ISM5206 Decision AnalysisBahçeşehir UniversityDegree Programs POLITICAL SCIENCE AND INTERNATIONAL RELATIONSGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
POLITICAL SCIENCE AND INTERNATIONAL RELATIONS
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
ISM5206 Decision Analysis Spring
Fall
3 0 3 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: Turkish
Type of course: Non-Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Instructor ÖZLEM KANGA
Course Lecturer(s): Assoc. Prof. SEROL BULKAN
Recommended Optional Program Components: N.A.
Course Objectives: The aim of the course is to introduce the graphical models used in decision analysis and to provide a set of systematic tools to help the decision maker in giving a decision.

Learning Outcomes

The students who have succeeded in this course;
- Recognize the graphical models used in decision analysis.
- Model a given uncertain situation with Bayes networks.
- Compute exact and approximate inferences in Bayes networks.
- Model a given uncertain decision problem with influence diagrams.
- Make inferences in decision networks.
- Compute value of information.

Course Content

Expected Utility, Causal and Bayesian networks, Exact inference in Bayesian networks, Approximate inference in Bayesian networks, Learning Bayesian networks, Influence and decision networks, Value of information

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Probability review
2) Expected Utility
3) Causal and Bayesian networks
4) Building Bayesian models
5) Exact inference in Bayesian networks
6) Exact inference in Bayesian networks
7) Approximate inference in Bayesian networks
8) Approximate inference in Bayesian networks
9) Midterm exam
10) Learning Bayesian networks
11) Influence and decision networks
12) Influence and decision networks
13) Value of information
14) Project presentations

Sources

Course Notes / Textbooks: F.V. Jensen, 2001. Bayesian networks and decision graphs, New York : Springer
References: Robert T. Clemen, 1996. Making Hard Decisions: An Introduction to Decision Analysis, 2nd edition, Duxbury Press

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 4 % 10
Project 1 % 20
Midterms 1 % 30
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 2 28
Presentations / Seminar 1 10 10
Project 1 40 40
Homework Assignments 4 10 40
Midterms 1 15 15
Final 1 20 20
Total Workload 195

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Grasp basic theoretical and conceptual knowledge about the field and relations between them at the level of practice.
2) Possess basic knowledge about the causes and effects of political transformations in societies.
3) Possess knowledge about quantitative, qualitative and mixed research methods in social and behavioral sciences.
4) Recognize historical patterns while evaluating contemporary political and social developments.
5) Demonstrate interdisciplinary and critical approach while analyzing, synthesizing and forecasting domestic and foreign policy.
6) Conduct studies in the field professionally, both independently or as a team member.
7) Possess consciousness about lifelong learning based on Research & Development.
8) Communicate with peers both orally and in writing, by using a foreign language at least at a level of European Language Portfolio B1 General Level and the necessary informatics and communication technologies.
9) Apply field-related knowledge and competences into career advancement, projects for sustainable development goals, and social responsibility initiatives.
10) Possess the habit to monitor domestic and foreign policy agenda as well as international developments.
11) Possess competence to interpret the new political actors, theories and concepts in a global era.
12) Evaluate the legal and ethical implications of advanced technologies on politics.