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# Economic Decisions in Engineering

Classical decision theory, deterministic decision rules, decision trees, influence diagrams, single/multiple stage analysis, sensitivity analysis; subjective probability, heuristics and biases, Bayesian methods, conjugate belief forms, inference, belief assessment methods, value of information, risk analysis; utility theory, risk aversion, conflicting objectives, multi-attribute decision theory, analytic hierarchy process.

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## Learning Objective:

We will learn decision making and rationality, including decision analysis; decision making under uncertainty; various descriptive and prescriptive models from operations research, economics, and business. Applications are drawn from engineering decision-making, public policy, and personal decision-making. Attention is also paid to designing aids to improve decision-making. Use of risk analysis software is optional (not required).

## Description:

Classical decision theory, deterministic decision rules, decision trees, influence diagrams, single/multiple stage analysis, sensitivity analysis; subjective probability, heuristics and biases, Bayesian methods, conjugate belief forms, inference, belief assessment methods, value of information, risk analysis; utility theory, risk aversion, conflicting objectives, multi-attribute decision theory, analytic hierarchy process.

Spring 2021 Syllabus

## Topics Covered:

Classical decision theory, deterministic decision rules, decision trees, influence diagrams, single/multiple stage analysis, sensitivity analysis; subjective probability, heuristics and biases, Bayesian methods, conjugate belief forms, inference, belief assessment methods, value of information, legal reasoning, risk analysis; utility theory, risk aversion, conflicting objectives, multi-attribute decision theory, analytic hierarchy process.

## Prerequisites:

Background in probability theory and familiar with general economics concepts.

## Applied / Theory:

50 / 50

https://mycourses.purdue.edu/

## Web Content:

Syllabus, grades, lecture notes, homework assignments, solutions.

## Homework:

Seven to eight homeworks.

None.

## Exams:

Two midterms and one final exam

## Textbooks:

### Required:

Robert T. Clemen. Making Hard Decisions: An Introduction to Decision Analysis, 2nd edition, Duxbury Press

### Reference (not required):

• Ronald A. Howard and Ali E. Abbas. Foundations of Decision Analysis. Pearson Higher Ed, 2015
• Hammond, John S., Ralph L. Keeney, and Howard Raiffa. Smart Choices: A Practical Guide to Making Better Decisions. Harvard Business Review Press, 2015.
• Reference book on probability - Sheldon Ross. First Course in Probability. (Does not matter which edition you use.)

## Computer Requirements:

ProEd minimum computer requirements.

None.