Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	361887	Decision Analysis	1	3.0	3	★	SHEAU-FARN LIANG	4IEM3A 4IEM3B	48	0	工管三甲乙合開

Course Plan：

Instructor	SHEAU-FARN LIANG　Office Hours
Email	maxliang@ntut.edu.tw
Last Updated	2026-08-10 14:11:59
Course Objective	This course provides an overview of the basic concepts of decision theory and their applications on the real-world decision problems. Students will learn about the identification and modeling of decision problems, the modeling and estimation of uncertainties, and the modeling and evaluation of preferences.
Course Schedule (Week 1-16)	Module 1 Decision (W1~5) 1: Introduction 2: Elements of decision problems 3: Structuring decisions 4: Making choices 5: Sensitivity analysis Module 2 Uncertainty (W6~8, W9: mid-term exam, W10~11) 1: Probability basics 2: Subjective probability 3: Theoretical probability models 4: Using data 5: Monte Carlo simulation/Value of Information Module 3 Preference (W12~15, W16: final exam) 1: Risk attitudes 2: Utility axioms, paradoxes, and implications 3: Conflicting objectives I 4: Conflicting objectives II
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) Content Team project & report on decision making Hours 6 Learning Outcomes Submitting a team report of no more than 10 pages in PowerPoint format through the class network. Assessment Percentage 10%
Evaluation and grading policy	Mid-term exam: 25% Final exam: 25% Homework and Participation: 40% Team report of flexible learning: 10%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Making hard decisions with DeccisionTools (2001) by Clemen and Reilly, Duxbury (雙葉)
The access to curricular consultation	maxliang@ntut.edu.tw
Expanding teaching and resources	● 無 (None)
The course corresponds to the SDGs	無（None）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note