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2023　Social Modeling C

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Academic unit or major
Humanities and social science courses
Instructor(s)
Ohori Kotaro  Anai Hirokazu  Iwashita Hiroaki 
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Mon3-4(M-123(H111))  Thr3-4(M-123(H111))  
Group
-
Course number
LAH.T308
Credits
2
Academic year
2023
Offered quarter
2Q
Syllabus updated
2023/3/20
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course deals with basic methodologies for understanding and solving complex social issues. Specially, this course takes up some mathematical technologies to appropriately take into account human behavior and psychology such as game theory, mechanism design, network analysis, agent-based social simulation, and useful artificial intelligence (AI) technologies for developing social solutions.
This course aims to cultivate the students' abilities to understand the process of social system design consisting of social system modeling, policies and programs design and their evaluation.

Student learning outcomes

Upon completion of this course, students should be able to:
1） State the basic concepts utilized in some theories related to social system design,
2） Model, analyze and design mathematically some situations of an issue in social systems,
3） State a set of processes for social system design.

Course taught by instructors with work experience

✔ Applicable How instructors' work experience benefits the course
Instructors have practically solved real social problems using mathematical technologies.
This course includes some case studies on the problem solving processes.

Keywords

systems approach, artificial intelligence, game theory, mechanism design, network analysis, agent-based social simulation, optimization

Competencies that will be developed

✔ Specialist skills Intercultural skills ✔ Communication skills ✔ Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

Each theme is dealt with over a set of classes (one set : 1 to 3 classes)
Students work on exercise problems during a class. At the end of the class, each student writes and submits a "exercise report" on what he/she learned through a lecture and exercise problems.

Should the number of applicants for this course exceed the limit, a lottery system will be used to determine which students are admitted. Be sure to attend the first class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Guidance, briefing Have a panoramic view of this course
Class 2 Way of thinking on social systems State the basic concepts of social systems
Class 3 Description of decision making situations 1 State the role of game theory
Class 4 Description of decision making situations 2 State how we model decision making situations
Class 5 Applications of artificial intelligence 1 Understand AI applications based on game theory
Class 6 Social network analysis 1 State basic concepts of graph theory for describing social networks
Class 7 Social network analysis 2 State the definition of complex networks and their properties
Class 8 Applications of artificial intelligence 2 Understand AI applications based on decision making models
Class 9 Policy and program design of social systems 1 State the role of mechanism design
Class 10 Policy and program design of social systems 2 Understand and state application examples of mechanism design
Class 11 Applications of artificial intelligence 3 Understand AI applications to analyze social networks
Class 12 Policy and program evaluation of social systems 1 State the role of agent-based social simulation
Class 13 Policy and program evaluation of social systems 2 State the method to analyze simulation results
Class 14 Final examination Achieve a passing score

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

None required.

Reference books, course materials, etc.

Takehiro Inohara, “Rationality and Flexibility,” Keiso-syobo, 2002 (in Japanese)
Takehiro Inohara, “Emotions and Perception,” Keiso-syobo, 2002 (in Japanese)
Shingo Takahashi, “Fundamentals of system science”, Baifukan, 2007 (in Japanese)
Makoto Yokoo, “Fundamentals of auction theory ”, TDU-syuppannkyoku, (in Japanese)
Naoki Masuda, Norio Konno, “Introduction of complex network”, Sangyou-Tosho, 2005 (in Japanese)
Hirokazu Anai, Tsutomu Saito, “Guide book of Combinatorial optimization”, Kodansha, 2015 (in Japanese)
Shingo Takahashi, Yusuke Goto, Kotaro Ohori, “Modeling Social Systems“, Kyoritsu Shuppan, 2022 (in Japanese)

Assessment criteria and methods

Assessment will be based on “exercise reports” written during each class (20% in total) and the final examination (80%).

Related courses

  • LAH.T108 ： Decision Making A
  • LAH.T208 ： Decision Making B
  • LAH.T307 ： Decision Making C
  • LAH.T107 ： Social Modeling A
  • LAH.T209 ： Social Modeling B

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None required

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

Kotaro Ohori, ohori.k.aa[at]m.titech.ac.jp

Other

We want students to know that there are a variety of mathematical approaches to help solve social issues.
This course includes the content of science.

Should the number of applicants for this course exceed the limit, a lottery system will be used to determine which students are admitted. Be sure to attend the first class.

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