Micro to macro. Social processes and individual behaviour. 3500-FAKANG-MM
Why are social processes hard to predict?
How can we estimate the size of the protest before it happens?
Why do our decisions sometimes seem irrational?
How do we spread disease, fake news and good ideas?
Why do people abide by group norms, even when they are
harmful?
How does social polarisation happen?
Why are we growing apart and is there a way out?
During the course students will seek answers to such questions using
various models that describe the social consequences of individual
behaviors. Many seemingly simple social processes are far more complex
than they initially appear.
The main aim of this course is to help students understand why social
processes seem so unpredictable and better understand the basics of
social dynamics. Throughout, students will focus on social mechanisms
and will explore how models and simulations can help to understand
them.
This course will propose a perspective on society as a system of
interacting elements, where its dynamics lead to new emergent
properties at the aggregate level. Thus, it will be an attempt to approach
social problems and methods for solving them from a slightly different
angle.
The course will start from introducing basic concepts of social dynamics,
modelling and simulations. We start with asking the question about
difficulties with predicting social behaviour and then we build a simple
model of organising a protest (based on a simple threshold model). This
story together with the model will serve as an opportunity to introduce
the main concepts of social dynamics and to explore how models of social
processes help understand the mechanisms within these processes.
Then we will go deeper and - during more detailed modules - students
will have an opportunity to concentrate on more specialized modules,
striving to understand different aspects of the intricate connections
between individual decisions and social outcomes, based on the analysis
of the phenomenon of polarization, the spreading of different types of
information in society (on the base of social network analysis) and
deeper analysis of human decision process and how individual decisions
can be influenced by our social networks. This course will propose a
perspective on society as a system of interacting elements, where its
dynamics lead to new emergent properties at the aggregate level.
At the end of the course learners should be able to:
Explain how individual behaviors lead to unexpected societal
outcomes.
Discuss how models help decode social processes.
Compare social processes across different contexts.
Demonstrate modeling through pen-and-paper and computer
simulations.
Explore computational models for social analysis.
Explain decision-making from economics, psychology, and
sociology perspectives.
Analyze how the environment shapes 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 their effects on social
dynamics.
Examine social networks' role in spreading influences (e.g.,
norms, viruses, products).
Explore the effects of social influences and network structures
on diffusion.
Identify and assess signs of polarization.
Understand key cognitive and social drivers of polarization (e.g.,
confirmation bias, filter bubbles).
Describe polarization's impact on democracy.
Propose strategies for managing polarization, from prevention to
reconciliation.
Time allocation of tasks: completing online tasks -15 weeks – appr. 2-4
hours/week, preparing for classes and reading literature: 30h,
preparation to final test - 15h
Rodzaj przedmiotu
Tryb prowadzenia
Założenia (opisowo)
Koordynatorzy przedmiotu
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
Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb: