Introduction to fundamental concepts such as the definition of decision problems and the classification of decision models (certainty, uncertainty, risk; individual decisions vs. strategic interactions). Translation of real-world problems into mathematical models. Analysis of economic choice models, with particular focus on consumer theory and firm theory (cost minimization, profit maximization, production, and returns to scale). Study of decision-making under uncertainty and risk, including decision trees and methods for evaluating alternatives in such contexts. In-depth exploration of game theory and strategic interactions, focusing on two-player games and non-cooperative games, pure and mixed strategies, Nash equilibrium (definition, examples, and applications), as well as multiplayer games, repeated games, evolutionary games, and Bayesian games. Practical applications and software development in Python for implementing models, including writing functions, solving numerical problems, and graphical representation, with special attention to solving evolutionary, repeated, and sequential games. Finally, critical evaluation and comparison of the proposed models.