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.
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. The popularity of peer-to-peer (P2P) networks makes them an attractive target to the creators of viruses and other malicious code. Recently a number of viruses designed specifically to spread via P2P networks have emerged. Pollution has also become increasingly prevalent as copyright holders inject multiple decoy versions in order to impede item distribution. In this paper we derive deterministic epidemiological models for the propagation of a P2P virus through a P2P network and the dissemination of pollution. We report on discrete simulations that provide some verification that the models remain sufficiently accurate despite variations in individual peer conduct to provide insight into the behaviour of the system. The paper examines the steady-state behaviour and illustrates how the models may be used to estimate in a computationally efficient manner how effective object reputation schemes will be in mitigating the impact of viruses and preventing the spread of pollution.