シラバス Syllabus

授業名 Behavioral Economics
Course Title Behavioral Economics
担当教員 Instructor Name Qin Dan
コード Couse Code GLP152_G24N
授業形態 Class Type 講義 Regular course
授業形式 Class Format On Campus
単位 Credits 2
言語 Language EN
科目区分 Course Category 基礎科目100系 / Basic
学位 Degree MSc in Management
開講情報 Terms / Location 2024 GSM Nagoya Spring

授業の概要 Course Overview

Misson Statementとの関係性 / Connection to our Mission Statement

This course assists participants to foster a "Frontier Spirit", and helps them to better understand the nature of human behavior, building up a basis for ethical and efficient business decision making.

授業の目的(意義) / Importance of this course

Participants will study important psychological, cognitive, cultural, and social factors that shape the decision maker’s behavior in the economic environment. Participants study models of behavioral patterns that are commonly observed but cannot be incorporated by classical economic theory. We focus on a selection of behavioral economic models that are essential in understanding consumer behavior. We will emphasize intuitions, real-life experience, and experimental results.

到達目標 / Achievement Goal


Upon the completion of the course, participants are expected to be able to:
• Understand factors that can affect consumer behavior.
• Make predictions of consumer behavior by taking into account various factors beyond price and quality.
• Design strategies in the market place based on models of behavioral economics.
• Understand the role of experiments in behavioral economics.

本授業の該当ラーニングゴール Learning Goals

*本学の教育ミッションを具現化する形で設定されています。

LG1 Critical Thinking
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Innovative Leadership (MBA)
LG7 Global Perspective (GLP)

受講後得られる具体的スキルや知識 Learning Outcomes


Participants are expected to better understand the cognitive factors and biases that influence the behavior. They will be able to identify behavioral patterns and predict consumer behavior. They will understand the importance of experiments in business decision making.

SDGsとの関連性 Relevance to Sustainable Development Goals

Goal 4 質の高い教育をみんなに(Quality Education)

教育手法 Teaching Method

教育手法 Teaching Method % of Course Time
インプット型 Traditional 20 %
参加者中心型 Participant-Centered Learning ケースメソッド Case Method 80 %
フィールドメソッド Field Method 0 %
合計 Total 100 %

事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods

Case reading and class discussion are the core parts of this course. Students are expected and required to read the case in advance and actively join the discussion. We will start the class with small group discussions, followed by class discussions. There is no prerequisite. However, a basic understanding of introductory Microeconomics is desirable. Grades are determined on the basis of in-class participation and the final report. Details about the final report will be given on the first day.

授業スケジュール Course Schedule

第1日(Day1)

(Ir)rational behavior and experimental identification
Group and class discussion on the importance and method of understanding behavior. The discussion emphasizes intuition and real-life experience.


●使用するケース
(1) Behavioural Insights Team (A), 915024-PDF-ENG
(2) Behavioural Insights Team (B), 915025-PDF-ENG (in-class reading)
(3) Lemonade: Delighting Insurance Customers with AI and Behavioural Economics - A Disruptive InsurTech Business Model for Outstanding Customer Experience and Cost-Effective Service Excellence, IN1673-PDF-ENG

第2日(Day2)

Limited attention and consideration
Group and class discussion on the limit of cognitive recourses and its implication over consumer behavior
Group work 1

●使用するケース
Original material

第3日(Day3)

Anchoring effect, relativity, and more biases
Group and class discussion on well documented behavioral biases


●使用するケース
(1) Anchoring Expectations, N0409D-PDF-ENG
(2) Microsoft Windows: The Launch of Windows 7, 909A23-PDF-ENG

第4日(Day4)

The power of experiments
Group and class discussion on business experiments
Group work 2

●使用するケース
(1) Booking.com, 619015-PDF-ENG
(2) Dare to Experiment: The Scientific Approach to Consumer Behavior, UV8110-PDF-ENG (in-class reading)

第5日(Day5)



第6日(Day6)



第7日(Day7)



成績評価方法 Evaluation Criteria

*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
講師用内規準拠 Method of Assessment Weights
コールドコール Cold Call 0 %
授業内での挙手発言 Class Contribution 80 %
クラス貢献度合計 Class Contribution Total 80 %
予習レポート Preparation Report 0 %
小テスト Quizzes / Tests 0 %
シミュレーション成績 Simulation 0 %
ケース試験 Case Exam 0 %
最終レポート Final Report 20 %
期末試験 Final Exam 0 %
参加者による相互評価 Peer Assessment 0 %
合計 Total 100 %

評価の留意事項 Notes on Evaluation Criteria

80% class contribution = 40% class discussion +40% group work participation
20% final report = 20% reflection of group works

使用ケース一覧 List of Cases

    ケースは使用しません。

教科書 Textbook

  • 配布資料

参考文献・資料 Additional Readings and Resource

Kahneman “Thinking, Fast and Slow” Penguin Group (2012) 978-0141033570
Thaler “Misbehaving: The Making of Behavioral Economics” W W Norton (2016) 978-0393352795

授業調査に対するコメント Comment on Course Evaluation

I have modified the course structure, reflecting the concerns raised in last year’s course.

担当教員のプロフィール About the Instructor 


2015年早稲田大学経済学研究科博士後期終了。同年に早稲田大学より博士(経済学)を取得。その後、早稲田大学政治経済学部助手、東北大学経済学研究科准教授を経て、2020年より本学に着任。
Dr. Dan Qin is Associate Professor at NUCB. He received his Ph.D. in economics from Waseda University. He joined NUCB in 2020 after working as research associate at Waseda University and associate professor at Tohoku University. His main fields of interest are microeconomic theory and behavioral economics.



Refereed Articles

  • (2024) Differentiating roles of the reference alternative. Games and Economic Behavior
  • (2024) A simple model of two-system choice. Journal of Mathematical Economics
  • (2021) A Note on Numerical Representations of Nested System of Strict Partial Orders. Games
  • (2021) Exclusive shortlisting choice with reference. Economics Letters
  • (2020) A Note on Reference-Dependent Choice with Threshold Representation. The B.E. journal of theoretical economics






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