Towards Parameter Identification of a Behavioral Model from a Virtual Reality Experiment - Nathalie Verdière; Oscar Navarro; Aude Naud; Alexandre Berred; Damienne Provitolo (Mathematics, 2021)
In this paper, we investigate the calibration of a mathematical model describing different behaviors occurring during a natural, a societal, or a technological catastrophe. This model was developed in collaboration with geographers and psychologists. To collect information on the level of stress, psychologists of the LPPL laboratory of Nantes (France) led virtual reality experiments. These experiments consisted in immersing individuals in a situation of catastrophe and measuring their electrocardiogram. From the physical and biological data collected, we present the methodology to calibrate the behavioral model. First, a theoretical analysis is carried out to determine (i) if the parameters can be uniquely estimated, (ii) the minimal number of discrete measurements required for the estimation. Then, from these analyses, an estimation procedure is performed to calibrate the mathematical model or at least to have an order magnitude of the model parameters. Through this work, we will show from simulations that the proposed system makes it possible to apprehend non observable human processes.