• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта

Бакалаврская программа «Социология и социальная информатика»

Computer Modeling of Social Processes

Учебный год
Обучение ведется на английском языке
Курс по выбору
Когда читается:
4-й курс, 1, 2 модуль


Course Syllabus


Computers are powerful modeling tools, and they offer us a flexible virtual world where we can create nearly anything imaginable. Of course, there are many types of computer models, from basic spreadsheets to complex simulation modeling tools that help experienced users explore complex processes and apply dynamic systems and multi-agent systems (MAS) – systems of distribution artificial intelligence (AI). This course focuses on a broad introduction to modern computer modeling paradigms and simulation systems. The course explores/studies computer modeling from two perspectives – theoretical and practical. At the theoretical level, participants will deal with the computer modeling discourse – methodology, a rich conversation of competing ideas, debate, and case-studies. At the practical level, students will examine a systematic overview about modeling paradigms used throughout the social processes analysis: system dynamics, discrete-event as well as agent based approaches.
Learning Objectives

Learning Objectives

  • The main goal of the course is to provide participants with a broader understanding of computer modeling procedures and tools, including its application in social science and social data analysis. This course also focuses on a broad introduction to machine learning, data mining, and statistical pattern recognition.
Expected Learning Outcomes

Expected Learning Outcomes

  • to get knowledge about the course structure and the main goal, indicative assessment methods and strategy
  • be introduced to the main paradigms of modern computer modeling and to basic concepts of discrete simulation, system dynamics, agent modeling;
  • get knowledge about how to use computer modeling in social science researches and to apply agent-based modeling for social data analysis;
  • learn to design computer models of systems existing in reality;
  • to work out of models of complicated systems referring to different areas of social researches, to use computer modelling as a tool of their work in practice and science.
  • be taught methods of how to present their results of simulation experiments.
  • get knowledge about how to use the advantages of AI systems in social science researches
Course Contents

Course Contents

  • Part 1. Course Introduction.
  • Part 1. Computer modeling: the current state, role and the evolution tendency.
  • Part 1. New modeling paradigms for social sciences and data analysis
  • Part 2. Simulation systems and applications of simulation modelling in social science.
  • Part 2. Computer models design and simulation experiments
  • Part 2. Artificial Intelligence and systems in social processes analysis
  • Part 2. Review of the course.
Assessment Elements

Assessment Elements

  • non-blocking Labs with computer (exercises), quizzes, panel discussion, tests, and presentations
    System dynamics, discrete-event as well as agent based modeling approaches for social processes and data analysis
  • non-blocking A group research project
    A group research project including the production of a report (10%) and group presentation (10%) - 20%. The points obtained will allow students to receive higher grades - 9 and 10 points, depending on the number of tasks completed.  The major academic purpose of the Research Group Project is to challenge participants to apply theories, concepts, models and tools from the classes and from their previous academic background and to learn additionally
  • non-blocking Exam
    Exam is held as a written test based on all course issues and materials. Students have to show their knowledge or ability in a particular subject, or to obtain a qualification.
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    0.5 * Labs with computer (exercises), quizzes, panel discussion, tests, and presentations + 0.2 * A group research project + 0.3 * Exam


Recommended Core Bibliography

  • Bala BK, Arshad FM, Noh KM. System dynamics. Springer Texts in Business and Economics. 2017.
  • Bernard Marr, & Matt Ward. (2019). Artificial Intelligence in Practice : How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley.
  • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
  • Davide Secchi, Martin Neumann. Agent-Based Simulation of Organizational Behavior. New Frontiers of Social Science Research. 2016. Springer. https://proxylibrary.hse.ru:2184/search?query=organizational+behavior
  • Eric Silverman. (2018). Methodological Investigations in Agent-Based Modelling: With Applications for the Social Sciences. Web server without geographic relation, Web server without geographic relation (org): Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.36B944A9
  • Harvard Business Review Press. (2019). Artificial Intelligence : The Insights You Need From Harvard Business Review. La Vergne: Harvard Business Review Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2003692
  • Hiroki Sayama - Introduction to the Modeling and Analysis of Complex Systems - CCBY4_055 - Open Educational Resources: libretexts.org - 2022 - 390841 - https://ibooks.ru/bookshelf/390841/reading - iBOOKS
  • Jordi Cabot, Cristina Gómez, Oscar Pastor, Maria Ribera Sancho, & Ernest Teniente. (2017). Conceptual Modeling Perspectives. Springer.
  • Klein, D., Marx, J., & Fischbach, K. (2018). Agent-Based Modeling in Social Science, History, and Philosophy. An Introduction. Historical Social Research, 43(1), 7–27. https://doi.org/10.12759/hsr.43.2018.1.7-27
  • Wilensky, U., & Rand, W. (2015). An Introduction to Agent-Based Modeling : Modeling Natural, Social, and Engineered Complex Systems with NetLogo. Cambridge, Massachusetts: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=976350
  • Боев, В. Д.  Моделирование в среде AnyLogic : учебное пособие для вузов / В. Д. Боев. — Москва : Издательство Юрайт, 2023. — 298 с. — (Высшее образование). — ISBN 978-5-534-02560-6. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/514023 (дата обращения: 28.08.2023).

Recommended Additional Bibliography

  • Applied stochastic modelling, Morgan, B. J. T., 2009