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Peer-to-peer sharing has become increasingly popular in recent years. Many digital platforms exist that allow individuals to use others' belongings part-time. These platforms explicitly mention their green credentials, as the environmental benefits of such sharing initiatives are often taken for granted. However, there is a recent empirical literature showing evidence of the contrary. We propose a theoretical framework to analyze the economic and environmental implications of peer-to-peer sharing. We present a stylized model where a monopolist supplies a product that is suitable for rent on a sharing platform. Interestingly, we find that the existence of such a platform is typically beneficial for the monopolist, especially in the long run, when she can optimally anticipate the effects of her decisions on the sharing market. Such a scenario may not be beneficial for consumers, especially for those who rent the good rather than buy it. Moreover, the existence of the sharing platform induces higher use and (under some likely conditions) larger production levels and shorter product lifespans. The combination of these three aspects contributes to a worse environmental impact with sharing, which provides a theoretical rationale for the aforementioned empirical studies. [ABSTRACT FROM AUTHOR]

A learning rule is completely uncoupled if each player's behavior is conditioned only on his own realized payoffs, and does not need to know the actions or payoffs of anyone else. We demonstrate a simple, completely uncoupled learning rule such that, in any finite normal form game with generic payoffs, the players' realized strategies implements a socially optimal coarse correlated (Hannan) equilibrium a very high proportion of the time. That is, the empirical frequency associated with the players' collective behavior will be consistent with a socially optimal coarse correlated equilibrium. A variant of the rule implements a socially optimal correlated equilibrium a very high proportion of the time. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a modification to the BitTorrent protocol related to its peer unchoking policy. In particular, we apply a novel optimistic unchoking approach that improves the quality of inter-connections amongst peers, i.e., increases the number of directly-connected and interested-in-cooperation peers without penalizing underutilized and/or idle peers. Our optimistic unchoking policy takes into consideration the number of clients currently interested in downloading from a peer that is to be unchoked. Our conjecture is that peers having few clients interested in downloading data from them, should be favored with optimistic unchoke intervals. This enables the peers in question to receive data since they become unchoked faster and in turn, they will trigger the interest of additional clients. In contrast, peers with plenty of “interested” clients should enjoy a lower priority to be selected as planned optimistic unchoked, since these peers likely have enough data to forward; nevertheless, they receive enough data due to tit-for-tat peer reciprocation and are not in need of optimistic unchoking slots. Armed with this realization, we establish an analytical model and prove a significant performance improvement under our modified BitTorrent protocol. Experimental results, also, indicate that our approach significantly outperforms the existing optimistic unchoking policy in three important aspects: first, there is a higher number of interested-in-cooperation and directly-connected peers. Second, since leechers now act as data intermediaries, the load on seeders eases up considerably. Last, a shorter bootstrapping period for fresh peers is achieved. Hence, we claim that our approach helps implement an enhanced BitTorrent protocol and we name it “EnhancedBit”. [Copyright &y& Elsevier]

Abstract: Trust research has become a key issue in the last few years as a novel and valid solution to ensure the security and application in peer-to-peer (P2P) file-sharing networks. The accurate measure of trust and reputation is a hard problem, most of the existing trust mechanisms adopt the historical behavior feedback to compute trust and reputation. Thus exploring the appropriate transaction behavior becomes a fundamental challenge. In P2P system, each peer plays two roles: server and client with responsibility for providing resource service and trust recommending respectively. Considering the resource service behavior and trust recommending behavior of each peer, in this paper, we propose a new trust model adopting the technology to calculate eigenvectors of trust rating and recommending matrices. In our model, we define recommended reputation value to evaluate the resource service behavior, and recommending reputation value to evaluate the trust recommendation behavior. Our algorithm would make these two reputation values established an interrelated relation of reinforcing mutually. The normal peers provide authentic file uploading services, as well as give correct trust recommendation, so they can form a trusted and cooperative transaction community via the mutual reinforcement of recommended and recommending reputation values. In this way, the transaction behaviors of those malicious peers are isolated and confined effectively. Extensive experimental results also confirm the efficiency of our trust model against the threats of exaggeration, collusion, disguise, sybil and single-behavior. [Copyright &y& Elsevier]

Abstract: Motivating peers to contribute services is critical to the success of peer-to-peer (P2P) systems. Incentive protocols use reciprocity to enforce contributions. Indirect reciprocity schemes are more efficient than direct reciprocity schemes for large-scale P2P systems under high churn rate. In this paper, we propose an indirect reciprocity scheme, called FairTrade, in which peers issue personal currencies to trade services in a P2P system. Personal currency enables indirect reciprocity without relying on any central banks or authorities. It wins extra robustness over global currency as well as much improved trading flexibility and efficiency over direct reciprocity schemes. The acceptance degree of a personal currency depends on the issuer’s service capability and reliance. Peer credit limit is introduced to represent the amount of personal currency that will be accepted by other peers. Every peer as a creditor applies a Bayesian network model to setting peer credit limit for a trading partner peer as a creditee. The Bayesian network model learns the creditee’s capability and reliability and anticipates the associated profits and risks for credit setting. Using simulations on a file-sharing P2P system, we demonstrate that FairTrade achieves success rate of download requests without malicious peers, and maintains over success rate even with malicious nodes. The system warms up quickly and does not assume any altruistic service or other kind of help. On average, the system traffic stabilizes before peers issue their second download requests. All these good performances are achieved with extremely low trading overhead, which takes up less than of the total traffic. [Copyright &y& Elsevier]

Abstract: BitTorrent is a popular peer-to-peer file sharing system and a target file shared through BitTorrent is partitioned into pieces and downloaded from multiple peers in parallel in order to shorten the download process. However, due to peer dynamics in P2P networks, rare pieces may be lost and thus lead to the so-called last piece problem. BitTorrent employs rarest-first piece selection algorithm to deal with this problem, but its efficacy is limited because each peer only has a local view of piece rareness. In this paper, we propose an Interest-Intended Piece Selection (IIPS) algorithm aiming at better alleviating the last piece problem while maintaining stable cooperation between peers. IIPS is named interest intended in that every IIPS peer favors pieces that, if downloaded, would increase the probability of being interesting to its cooperating peers. Simulation results show that IIPS achieves less occurrences of piece loss under tough conditions and slightly outperforms the BitTorrent’s rarest-first algorithm in terms of higher piece diversity. [Copyright &y& Elsevier]

Abstract: In large-scale peer-to-peer (P2P) computing, trust management technology has become a key requirement for network security. However, trust is one of the most complex concepts in network communities. It involves many factors, such as, assumptions, expectations, behaviors, risks, and so on. As a result, trustworthiness has multi-dimensional properties. In this paper, an innovative trust model is proposed for large-scale P2P computing, in which multiple factors are incorporated to reflect the complexity of trust. More importantly, the properties (weights) of these factors are dynamically assigned by weighted moving average and ordered weighted averaging (WMA–OWA) combination algorithms. This model surpasses the limitations of existing approaches, wherein weights are assigned subjectively. The simulation results show that, compared with the existing approaches, the proposed model provides greater accuracy and a more detailed analysis in trust evaluation. [Copyright &y& Elsevier]

Abstract: The decentralized peer-to-peer (P2P) technique has been widely used to implement scalable file sharing systems. It organizes nodes in a system into a structured or unstructured network. The advantages of the unstructured P2P systems are that they have lower maintenance complexity and can better adapt to node heterogeneity as well as network dynamics. However, the search process in unstructured systems is not as efficient as in structured P2P systems because the same search message may go through a node multiple times. To facilitate the complex search and improve the search efficiency, we propose a novel approach of assigning identifications to nodes in an unstructured system. Our method can prevent a node from receiving duplicate search messages and retain the low maintenance overhead for the system. The performance evaluations demonstrate that the proposed approach can improve the search efficiency of unstructured P2P systems while keeping the maintenance overhead at a comparable or even lower level, compared with the traditional unstructured systems. [Copyright &y& Elsevier]

Knowledge sharing enables people in virtual communities to access relevant knowledge (explicit or tacit) from broader scope of resources. The performance in such environments is fundamentally based on how effectively the explicit and tacit knowledge can be shared across people, and how efficiently the created knowledge can be organized and disseminated to enrich digital content. This study will address how to apply social network-based system to support interactive collaboration in knowledge sharing over peer-to-peer networks. Results of this study demonstrate that applying such social network-based collaboration support to knowledge sharing helps people find relevant content and knowledgeable collaborators who are willing to share their knowledge. [Copyright &y& Elsevier]