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This document focuses on modeling a complex situations to achieve anadvantage within a competitive context. Our goal is to devise thecharacteristics of games to teach and exercise non-easily quantifiable taskscrucial to the math-modeling process. A computerized game to exercise themath-modeling process and optimization problem formulation is introduced. Thegame is named The Formula 1 Championship, and models of the game were developedin the computerized simulation platform MoNet. It resembles some situations inwhich team managers must make crucial decisions to enhance their racing cars upto the feasible, most advantageous conditions. This paper describes the game'srules, limitations, and five Formula 1 circuit simulators used for thechampionship development. We present several formulations of this situation inthe form of optimization problems. Administering the budget to reach the bestcar adjustment to a set of circuits to win the respective races can be anapproach. Focusing on the best distribution of each Grand Prix's budget andthen deciding how to use the assigned money to improve the car is also theright approach. In general, there may be a degree of conflict among theseapproaches because they are different aspects of the same multi-scaleoptimization problem. Therefore, we evaluate the impact of assigning thehighest priority to an element, or another, when formulating the optimizationproblem. Studying the effectiveness of solving such optimization problems turnsout to be an exciting way of evaluating the advantages of focusing on one scaleor another. Another thread of this research directs to the meaning of the gamein the teaching-learning process. We believe applying the Formula 1 Game is aneffective way to discover opportunities in a complex-system situation andformulate them to finally extract and concrete the related benefit to thecontext described.

Complementing the Linear-Programming Learning Experience with the Design and Use of Computerized Games: The Formula 1 Championship Game