We present a game-theoretic model for the spread of deviant behavior in online social networks. We utilize a two-strategy framework wherein each player's behavior is classified as normal or deviant and evolves according to the cooperate-defect payoff scheme of the classic prisoner's dilemma game. We demonstrate convergence of individual behavior over time to a final strategy vector and indicate counterexamples to this convergence outside the context of prisoner's dilemma. Theoretical results are validated on a real-world dataset collected from a popular online forum.
Finally, our work parallels the body of work on free-riding in peer-to-peer systems @cite_5 @cite_26 . Peer-to-peer systems are designed to allow users to connect with others and share resources. Similar to online communities, users are free to access and contribute as much as desired, and few controls are in place. As a result, in p2p systems peers may abuse their connections by exploiting other peers' resources, refusing to share owned resources, sharing broken or corrupted resources, etc., draining the network without contributing it. In online communities, the health of the community is heavily based on individual peers' reactions to selfish behavior, which they may choose to emulate or disengage from. Punishment mechanisms can also be put in place, although these are often considered not to be truly effective. To tackle these issues, the most common solution is the implementation of incentive-based mechanisms. Incentives are applied in certain online forums, whereby end users are given special roles and privileges as a reward for good behavior.
@cite_5 @cite_26
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We devise a model to study the phenomenon of free-riding and free-identities in peer-to-peer systems. At the heart of our model is a user of a certain type, an intrinsic and private parameter that reflects the user's willingness to contribute resources to the system. A user decides whether to contribute or free-ride based on how the current contribution cost in the system compares to her type. We study the impact of mechanisms that exclude low type users or, more realistically, penalize free-riders with degraded service. We also consider dynamic scenarios with arrivals and departures of users, and with whitewashers -users who leave the system and rejoin with new identities to avoid reputational penalties. We find that imposing penalty on all users that join the system is effective under many scenarios. In particular, system performance degrades significantly only when the turnover rate among users is high. Finally, we show that the optimal exclusion or penalty level differs significantly from the level that optimizes the performance of contributors only for a limited range of societ al generosity levels. Peer-to-peer (P2P) networks can be easily deployed to distribute user-generated content at a low cost, but the free-rider problem hinders the efficient utilization of P2P networks. Using game theory, we investigate incentive schemes to overcome the free-rider problem in content production and sharing. We build a basic model and obtain two benchmark outcomes: 1) the non-cooperative outcome without any incentive scheme and 2) the cooperative outcome. We then propose and examine three incentive schemes based on pricing, reciprocation, and intervention. We also study a brute-force scheme that enforces full sharing of produced content. We find that 1) cooperative peers share all produced content while non-cooperative peers do not share at all without an incentive scheme; 2) by utilizing the P2P network efficiently, the cooperative outcome achieves higher social welfare than the non-cooperative outcome does; 3) a cooperative outcome can be achieved among non-cooperative peers by introducing an incentive scheme based on pricing, reciprocation, or intervention; and 4) enforced full sharing has ambiguous welfare effects on peers. In addition to describing the solutions of different formulations, we discuss enforcement and informational requirements to implement each solution, aiming to offer a guideline for protocol design for P2P networks. In this paper we model and study the performance of peer-to-peer (P2P) file sharing systems in terms of their 'service capacity'. We identify two regimes of interest: the transient and stationary regimes. We show that in both regimes, the performance of P2P systems exhibits a favorable scaling with the offered load. P2P systems achieve this by efficiently leveraging the service capacity of other peers, who possibly are concurrently downloading the same file. Therefore to improve the performance, it is important to design mechanisms to give peers incentives for sharing cooperation. One approach is to introduce mechanisms for resource allocation that are 'fair', such that a peer's performance improves with his contributions. We find that some intuitive 'fairness' notions may unexpectedly lead to 'unfair' allocations, which do not provide the right incentives for peers. Thus, implementation of P2P systems may want to compromise the degree of 'fairness' in favor of maintaining system robustness and reducing overheads. Peer-to-peer systems are typically designed around the assumption that all peers will willingly contribute resources to a global pool. They thus suffer from freeloaders, that is, participants who consume many more resources than they contribute. In this paper, we propose a general economic framework for avoiding freeloaders in peer-to-peer systems. Our system works by keeping track of the resource consumption and resource contribution of each participant. The overall standing of each participant in the system is represented by a single scalar value, called their karma. A set of nodes, called a bankset, keeps track of each node’s karma, increasing it as resources are contributed, and decreasing it as they are consumed. Our framework is resistant to malicious attempts by the resource provider, consumer, and a fraction of the members of the bank set. We illustrate the application of this framework to a peer-to-peer filesharing