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- > Methodology of Mathematical and Computational Analysis II
- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Technology and Innovation Management
- Instructor(s)
- Kajikawa Yuya Nakamaru Mayuko
- Class Format
- Lecture / Exercise
- Media-enhanced courses
- Day/Period(Room No.)
- Mon9-10(CIC812)
- Group
- -
- Course number
- TIM.A406
- Credits
- 1
- Academic year
- 2017
- Offered quarter
- 2Q
- Syllabus updated
- 2017/3/17
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course teaches how modeling and simulation help understand innovation management. The aim of this course is to acquire analytical skills which are required to learn and to conduct research on technology management.
Modeling and simulations are good tools to grasp the essence of individuals and organizations. In this course, the students will learn and exercise the methodology of simulations and modeling.
Student learning outcomes
In this course, the students will learn:
1) how to grasp the cause-and-effect relationship in innovation management and make a model to describe it qualitatively and quantitatively.
2) how to analyze models.
3) how to interprete the results properly by comparison with practice.
Keywords
differential equation, numerical analysis, game theory, evolutionary game theory, optimization theory, agent-based simulation
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Give a lecture in every class. Exercises are assigned to students, if necessary.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | innovation management and simulations | learn that simulations and modeling can help understand innovation management |
| Class 2 | Modeling and differential equation | learn how to make a model by means of differential equations |
| Class 3 | Numerical analysis of differential equation | learn how to analyze differential equations numerically |
| Class 4 | Introduction to game theory | learn the application of game theory to reality |
| Class 5 | Introduction to evolutionary game theory | learn the basic evolutionary game theory |
| Class 6 | Introduction to social simulation | learn what social simulation, especially agent-based simulation, is |
| Class 7 | Introduction to optimization theory | learn the basic optimazation theory using simple examples |
| Class 8 | Execises | exercise what the students have learned |
Textbook(s)
Not assigned.
Reference books, course materials, etc.
Not assigned.
Assessment criteria and methods
Report (100%)
Related courses
- TIM.A510 ： Social Simulation I
- TIM.A511 ： Social Simulation II
- TIM.C401 ： Ecosystem Management I
- TIM.C402 ： Ecosystem Management II
- TIM.D401 ： Exercises in Research Literacy I
- TIM.D401 ： Exercises in Research Literacy I
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisite.
