Micro to macro. Social processes and individual behaviour. 3500-FAKANG-MM-OG

Course Overview
This course explores the complexity of social processes and the mechanisms that drive
them. Students will investigate questions such as:
- Why are social processes hard to predict?
- How can we estimate the size of a protest before it happens?
- How do diseases, fake news, and good ideas spread?
- Why do people conform to group norms, even when they are harmful?
- How does social polarisation occur?
- Why are we growing apart—and is there a way out?
Although many social processes seem simple on the surface, they often involve unintended
collective consequences of individual behaviours. The course aims to help students
understand why social phenomena appear unpredictable and to introduce the fundamental
principles of social dynamics. Emphasis is placed on social mechanisms and the use of
models and simulations to explore them.
Course Structure
The course conceptualises society as a system of interacting elements whose dynamics
generate new emergent properties at the aggregate level. It is organised into three modules:
Module 1: Introduction to Social Dynamics
We begin with an illustrative case of a protest in a small town to introduce key concepts of
social dynamics, modelling, and simulation. Students will construct a simple threshold-based
protest model and develop it throughout the module. Various extensions, interpretations, and
complexities will be explored to highlight the connections between individual decisions and
societal outcomes.
Module 2: Social Network Analysis
This module provides a step-by-step introduction to Social Network Analysis. Students will
analyse how information spreads through networks and how individual decisions both
influence and are influenced by social structures.
Module 3: Social Polarisation and Computational Analysis
The final module integrates the concepts covered in the course and applies computational
tools to the study of social polarisation—a process that creates deep divisions within
contemporary societies and poses challenges for democratic decision-making.
Learning Outcomes
By the end of the course, students will be able to:
- Explain how individual behaviours lead to unexpected societal outcomes.
- Discuss how models help decode social processes.
- Compare social processes across different contexts.
- Demonstrate modelling using pen-and-paper and computer simulations.
- Analyse how the social environment shapes individual choices and vice versa.
- Apply a decision-making model to a real-world scenario.
- Experiment with decision processes and their social impacts.
- Compare network structures and assess their effects on social dynamics.
- Examine the role of networks in spreading influences (e.g., norms, viruses, products).

- Identify and evaluate signs of polarisation.
- Understand key cognitive and social drivers of polarisation (e.g., confirmation bias,
filter bubbles).
- Describe the impact of polarisation on democratic processes.
- Propose strategies for managing polarisation, from prevention to reconciliation.
Workload and Time Allocation
- Online tasks: 15 weeks, approximately 2–4 hours per week
- Preparation for classes and reading: 30 hours
- Preparation for final test: 15 hours

Rodzaj przedmiotu

ogólnouniwersyteckie

Tryb prowadzenia

zdalnie

Założenia (opisowo)

No prior knowledge of advanced mathematics or programming skills required. The course is dedicated for students interested in social phenomena. Participants must be able to read and communicate in English

Koordynatorzy przedmiotu

Katarzyna Abramczuk
Anna Baczko-Dombi
Agata Komendant-Brodowska

Efekty kształcenia

W_01 – The student understands the basic concepts of social dynamics and simulation.

U_01 – The student is able to build simple models of social processes.

U_02 – The student is able to apply simulation tools and network analysis in social research.

U_03 – The student is able to analyze processes of polarization and information diffusion in social networks.

K_01 – The student systematically develops digital and research competencies in individual and team work.

Kryteria oceniania

The course takes place on the "Kampus" platform and is conducted in an

e-learning format.
Educational materials are divided into modules and then into weeks
(units) consisting of a series of small steps:
 short articles - max.1000 words, usually followed by a discussion
prompt,
 short videos - max. 6 minutes (links are included in the text),
 discussion questions,
 exercises (if they relate to models, links are included in the text),
 quizzes (to check student understanding)
 In each module, there are some NetLogo models that were
designed for the course or adjusted to its needs. They do not require
prior mathematical or IT knowledge.
The course is graded based on a point system and has a strong self-study
component – students will participate in various tasks, quizzes, and
online discussions and collect points.
The course will be delivered on the "Kampus" platform (Moodle platform)
and conducted in an e-learning format. The course will require students
to engage in independent work, including completing assignments, taking
part in quizzes, and participating in discussions on the platform. Students
should be aware that in order to obtain a good grade it will require
systematic work and completing various tasks within a given time period.
The tasks will include: quizzes or exercises, reading selected materials and
engaging in discussions with other course participants.
The course will be graded based on regular completion of assignments
throughout the course (80%) AND passing a final test at the end of the
course (20%).

The course is based on educational materials from two international
Erasmus+ projects:
ACTiSS (actiss-edu.eu) – Action for Computational Thinking in Social
Sciences was an international educational project aimed at fostering the
development of computational thinking among social science students
and young professionals, it was carried out 2018-2022 and it was
coordinated by the University of Warsaw.
ACTIPLEX (socialpolarisation.eu) - Action for Interactive Anti-Polarisation
Learning Experiences for a Better Democracy is an international
educational project whose aim is to combat social polarization among
young people by educating about this process and its mechanisms and
raising awareness of the risks associated with it. In order to achieve that
goal the partnership of 4 institutions from 4 countries is developing a set
of interactive learning experiences, combining digital and analogue tools:
a massive open online course, online courses on university platforms and
a game-based workshop to be used within university and secondary
schools classrooms.

Literatura

Course materials are based on a wide range of lectures and bibliography.
Due to the online character of the course all lectures are going to be
incorporated into course materials.
Additionally, a list of readings expanding on the discussed topics will be
proposed during the course. Examples of selected readings:
Chen, Yijing, et al. "A “Broken Egg” of US Political Beliefs: Using Response-
Item Network (ResIN) to Measure Ideological Polarization."
Gigerenzer, Gerd, Gaissmaier, W. 2011. Heuristic decision making.
“Annual review of psychology” 62: 451-482.
Henrich, Joseph, Robert Boyd, Samuel Bowles, Colin Camerer, Ernst Fehr,
Herbert Gintis, and Richard McElreath. 2001. "In Search of Homo
Economicus: Behavioral Experiments in 15 Small-Scale Societies."
American Economic Review, 91 (2): 73-78.
Kahneman, Daniel. 2003, Maps of bounded rationality: Psychology for
Behavioral Economics. “The American Economic Review” 93(5): 1449-
1475.
Simon, Herbert A. 1990. Invariants of Human Behaviour. “Annual Review
of Psychology” 41: 1-19.
Cioffi-Revilla, C. (2014). Introduction to computational social science.
Principles and applications. London, UK: Springer-Verlag (lub inna praca
Cioffi-Revilli)

Jackson, M. O. (2010). Social and economic networks. Princeton
university press. (fragmenty)
Squazzoni, F., Jager, W., & Edmonds, B. (2014). Social simulation in the
social sciences: A brief overview. Social Science Computer Review, 32(3),
279-294.
Li, T., & Jager, W. (2023). How Availability Heuristic, Confirmation Bias
and Fear May Drive Societal Polarisation: An Opinion Dynamics
Simulation of the Case of COVID-19 Vaccination. Journal of Artificial
Societies and Social Simulation, 26(4), Article 2.
https://doi.org/10.18564/jasss.5135

Więcej informacji

Więcej informacji o poziomie przedmiotu, roku studiów (i/lub semestrze) w którym się odbywa, o rodzaju i liczbie godzin zajęć - szukaj w planach studiów odpowiednich programów. Ten przedmiot jest związany z programami:

Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb:

Strona przedmiotu 3500-FAKANG-MM-OG w USOSweb