FACULTY OF ARTS AND SCIENCES
Department of Mathematics
SOCIAL MEDIA
IE 342 | Course Introduction and Application Information
| Course Name |
Decision Theory
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 342
|
Fall/Spring
|
3
|
0
|
3
|
5
|
| Prerequisites |
|
|||||||
| Course Language |
English
|
|||||||
| Course Type |
Elective
|
|||||||
| Course Level |
First Cycle
|
|||||||
| Mode of Delivery | - | |||||||
| Teaching Methods and Techniques of the Course | Lecture / Presentation | |||||||
| Course Coordinator | ||||||||
| Course Lecturer(s) | ||||||||
| Assistant(s) | - | |||||||
| Course Objectives | The objectives of this course are to familiarize students with the introductory knowledge on modelling, analysis and solution approaches for decision making situations under uncertainty, under risk, under certainty and in situations with multiple criteria. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course is one of the basic sections of Operations Research, which studies a rational process for selecting the best of several alternatives. The “goodness” of a selected alternative depends on the quality of the data used in describing the decision situation. From this standpoint, a decisionmaking process can fall into one of three categories. 1. Decisionmaking under uncertainty in which the data cannot be assigned relative weights that represent their degree of relevance in the decision process. 2. Decisionmaking under risk in which the data can be described by probability distributions. 3. Decisionmaking under certainty in which the data are known deterministically. 4. Decision making in multicriteria environment. The main subjects of the course are the decision situation, decision rule, decision trees, information and the cost of additional information, utility theory, multiobjective problems, solution notions for such problems and methods for calculations efficient solutions for multiobjective problems, goal programming and the methods of analyzing solutions for goal programming problems. |
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Introduction to the Course. Introduction to Decision Theory. Behavioral decision analysis. | |
| 2 | Decision making under certainty. Decision making under uncertainty. Decision making under risk | |
| 3 | Utility Theory. Single attribute utility. Probability-equivalence approach. | |
| 4 | Interpreting utility functions. Utility functions for nonmonetary attributes. | |
| 5 | The axioms of utility. Certainty equivalence approach. | |
| 6 | Attitudes towards risk. Risk premium. Decreasing and constant risk aversion. | |
| 7 | Midterm | |
| 8 | Value of information. | |
| 9 | Expected value of perfect information. | |
| 10 | Expected value of sample information. | |
| 11 | Multicriteria Decision Making. Goal Programming. | |
| 12 | Analytic Hierarchy Process. | |
| 13 | Multiattribute Utility Theory | |
| 14 | Outranking relations. | |
| 15 | Review | |
| 16 | Review |
| Course Notes/Textbooks | Lecture Notes |
| Suggested Readings/Materials | 1. Robert T. Clemen, Terence Reilly, Making Hard Decisions With Decision Tools, Duxbury Thomson Learning, 2001; ISBN13: 9780495015086; ISBN10: 0495015083. 2. Wayne L. Winston, Operations Research. Applications and Algorithms, Duxbury Press, Belmont, California, 1994. |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation |
1
|
5
|
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques |
1
|
30
|
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
30
|
| Final Exam |
1
|
35
|
| Total |
| Weighting of Semester Activities on the Final Grade |
3
|
65
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
35
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
14
|
3
|
42
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
1
|
20
|
20
|
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
20
|
20
|
| Final Exam |
1
|
20
|
20
|
| Total |
150
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics. |
|||||
| 2 | To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics. |
|||||
| 3 | To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials. |
X | ||||
| 4 | To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning. |
X | ||||
| 5 | To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals. |
X | ||||
| 6 | To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project. |
|||||
| 7 | To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge. |
|||||
| 8 | To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field. |
X | ||||
| 9 | To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security. |
|||||
| 10 | To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking. |
X | ||||
| 11 | To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language. |
|||||
| 12 | To be able to speak a second foreign language at a medium level of fluency efficiently. |
|||||
| 13 | To be able to relate the knowledge accumulated throughout the human history to their field of expertise. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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