ASN) where nodes share high trust with their neighbors

In this paper, we focus on the problem of maintaining significant levels of cooperation in peer-to-peer (P2P) networks of selfish adaptive peers. We propose a simple algorithm that maintains high levels of cooperation in such a network while performing the collective task of file sharing. The algorithm is adapted from novel "tag" models of cooperation that do not rely on explicit reciprocity, reputation or trust mechanisms. A sequence of three simulation models is presented-starting with an abstract model of tag-based cooperation (TagWorld) and finishing with a P2P file-sharing model (FileWorld) that puts the technique to work. From analysis of extensive computer simulations, we demonstrate the technique to be scalable, robust, and decentralized; it requires no central servers or authorities. The algorithm is relatively simple: peers do not need to store additional trust information about other nodes or to perform significant additional processing.

For peer-to-peer (P2P) systems to operate effectively peers need to cooperate for the benefit of the network as a whole. Most existing P2P systems assume cooperation, relying on peers to perform tasks that are of no direct individual benefit. However, when large open systems are deployed such assumptions no longer hold because by adapting selfishly nodes may become "freeloaders" leaching resources from the network. We present initial results from simulations of an algorithm allowing nodes to adapt selfishly yet maintaining high levels of cooperation in both a Prisoners' dilemma and a flood-fill query scenario. The algorithm does not require centralized or third party reputation systems, the monitoring of neighbor behavior or the explicit programming of incentives and operates in highly dynamic and noisy networks. The algorithm appears to emerge its own incentive structure.