Abstract
Computational models of human processes are used for many different purposes and in many different types of applications. A common challenge in using such models is to find suitable parameter values. In many cases, the ideal parameter values are those that yield the most realistic simulation results. However, there are situations in which the goodness of fit is not the main or only criterion to evaluate the appropriateness of a model, but where other aspects of the model behavior are also relevant. This is often the case when computational models are employed in real-life applications, such as mHealth systems. In this paper, we explore how parameter tuning techniques can be used to analyze the behavior of computational models systematically and to investigate the reasons behind the observed behavior. We study a computational model of psychosocial influences on physical activity behavior as an in-depth use case. In this particular case, an important measure of the feasibility of the model is the diversity in the simulation outcomes. This novel application of parameter tuning techniques for analysis and understanding of model behavior is transferable to other cases, and is therefore a valuable new approach in the toolset of computational modelers.
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Mollee, J.S., Araújo, E.F.M., Klein, M.C.A. (2017). Exploring Parameter Tuning for Analysis and Optimization of a Computational Model. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_36
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