IE 342 | Course Introduction and Application Information - Department of Mathematics

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

MATH 240 To succeed (To get a grade of at least DD)

Course Language

English

Course Type

Elective

Course Level

First Cycle

Mode of Delivery

-

Teaching Methods and Techniques of the Course

Lecture / Presentation

National Occupation Classification

-

Course Coordinator

Prof. Dr. Selin ÖZPEYNİRCİ

Course Lecturer(s)

Prof. Dr. Selin ÖZPEYNİRCİ

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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	Will be able to analyze problems faced in certainty, uncertainty and risk environments
2	Will be able to develop decision trees to find rational solutions for problems under uncertainty and risk environments
3	Will be able to calculate the value of information
4	Will be able to use fundamentals of the utility theory
5	Will be able to analyze different solution aspects of multicriteria problems
6	Will be able to use fundamental approaches of goal programming

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.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
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.

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5	LO 6
Participation	1
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	40
Total

Weighting of Semester Activities on the Final Grade	3	65
Weighting of End-of-Semester Activities on the Final Grade	1	35
Total

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

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	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.		-	-	-	-	-