Social similarity as a driver for selfish, cooperative and altruistic behavior

Abstract

Abstract-This paper explores how the degree of similarity within a social group can be exploited in order to dictate the behavior of the individual nodes, so as to best accommodate the typically non-coinciding individual and social benefit maximization. More specifically, this paper investigates the impact of social similarity on the effectiveness of content dissemination, as implemented through three classes representing well the spectrum of behavior-shaped content storage strategies: the selfish, the self-aware cooperative and the optimally altruistic ones. This study shows that when the social group is tight (high degree of similarity), the optimally altruistic behavior yields the best performance for both the entire group (by definition) and the individual nodes (contrary to typical expectations). When the group is made up of foreigners with almost no similarity, altruism or cooperation cannot bring much benefits to either the group or the individuals and thus, a selfish behavior would make sense due to its simplicity. Finally, the self-aware cooperative behavior could be adopted as an easy to implement distributed scheme -compared to the optimally altruistic one -that has close to the optimal performance for tight social groups, and has the additional advantage of not allowing mistreatment to any node (i.e., the content retrieval cost become larger compared to the cost of the selfish strategy).

Benefits and challenges of multi‐driver simulator studies

“…However, the sample of one test trial should be homogenous or balanced over the test trials in regard to factors possibly influencing the outcome of the interaction scenario. For instance, subjects being more similar will likely be more cooperative as social similarity is an important predictor of cooperative, altruistic behaviour [18]. Gender effects might also have an influence.…”

Putting contacts into context

“…Communities are (informally) defined as subsets of nodes with stronger connections between them than towards other nodes. They generally imply social groups (e.g., friends, co-workers) [26], and are thus of particular interests from both a sociological as well as a protocol design perspective (e.g.. for the design of DTN routing [15,13] and multicast [16], collaboration for content distribution [27], security and trust systems, etc.). Looking at the community structure of the measured traces and from traces obtained from the synthetic models we will find that synthetic mobility models, while able to generate high level community structure, they all fail to accurately capture inter-community linkage.…”

ISCoDe: A framework for interest similarity-based community detection in social networks

“…A major research question is then how could the dynamics of these virtual worlds be exploited for more efficient design of networked communication protocols and which factors may shape the end-user (the network communication subject) behavior. It has been reported, for example, that higher similarity in the interests/preferences of online social group members favors collaborative, and even altruistic, behavior in content replication [10] and content dissemination [1] scenarios. But is such similarity present in social networks, where users tend to select their friends/followers with very different criteria, including acquaintance, social status, educational and family background?…”