Decision Modeling

Preface

Making informed decisions is crucial in a world of uncertainty and ever-changing dynamics. This book is a guide that explores decision analysis, simulation, and time series techniques using R. It is recommended for anyone seeking to learn data-driven approaches to predict outcomes, simulate scenarios, and make informed choices.

In the book’s first part, we delve into decision analysis. Here, we explore various frameworks, such as decision trees and risk analysis, enabling us to assess our choices’ potential outcomes and consequences. We uncover strategies to quantify uncertainties, analyze trade-offs, and optimize decision paths. Real-world examples illustrate how these techniques are applied in business scenarios.

The book’s second part deals with simulation. We create dynamic, virtual environments to mimic complex systems in business and evaluate multiple scenarios. Through Monte Carlo simulation, we unlock the power to predict outcomes and quantify the impacts of our decisions. Practical exercises and step-by-step guidance equip readers with the skills to build and analyze simulations, enabling them to gain deeper insights into the consequences of their choices.

In the final part of this book, we introduce time series analysis. As time is fundamental in many domains, we explore techniques to extract meaningful patterns and forecast future values. The classical methods of ARIMA and ETS are introduced by illustrating their real-world applications in business. Readers will learn to navigate the intricacies of forecasting and leverage time series insights to enhance decision-making.

Comments are welcomed at jagelves@wm.edu