2016 Methodology of Mathematical and Computational Analysis II

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Technology and Innovation Management
Kajikawa Yuya  Nakamaru Mayuko 
Class Format
Lecture / Exercise     
Media-enhanced courses
Day/Period(Room No.)
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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.


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


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.

Page Top