Publication
This thesis tackles new challenges associated with the disaggregate modeling of the human behavior. Decision-aid tools help in making decisions, by providing quantitative insights on the decisions and associated consequences. They are useful in complex situations where human actors are involved. Inside decision-aid tools, there is a need for explicitly capturing and predicting the human behavior. The prediction of human actions is done through models. Models are simplified representations of the reality, which provide a better understanding of it and allow to predict its future state. They are often too simplistic, with bad prediction capabilities. This is an issue as they generate the outcome of the decision-aid tools, which influence decisions. Good models are required in order to adequately capture the complexity of human actions. Behavioral models appear to be relevant. They allow to translate behavioral assumptions into equations, which make their strength but also their complexity. They have been mainly used in transportation and marketing. Many advances have been recently achieved. On one hand, emerging technologies allow to collect various and detailed data about the human behavior. On the other hand, new modeling techniques have been proposed to handle complex behaviors. Estimation softwares are now available for their estimation. The combination of these advances open opportunities in the field of the behavioral modeling. The motivations of the proposed work are the investigation of the challenges associated with non-traditional applications of the behavioral modeling, the emphasis of multi-disciplinarity, the handling of the behavioral complexity and the development of operational models. Different applications are considered where these challenges appear. The applications are the investors' behavior, the walking behavior and the dynamic facial expression recognition. Challenges are addressed in the different tasks of the modeling framework, which are the data collection, the data processing, the model specification, estimation and validation. The modeling of the investors' behavior consists in characterizing how individuals are taking financial decisions. It is relevant for predicting monetary gains and regulating the market. We propose an hybrid discrete choice framework for modeling decisions of investors performed on stock markets. We focus on the choice of action (buy or sell) and the duration until the next action. The choice of action is handled with a binary logit model with latent classes, while a Weibull regression model is used for the duration until the next action. Both models account for the risk perception and the dynamics of the phenomenon. They are simultaneously estimated by maximum likelihood using real data. The predictive performance of the models are tested by cross-validation. The forecasting accuracy of the action model is studied more in details. Parameters of both models are interpretable and emphasize interes