Introduction to the theory and practice of decision processes under risk; use of decision trees and influence diagrams in solving decision-making problems; assessing subjective probabilities in modeling uncertainty; Bayes' theorem; value of information; attitudes towards risk; utility theory; multi criteria decision making; analytic hierarchy process; auctions.

This course introduces undergraduate engineering students to the principles and methods of decision analysis for making informed decisions under uncertainty. We discuss how to structure complex decision problems involving multiple stages, uncertain outcomes, and competing objectives using graphical and analytical models such as decision trees and influence diagrams. Students explore techniques for analyzing risk preferences and conduct sensitivity analysis. The course develops quantitative skills in probability assessment and Bayesian inference so that students can assess the value of perfect and imperfect information. It also examines behavioral aspects of decision making, including common heuristics and cognitive biases that influence judgment under uncertainty. We cover evaluating trade-offs among conflicting objectives while constructing utility-based decision making models. Furthermore, the students become familiar with the Analytic Hierarchy Process (AHP) and they use a scientific computational approach for Monte Carlo simulation to compare alternatives.. Upon successful completion of the course, students will be able to formulate, analyze, and communicate solutions to complex engineering decision problems using appropriate decision analysis methods.

REQUIRED TEXTBOOK:
▪ Making Hard Decisions with Decision Tools 3rd Edition, Robert T. Clemen and Terence Reilly, South-Western Cengage Learning, 2014.