A Semester of Spatial Agent-based Models

So draws an end of another semester and as it is becoming a bit of tradition, here is a post highlighting some of the class projects from my graduate class  entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students were expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions.  

For several of the students this was their first exposure to either agent-based modeling or utilizing geographical information in the modeling process. In the movie below a selection of these projects can be seen. The projects ranged from exploring how farming practices impact erosion, water reuse practices within agriculture, to the spread of diseases, deciding to evacuate during a disaster, to that of war gaming or how  zooplankton impacts Basking Shark shoaling behavior. As can be seen the movie, the models ranged from abstract spatial representations to those utilizing geographical information as a foundation of their artificial worlds. Many of the models where created using NetLogo (including one using LevelSpace) while others chose to utilize MASON or Mesa.

I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation both in person and virtually in the class.  

Class Model Examples

Avid readers of this blog (if there are any) might have noticed at the end of each semester I do a post pertaining to class models from the various courses I teach. This often involves a short movie of some of these models like the one below.

Often I get asked about these models are, so finally I have complied a selection of them on GitHub: https://github.com/acrooks2/ClassModels. These are only NetLogo  models (for the time being) as I use it as a way of introducing students to agent-based modeling and programing.  As noted on the readme of the repository these models come as is. What explanations there are is given in the readme file for each model (these mainly come in the form of abstracts from the papers that were submitted with the models). No further explanations, support etc. will be given and are only provided to show the range of problems agent-based models can be used to explore. I also need to acknowledge all the students who submitted the models, you know who you are! This project would not be possible without you!

Maybe one day I will also get around to containerizing some of these models. For those interested containerization and how to do this for NetLogo models, https://www.comses.net/ has a great tutorial on this (click here for further details).

Examples of the types of GIS and agent-based modeling projects.

Models from Teaching CSS Fall 2019

Avid readers of this blog (if there are any) may be familiar with my routine of combing end of semester projects into a short movie and blogging about it. Well its that time again. Last semester I gave a class entitled Introduction to Computational Social Science and instead of setting a final exam, I ask the students to carryout an end of semester research project. The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.: 

• to understand the motivation for the use of computational models in social science theory and research; 
• to learn about the variety of CSS research programs across the social science disciplines; 
• to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.

Below you can see some of the outputs from these projects this last fall. These models ranged in type from agent-based models, microsimulation to system dynamics models applied to a variety of topics from how machine learning can be utilized within agent-based models to applications such as the courts, common pool resources, public goods, economic growth, supply chains, heath care issues (e.g. patient diagnosis, fungi infections within hospitals), team performance, labor markets, voting, and several other topics along the way.

A Semester of CSS 645: Spatial Agent-based Models of Human-Environment Interactions

This last Spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students were expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions.  For several of the students this was their first exposure to either agent-based modeling or utilizing geographical information in the modeling process. In the movie below a selection of these projects can be seen. The projects ranged from migration, evacuation modeling during a natural disaster, gerrymandering, the spread of diseases, recidivism, Commons problems to that of urban decline. As can be seen the models ranged from abstract spatial representations to those utilizing geographical information as a foundation of their artificial worlds. Many of the models where created using NetLogo.

I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation in the class.  

Models from Teaching CSS Fall 2018

Most of the time when I teach a class instead of setting a final exam, I ask the students to carryout an end of semester research project. In my Introduction to Computational Social Science class this project entails the development of a computational model in an area of  interest to the student . The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.: 

• to understand the motivation for the use of computational models in social science theory and research; 
• to learn about the variety of CSS research programs across the social science disciplines; 
• to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.

Below you can see some of the outputs from these projects this last fall. The models range in type from agent-based models, microsimulation to system dynamics models applied to a variety of topics from voting and political parties, the peer effects of students, urban decline, employment growth and rise and fall of civilizations and many other topics along the way.

Spatial Agent-based Models of Human-Environment Interactions: Spring 2018

During the past spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students are expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions. In the movie below is a selection of these projects can be seen. The projects ranged from urban growth, housing markets, the adoption of solar energy, employment opportunities, populations at risk from terrorism, commuting, to the spread of diseases. Many of the models were done in NetLogo, MASON and some in Python including using MESA.




I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation in the class.

Models from Teaching CSS

Most of the time when I teach a class instead of setting a final exam, I ask the students to carryout an end of semester research project. In my Introduction to Computational Social Science classes (both at the graduate and undergraduate level), this project entails the development of a computational model in an area of  interest to the student (or at the undergraduate level, students can opt to systematically explore someone else's model). The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.:

• to understand the motivation for the use of computational models in social science theory and research;
• to learn about the variety of CSS research programs across the social science disciplines;
• to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.

Below you can see some of the outputs from these projects this last fall. The models range in type from agent-based models, cellular automata models to discrete event simulations (aka. queuing models) applied to a variety of topics from elephant poaching, artists and patrons, inheritance and wealth accumulation, the spread of religion, to that of looking at serving times at a Chipotle Mexican Grill.

 

Spatial Agent-based Models of Human-Environment Interactions: Spring 2016

During the past spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students were expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions. Below is a selection of these projects, which ranged from hiking along the Application trail,  to that of exploring the ride-sharing economy, to the spread of diseases, ecosystem recovery modeling and the origins of social complexity. 

I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation in the class.

A Semester with Urban Analytics

This past semester I gave a new class at GMU entitled "Urban Analytics". In a nutshell the class was about introducing students to a broad interdisciplinary field that focuses on the use of data to study cities. More specifcally the emphasis of the class was to provide students with a understanding of what methods, tools and theory can be used to monitor, analyze and model cities. 

From my past research and also when preparing the class material,  I have come to the realization that to study cities (like many others, you know who you are) that there is no one general model, tool or dataset. Therefore, one needs to maintain a toolbox of specialized tools than can be applied to different aspects of urban problems and questions. 

The toolbox that we used in class included a variety of software such as ArcGIS, QGIS, GeoDa, SANET along with programing and scripting in Python and R to modeling  cities via UrbanSim, NetLogo and MASON. Data we used ranged from crowdsourced (e.g. volunteered geographical information) data such as from OpenStreetMap or Wikipedia, to crowd harvested (ambient geographical information) data such as Twitter and Flickr, as-well as more traditional sources of data such as the US Census.

The Urban Analytics Toolbox

As an introduction to urban analytics, the course had the following objectives:

1. to understand the motivation for the use of data to study cities, including some historical aspects; 
2. to learn about the variety of Urban Analytics research programs across the several disciplines (urban planning, regional science, public policy, geography, computational social science etc.), through a survey of the literature and case studies. 
3. to understand the distinct contribution that Urban Analytics can make by providing specific insights about cities at multiple scales. 
4. to provide the foundations for more advanced work in the area of Urban Analytics. 

As with many of my courses, students were expected to complete a end of semester project. Below is a selection of these projects which explored some aspect of urban life.

I would like to thank the students for participating in this new class. It was a fun trip.

A semester of CSS

For the last few years one of the classes that I have given is the Introduction to Computational Social Science (CSS). This is often the first class many students take within our program and as such its objectives are:

1. To understand the motivation for the use of computational models in social science theory and research, including some historical aspects (Why conduct computational research in the social sciences?).
2. To learn about the variety of CSS research programs across the social science disciplines, through a survey of social simulation models (What has CSS accomplished thus far?).
3. To understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity (What is the relation between computational social science.
4. To provide foundations for more advanced work in subsequent courses or projects for those students who already have or will develop a long-term interest in computational social science.

The course surveys computational approaches such as system dynamics, social network analysis, machine learning, cellular automata, discrete event simulation, agent-based modeling, and microsimulation to study social phenomena with emphasis on complexity theory. 

On thing that all the students need to do during the semester is create a computational model investigating a social science research question. This exercise is often their first model that many students ever create. Below you can see some of this years models. Most of the models where created in NetLogo.  

A Semester with Spatial Agent-based Models

With the spring semester over, I thought I would show some of the final agent-based modeling projects that were carried out in CSS 645: Spatial Agent-based Models of Human-Environment Interactions. As always I was quite impressed the models and we had a plethora of topics ranging from mobile agent-based models, shopping, pick pocketing, route finding, travel to work, the spatial spread of information, deer management, urban growth etc... What is interesting is that while the majority of models are implemented in NetLogo, more and more are being done in Python.

Something new for this semester is we also tried to reproduce a published model. Below you can see 3 examples of such work. Click here to see a previous post on reproduction and replication. 

Example Models from CSS600

Even after several years of teaching it is always amazing how quickly a semester passes. One of the courses I taught this semester was CSS 600: Introduction to Computational Social Science. This is often the first CSS class many students take here at George Mason University. We discuss a number of computational approaches which are used for social science research, coupled to  complexity theory. As an introduction to the subject, the course has the following objectives:

1. To understand the motivation for the use of computational models in social science theory and research, including some historical aspects (Why conduct computational research in the social sciences?).
2. To learn about the variety of CSS research programs across the social science disciplines, through a survey of social simulation models (What has CSS accomplished thus far?).
3. To understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity (What is the relation between computational social science.
4. To provide foundations for more advanced work in subsequent courses or projects for those students who already have or will develop a long-term interest in CSS.

Part of the students final grade comes from the development of a computational model in an area of their interest (e.g., microeconomics, international relations, environmental policy, economic development, historical dynamics, finance etc..). Often, this is the first compuational model that the students have ever developed. Below you can see a number of models developed using NetLogo as part of the class.

To find out more about our program see: http://www.css.gmu.edu/