DECISION ANALYSIS

AIM

Major objectives of this course include; • Training students to apply statistical models at intermediate level to solve relevant real-world Decision Making problems. • Developing a sense of critical thinking and providing a comprehension of modeling and rational approaches to decision making. • Developing analytical skills in structuring and analysis of decision making problems. • Understanding the use and limitations of mathematics (probability) theory to find solutions to real world problems.

CONTENT

This course contains; Overview of the Course, Introduction to Decision Analysis and Decision Making,Analytic Hierarchy Process,TOPSIS METHOD,VIKOR METHOD,INTRODUCTION TO DECISION ANALYSIS,DECISION TREES and EXPECTED MONETARY VALUE ,RISK PROFILES and DOMINANCE,MAKING DECISIONS WITH MULTIPLE OBJECTIVES ,DECISION MAKING UNDER UNCERTAINTY I,DECISION MAKING UNDER UNCERTAINTY II,Value of information: Value of perfect information,Value of information: Value of imperfect information,TERM PROJECT PRESENTATIONS I,TERM PROJECT PRESENTATIONS II.

1. 1

   Identifies the best decision alternative by evaluating expectations and risk analysis results simultaneously.

   Taught by: Problem Solving Method, Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Oral Exam, Homework, Quiz

2. 2

   Identifies the modelling steps in decision theory and recognizes the related basic concepts.

   Taught by: Question - Answer Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, Quiz

3. 3

   Performs structural modeling of decision problems with the help of decision trees.

   Taught by: Problem Solving Method, Lecture Method · Assessed by: Traditional Written Exam, Homework, Quiz

4. 4

   Substitutes the preferences of decision maker into the decision problem and compares the results due to these objective /subjective preferences.

   Taught by: Problem Solving Method, Lecture Method · Assessed by: Traditional Written Exam, Quiz

5. 5

   Examines and finalises a real world decision problem by applying all stages that take place in a decision process.

   Taught by: Self Study Method, Lecture Method · Assessed by: Project Task

WEEKLY PLAN

1. WEEK 1

   Overview of the Course, Introduction to Decision Analysis and Decision Making

   Preparation: Lecture Notes

2. WEEK 2

   Analytic Hierarchy Process

   Preparation: Lecture Notes

3. WEEK 3

   TOPSIS METHOD

   Preparation: Lecture Notes

4. WEEK 4

   VIKOR METHOD

   Preparation: Lecture Notes

5. WEEK 5

   INTRODUCTION TO DECISION ANALYSIS

   Preparation: Lecture Notes

6. WEEK 6

   DECISION TREES and EXPECTED MONETARY VALUE

   Preparation: Lecture Notes

7. WEEK 7

   RISK PROFILES and DOMINANCE

   Preparation: Lecture Notes

8. WEEK 8

   MAKING DECISIONS WITH MULTIPLE OBJECTIVES

   Preparation: Lecture Notes

9. WEEK 9

   DECISION MAKING UNDER UNCERTAINTY I

   Preparation: Lecture Notes

10. WEEK 10

    DECISION MAKING UNDER UNCERTAINTY II

    Preparation: Lecture Notes

11. WEEK 11

    Value of information: Value of perfect information

    Preparation: Lecture Notes

12. WEEK 12

    Value of information: Value of imperfect information

    Preparation: Lecture Notes

13. WEEK 13

    TERM PROJECT PRESENTATIONS I

14. WEEK 14

    TERM PROJECT PRESENTATIONS II

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

WORKLOAD

READING

• Making Hard Decisions: An Introduction to Decision Analysis by Robert T. Clemen& T. Reilly South –Western Cengage Learning Academic Press. ISBN 0-495-01508
• W. L. Winston, Operations Research: Applications and Algorithms, Thompson Brooks/Cole, 2004. H. A. Taha, Operations Research: An Introduction, Pearson Education, 2007.

TEACHING STAFF

• Assoc.Prof. Melis Almula KARADAYICOORDINATOR
• Assoc.Prof. Melis Almula KARADAYI