Decision Analysis (IE 405)

2021 Spring
Faculty of Engineering and Natural Sciences
Industrial Engineering(IE)
3
6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Kemal Kılıç kkilic@sabanciuniv.edu,
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English
Undergraduate
MATH306
Formal lecture,Recitation
Communicative
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Programs\Type Required Core Elective Area Elective
BA- Political Science
BA-Cultural Studies
BA-Cultural Studies
BA-Economics
BA-Economics
BA-International Studies
BA-International Studies
BA-Management *
BA-Management *
BA-Political Sci.&Inter.Relat.
BA-Political Sci.&Inter.Relat.
BA-Social & Political Sciences
BA-Visual Arts&Visual Com.Des.
BA-Visual Arts&Visual Com.Des.
BS-Biological Sci.&Bioeng.
BS-Computer Science & Eng. *
BS-Computer Science & Eng. *
BS-Electronics Engineering
BS-Electronics Engineering
BS-Industrial Engineering *
BS-Manufacturing Systems Eng. *
BS-Materials Sci. & Nano Eng. *
BS-Materials Science & Eng. *
BS-Mechatronics *
BS-Mechatronics *
BS-Microelectronics
BS-Molecular Bio.Gen.&Bioeng
BS-Telecommunications *
Business Analytics
Decision and Behavior
Energy

CONTENT

Introduction to the theory and practice of decision processes under uncertainty; use of decision trees and influence diagrams in solving decision-making problems; assessing probabilities in modeling uncertainty; Bayesian statistical analysis; value of information; attitudes towards risk; and utility theory.

OBJECTIVE

The course provides a broad practical overview of topics and techniques in the field of decision analysis. As an engineering course for undergraduate students, the course will address advanced technical subjects that can be found in management science and operations research domains. At the end of the term, the students will be able to formulate decision making problems that have multiple decisions in time, uncertain events, and conflicting objectives. We will also discuss certain behavioral issues related to decision making.

LEARNING OUTCOME

Describe the objectives, alternatives and uncertainties in a decision problem
Model and solve decision problems using decision trees.

Conduct sensitivity analysis to understand the important variables in the decision problem.
Explain the concepts of strategy, risk profiles and dominance.
Apply Bayes' formula, and calculate the values of perfect and imperfect information.
Assess discrete and continuous probability distributions using subjective methods, approximate methods, as well as theoretical models.
Apply single and multi-attribute utility models.
Implement the Analytical Hierarchy Process (AHP).
Describe the fundamental decision heuristics and related biases.
Discuss the fundamental concepts and trade-offs in decision analysis.

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 40
Midterm 50
Participation 5
Other 5

RECOMENDED or REQUIRED READINGS

Textbook

Making Hard Decisions with Decision Tools, 2nd Edition by Robert T. Clemen, Duxbury, 2003