A game-theory modeling approach to utility and strength of interactions dynamics in biomedical research social networks

Abstract

Purpose Collaboration has become a cornerstone in biomedical research today. In contrast to physics which has a long history and experience in collaborative projects, biology is only recently becoming an evermore collaborative discipline. In this article we explore the effect of a collaboration network on the distribution of players having access to certain amount of resources from other players in the network and the distribution of the strength of interactions among them. We are interested in how they affect each other in the context of a network of scientific collaboration under the idea that while researchers are interested in maximizing their utilities, they also know that it is important to invest in building collaborative relationships. Methods We implemented two games played simultaneously: one for maximizing individual utility based on the iterated prisoner’s dilemma; the other, a coordination game for maximizing the connection strength between players. We tested our simulation on a biomedical research community network in México and compared the results with Erdös–Renyí, a Watts–Strogatz small-world and Barabási–Albert topologies. Results Different topologies display different global utility and global strength of interaction distributions. Moreover, the distribution of utility and strength of interaction in the researchers network is similar to that of Barabási–Albert and Watts–Strogatz topologies, respectively. Conclusions Data related to Science, from co-authorships to Scientists' movility are increasingly becoming available. We think that the readiness of these sort of data is a great opportunity for scientists interested in the social dynamics of science, especially in the context of computational social science.

Collusion-resistant multiparty data sharing in social networks

“…The strength of interaction (SoI) is commonly used in ecological research to understand the interactions between different organisms. Garcia et al [23] used the strength of interaction between different researchers to assess the effect of a collaborative network in biomedical research. In this study, the strength of interaction between researchers was given by the number of times they have worked together on a project.…”

Variations in power of opinion leaders in online communication networks

“…Once converted, Social network analysis can be employed as a tool for the exploration and analysis of large-scale unstructured data [ 7 ]. Social network models can vary significantly including applications as varied as biomedical research networks [ 8 ], vehicular social networks [ 9 ] and biological networks [ 10 ]. However, previous work has noted the variations in measurement of intelligence used for analysing complex data [ 11 ].…”