Toma de decisiones inteligentes bajo incertidumbre

Abstract

Large Scale Group Decision Making (LSGDM) facilitates the resolution of real problems involving a much larger number of decision-makers than in classical GDM. However, increasing the number of decision- makers who take part in the decision process leads to an increment in its complexity. Therefore, it is necessary to develop new models, methods, and tools to improve, analyze and solve LSGDM problems. All this implies new challenges such as nonlinear modeling of preferences, LSGDM processes with hundreds or thousands of experts, and the need for objective metrics to evaluate the performance of consensus processes. The research of this Ph.D. thesis seeks to improve LSGDM processes by using mathematical tools and models, with the aim of achieving the above challenges and increasing the accuracy and robustness of LSGDM models.