ComputationalModellingSocialSystems

KO | EN

gitlite — search

#react #ai #llm #android #rust #linux #macos #windows #kubernetes #awesome-list #pytorch #openai #mcp #open-source

ComputationalModellingSocialSystems

(★ 24)

Computational Modelling of Social Systems

ComputationalModellingSocialSystems Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Computational Modelling Social Systems
[David Garcia](http://dgarcia.eu), 2022

Welcome to the online materials for Computational Modelling of Social Systems. 
In this course, you will learn how to formulate and analyze computational models of social systems, in particular to study social interaction and the behavior of large groups of people and whole societiess. The course integrates contentz about social dynamics and analytic tools to understand the complex behavior of social systems. After this course, you will acquire programming skills to implement, simulate, and visualize these models.

## Who am I?

I am the Professor for Computational Behavioral and Social Sciences the Graz University of Technology, where I lead the [Computational Social Science Lab](http://www.csslab.at). I am also group leader at the Medical University of Vienna and at the Complexity Science Hub Vienna. My background is Computer Science but I worked my whole career with psychologists, sociologists and physicists to learn new ways to understand human behavior. I got my PhD from ETH Zurich in 2012 and a habilitation in 2018, starting to work as full professor TU Graz in 2020. To learn more about my research, check my [publications](https://dgarcia.eu/full-publication-list/). I teach this course in collaboration with the two postdoctoral researcher in the Computational Social Science Lab [Dr. Petar Jerčić](https://petarjercic.com/) and [Dr. Jana Lasser](https://janalasser.at/).

## Course Contents
The course is organized in 12 lectures grouped together in 4 blocks, plus two sessions in the end for project presentations. The course contains lectures and exercises in python to apply what you learned in the lecture. The online materials do not contain the solutions to the exercises, but if you are stuck or want to start from an easier point, in the github folder of the exercise you can find a version of the exercise with hints. We will add links to materials and readings a few days before each session.

### Block 1: Fundamentals of agent-based modelling

*(Please install Jupyter Notebook before the tutorial sessions takes place, where we can provide help with the installation if something goes wrong.)* The easiest way is using the [Anaconda](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/install.html) distribution, since it is well supported and maintained.

1. [**Basics of agent-based modelling: the micro-macro gap**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/01_Introduction/Introduction.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/01_Introduction/Slides/Slides.html)  (03.03.2022)  
- [**Tutorial: ABM basics in Python with Mesa (session 1)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_00_Tut/Exercise_Tutorial.md)

2. [**Modelling segregation: Schelling's model**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/02_Segregation/Segregation.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/02_Segregation/Slides/Slides.html) (10.03.2022)  
- [**Tutorial: ABM basics in Python with Mesa (session 2)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_00_Tut/Exercise_Tutorial.md)

3. [**Modelling cultures**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/03_Culture/Culture.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/03_Culture/Slides/Slides.html) (17.03.2022)  
- [**Exercise 1: Schelling's model and Pandas (session 1)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_01_Schelling/Excercise_Schelling_withSolution.md)

### Block 2: Opinion dynamics

4. [**Basics of spreading: Granovetter's threshold model**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/04_BasicSpreading/BasicSpreading.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/04_BasicSpreading/Slides/Slides.html)  (24.03.2022)  
- [**Exercise 1: Schelling's model and Pandas (session 2)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_01_Schelling/Excercise_Schelling_withSolution.md)

5. [**Opinion dynamics**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/05_OpinionDynamics/OpinionDynamics.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/05_OpinionDynamics/Slides/Slides.html)  (31.03.2022)  
- [**Exercise 2: Threshold models (session 1)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_02_Granovetter/Excercise_Granovetter.md)

6. [**Modelling hyperpolarization and cognitive balance**  (07.04.2022) - Guest lecture by Simon Schweighofer](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/06_Hyperpolarization/Hyperpolarization.html)    
- [**Exercise 2: Threshold models (session 2)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_02_Granovetter/Excercise_Granovetter.md)

*No class between 9.04.2022 and 23.04.2022: Easter holidays*

### Block 3: Network formation

7. [**Basic network models**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/07_NetworkModels/NetworkModels.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/07_NetworkModels/Slides/Slides.html)  (28.04.2022)  
- [**Exercise 3: Bounded confidence (session 1)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_03_BoundedConfidence/Excercise_Bounded-Confidence.md)

8. [**Modelling small worlds**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/08_SmallWorlds/SmallWorlds.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/08_SmallWorlds/Slides/Slides.html)  (05.05.2022)  
- [**Exercise 3: Bounded confidence (session 2)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_03_BoundedConfidence/Excercise_Bounded-Confidence.md)

9. [**Scale-free networks**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/09_ScaleFreeNetworks/ScaleFreeNetworks.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/09_ScaleFreeNetworks/Slides/Slides.html)  (12.05.2022)  
- [**Exercise 4: Scale-free networks and visualization (session 1)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_04_ScaleFreeNetworks/Excercise_Scale_Free_Networks.md)

### Block 4: Behavior on networks

10. [**Growth processes and spreading in networks**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/10_GrowthAndSpreading/GrowthAndSpreading.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/10_GrowthAndSpreading/Slides/Slides.html)  (19.05.2022)   
- [**Exercise 4: Scale-free networks and visualization (session 2)**](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/Exercise_04_ScaleFreeNetworks/Excercise_Scale_Free_Networks.md)

*No class on 26.05.2022: Ascension day*

11. [**Modelling epidemics: the SEIRX model**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/11_ModellingEpidemics/index.html) [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/11_ModellingEpidemics/index.html)  (06.02.2022)

12. [**Lecture Q&A session**](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/12_Summary/Slides/Slides.html)  [[Slides]](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/12_Summary/Slides/Slides.html)  (09.06.2022)  
*Project guidance*

*No class on 16.06.2022: Corpus Christi*

13. **Project presentations** [[Agenda]](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/final_project_agenda.pdf) (23.06.2022)
- Make sure your whole group is available between 16:00 and 19:00

14. **Project presentations** [[Agenda]](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems/blob/main/final_project_agenda.pdf) (30.06.2022)
- Make sure your whole group is available between 16:00 and 19:00
- **The deadline to submit your final report is July 10th (end of day). You can submit over Teach Center. [projects guide](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/ProjectsGuide.html)**

## Where to access materials

- Reading lists, codes, and data can be found on the [Github repository of the course](https://github.com/dgarcia-eu/ComputationalModellingSocialSystems).
- Students at TU Graz can access the [course at Teach Center](https://tc.tugraz.at/main/enrol/index.php?id=4384) to get additional information, watch videos, and to participate in the forums.

## Place and time

The course takes place on Thursdays at 16:15 (sharp), with a **lecture followed by an exercise session**. The first session of the course will take place in room [HS F (Kopernicusgasse 24)](https://online.tugraz.at/tug_online/ris.ris?pOrgNr=37&pQuellGeogrBTypNr=5&pZielGeogrBTypNr=5&pZielGeogrBerNr=350003&pRaumNr=568&pActionFlag=A&pShowEinzelraum=J) with live streaming over Webex (see TeachCenter for the link). From the second week of the course, the course will take place in [HS i7 (Inffeldgasse)](https://online.tugraz.at/tug_online/ris.ris?pOrgNr=37&pQuellGeogrBTypNr=5&pZielGeogrBTypNr=5&pZielGeogrBerNr=240001&pRaumNr=4030&pActionFlag=A&pShowEinzelraum=J) and streaming will take place through the standard TU Graz streaming system.

The semester starts with a yellow light, so we will start in hybrid mode with limited occupancy in person. When attending in person, proof of 2G status might be required and seating space is limited depending on varying policies. Seating will be given in a first-come-first-served basis each session and students that do not fit will have to follow the lecture online from another place.

There is an extra online exercise support group on Wednesdays at 13:00 (sharp). Check TeachCenter for the webex link.

## Course grading

The assessment for the course is based on four components:

- Exercises done in groups of **one or two students**
  - Exercise 1: ungraded
  - Exercise 2: **25% of final grade**. Deadline: 20.04.2022
  - Exercise 3: **25% of final grade**. Deadline: 18.05.2022
  - Exercise 4: ungraded
  
- Final group project in groups of **up to four students**. Projects are linked to two evaluations:
  - Participation in group presentation sessions (both as presenter and discussant): **25% of final grade**
  - Project report and codes: **25% of final grade**

You can find more information about the project presentations and reports, including deadlines, in the [projects guide.](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/ProjectsGuide.html) You can also find some guidance and recommendations in the [slides of the project support session.](https://dgarcia-eu.github.io/ComputationalModellingSocialSystems/ProjectsIntroSlides/Slides.html)

Graded exercises need to be submitted on teach center by the deadline (end of day in Graz time). We will provide solution files for ungraded exercises for you to check your progress. Nevertheless, we expect to see in your projects the techniques covered in the exercises, both graded and ungraded.

ComputationalModellingSocialSystems

ComputationalModellingSocialSystems Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

ComputationalModellingSocialSystems

ComputationalModellingSocialSystems Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README