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A Computational Model of Social Capital in Virtual Communities = 59.6%) low level of trust and correspondingly low level of social capital (P (SC=high) = 35.1). This is expected since core variables of social capital critical to distributed communities of practice such as shared understanding and profes- sional awareness were absent. CHALLENGES In theory computational models are expected to be fully verified and valid but in practice, no computational model will ever be fully verified, guaranteeing 100% error free accuracy. But a high degree of statistical certainty is certainly still required to achieve for a model to deliver useful insights and knowledge. One of greatest challenges of building a com- putational model is making it valid, relevant and useful, which implies in most part establishing model credibility. This requires gathering empiri- cal data, subjecting the model to undergo several rigorous verification and validation stages. It also requires establishing an argument that the model has produced sound insights and sound data based on a wide range of tests, which are comparable to data in real world settings. The development of the social capital model presented in this book did not passed rigorous model validation leading to statistically significant results. Since like in many social systems, model- ling social issues is not so much about gain 100% error free models but about insights required to understanding and solving problems. In addition, most of the approaches used for building models of social systems make use of qualitative infer- ences rather than quantitative predictions about the future state of systems. The input variables used for building the model of social capital were extracted from what exists in the literature, which might not necessarily be empirically based, or situated within virtual com- munities, though variables such as awareness were based on understanding of social capital in virtual communities. Furthermore, research into social capital in virtual communities is still premature and more needed to be done in terms of understanding the nature of independent variables constituting these communities and how they causally relate to each other. There are two ways to construct Bayesian models, one is to learn a graphical structure from data and the one is to initially propose a graphical structure based on some logical reasoning and train the graph to learn probability values from the structure using new evidences. The latter is the ap- proach used to build the social capital graph. This approach is not necessarily consistent all the time. There is a need to run a number of experiments to further validate and refine the structure, which in this case was minimally done. The sensitivity analysis conducted on the structure though has proved the logic used for building the model and some of the findings from the synthesis of what constitute social capital, it will be more interest- ing to reconfigure the structure and re-run several sensitivity analysis and observe the variability of the degree of influence of all the variables on social capital and social capital to each other its own components. These procedures though useful are not neces- sary to do since the overall goal of the model is to demonstrate a procedure for modelling social capital rather than building a final model of social capital, which though possible requires further work and perhaps in the second edition of this book. CONCLUSION The ultimate goal of this chapter was to dem- onstrate a working example of a computational model of social capital in virtual communities. It is also intended provide guidance to researchers and practitioners interested in exploring social issues in virtual communities. Moreover issues predicted by the model are intended to open up debate about social capital in virtual communi- 328