Mathematical Modeling and Statistics in Real-Life Problem Solving: Applications in Prediction and the Job Market
Overview
This essay examines the role of mathematical modeling and statistics in solving real-world problems, particularly through game theory and political prediction models. It discusses how mathematical models require integration with human intelligence for accuracy and explores career opportunities in mathematical modeling, including the Senior Engineer position in PKPD modeling and simulation.
What this essay covers
- Discussion of mathematical modeling and statistics as fundamental problem-solving tools, with focus on Bruce Bueno de Mesquita's game theory applications
- Analysis of the relationship between numerical models and human intelligence in predicting events and verifying data
- Job market exploration examining the Modeling Simulation Analyst career path and a specific Senior Engineer PKPD position in pharmaceutical development
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Discussion: Expanding the Scope
Mathematics and statistics have proven to be fundamental components in solving real-life problems. As the world experiences exponential technology change, modeling is on top of the components used to solve some of the problems (Kahl, 2009). The 2009 article published by Clive Thompson reveals the significance of modeling in predicting the future. Bruce Bueno de Mesquita is a renowned game theorist and a professor who has been using the concept of modeling to predict political events (Thompson, 2009). The numerical analysis and computer modeling controlled the game, reflecting the scope of mathematics in this game of predictions. Bruce Bueno de Mesquita uses real numbers and presents political events mathematically to determine the potential outcome.
These numerical numbers provide discretional predictions, which can help determine the scope of political events in any country. The theory applied here uses the game theory, whereby human relations are used as variables (Thompson, 2009). On the other hand, a conflicting model shows the relationship between numbers and events. Game theories and prediction models are computer-generated, and they rely on numbers and relational behaviors to determine probable events (Kahl, 2009). In most cases, these behaviors can change and may not align with the predictions. Therefore, it is critical to incorporate human intelligence with mathematical models and theories to make discretional opinions. Human intelligence is very critical in analyzing real-life events. Additionally, it plays an integral role in feeding the models with the numbers and players to make predictions.