This paper demonstrates a novel approach that combines generative models of organizational dynamics and sensor network data with a stochastic method. Generative models specify how organizational performance is related to who interacts with whom and who performs what. Sensor network data track who interacts with whom and who performs what within an organization, and the stochastic methodology fits multi-agent models to data through the Monte Carlo method. The data set used in this paper documents how employees in a data service center handle tasks with different difficulty levels -- tracked with sociometric badges for one month -- and documents links between performance and behavior. This paper demonstrates the potential for improving organizational dynamics with body sensor network data, and therefore also shows the need to systematically benchmark differential organizational dynamics models on data sets for different types of organizations.