An Empirical Analysis of Network Externalities in Peer-to-Peer Music-Sharing Networks

Peer-to-peer (P2P) networks are becoming an important medium for the distribution of consumer information goods. However, there is little academic research into the behavior of these networks. We analyze the impact of positive and negative network externalities on the optimal size of P2P networks. Using data collected from the six most popular OpenNap P2P music-sharing networks between December 19, 2000, and April 22, 2001, we find that additional users contribute value in terms of additional network content at a diminishing rate, while they impose costs in terms of congestion on shared resources at an increasing rate. Using an analytic model, we explore technical solutions to the congestion problem, for example, by increasing network capacity. This model suggests that although increasing capacity will allow more users to participate on the network, there may be little incentive for network operators to do so. This is because diminishing positive network externalities imply decreasing content benefits to adding more users. Together these results suggest that the optimal size of a P2P network may be bounded in many common implementations. We conclude by discussing various options to improve network performance including network membership rules and usage- based pricing

The New Role of the Private Sector in Community Development: A Case Study in Artisanal Fishery Communities in Thailand

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Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach

Improving the information retrieval (IR) performance of peer-to-peer networks is an important and challenging problem. Recently, the computer science literature has attempted to address this problem by improving IR search algorithms. However, in peer-to-peer networks, IR performance is determined by both technology and user behavior, and very little attention has been paid in the literature to improving IR performance through incentives to change user behavior. We address this gap by combining the club goods economics literature and the IR literature to propose a next generation file sharing architecture. Using the popular Gnutella 0.6 architecture as context, we conceptualize a Gnutella ultrapeer and its local network of leaf nodes as a "club" (in economic terms). We specify an information retrieval-based utility model for a peer to determine which clubs to join, for a club to manage its membership, and for a club to determine to which other clubs they should connect. We simulate the performance of our model using a unique real-world dataset collected from the Gnutella 0.6 network. These simulations show that our club model accomplishes both performance goals. First, peers are self-organized into communities of interest - in our club model peers are 85% more likely to be able to obtain content from their local club than they are in the current Gnutella 0.6 architecture. Second, peers have increased incentives to share content - our model shows that peers who share can increase their recall performance by nearly five times over the performance offered to free-riders. We also show that the benefits provided by our club model outweigh the added protocol overhead imposed on the network for the most valuable peers

Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach

Improving the information retrieval (IR) performance of peer-to-peer networks is an important and challenging problem. Recently, the computer science literature has attempted to address this problem by improving IR search algorithms. However, in peer-to-peer networks, IR performance is determined by both technology and user behavior, and very little attention has been paid in the literature to improving IR performance through incentives to change user behavior. We address this gap by combining the club goods economics literature and the IR literature to propose a next generation file sharing architecture. Using the popular Gnutella 0.6 architecture as context, we conceptualize a Gnutella ultrapeer and its local network of leaf nodes as a "club" (in economic terms). We specify an information retrieval-based utility model for a peer to determine which clubs to join, for a club to manage its membership, and for a club to determine to which other clubs they should connect. We simulate the performance of our model using a unique real-world dataset collected from the Gnutella 0.6 network. These simulations show that our club model accomplishes both performance goals. First, peers are self-organized into communities of interest - in our club model peers are 85% more likely to be able to obtain content from their local club than they are in the current Gnutella 0.6 architecture. Second, peers have increased incentives to share content - our model shows that peers who share can increase their recall performance by nearly five times over the performance offered to free-riders. We also show that the benefits provided by our club model outweigh the added protocol overhead imposed on the network for the most valuable peers

A Club Economics Approach

, is a non-profit institution devoted to research on network industries, electronic commerce, telecommunications, the Internet, “virtual networks” comprised of computers that share the same technical standard or operating system, and on network issues in general. Interest-Based Self-Organizing Peer-to-Peer Networks