We apply Bayesian inference to estimate model parameters and compare insights across the two datasets. We employ two datasets, one of student subjects designing an automotive engine and the other of NASA engineers designing a spacecraft. A stochastic network-behavior dynamics model quantifies the co-evolution of design interdependence, discipline-specific interaction decisions, the changes in system performance. We present an approach that combines predictive capabilities of computational modeling with contextual information from empirical data. In that context, we investigate how to generate behavioral insights to inform effective structuring of interdisciplinary interactions in engineering systems design. Understanding this co-evolution can lead to behavioral insights, resulting in efficient communication pathways and eventually better designs. Engineering systems design is a dynamic socio-technical process where the social factors such as interdisciplinary interactions and technical factors such as design interdependence and the state of the design co-evolve.
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