Agent-based Modeling of Urban Systems (Graduate class), Fall 09.


Description: This course will introduce graduate students in the spatial and computational social sciences to the use of agent-based techniques to model various aspects of urban systems. Emphasis will be placed on the notion that urban systems are constantly changing through time and across geographical scales where activities and features change from the split second decision involving local movements such as people walking, the development of land over months and years, to the migration of peoples over decades. We will cover applications ranging across the spectrum of urban systems, from pedestrian modeling, traffic simulation, and residential dynamics to that to urban growth models of cities and regions.


The course will combine taught classes, literature reviews with hands-on modeling. When code is available we will compile and run models as we review articles based on those models. Students will be expected to read assigned course literature and other relevant material they seem as appropriate to understand the subject matter covered in class in greater detail. In addition students will complete a class project where they develop their own agent-based model in their area of interest based on some aspect of urban systems discussed in the class.


Spatial Agent-based Models of Human-Environment Interactions (Graduate class), Spring 10, Spring 11, Spring 12


Description: This course will introduce graduate students in the spatial, environmental, and computational social sciences to the use of agent-based techniques as a means of modeling human-environmental interactions. Emphasis will be placed on spatial processes, the use of spatial identifiers to link socioeconomic and biophysical models, and where possible, links to geographic information and associated technologies. We will cover applications in areas such as agriculture, forestry, biodiversity, habitat degradation, interactions between human populations and nonhuman species, urban models, and civil violence.


The course will combine literature review with some hands-on modeling. When demo versions are available, we will compile and run models as we review articles based on those models. In addition, students will complete a class project where they develop their own models in their areas of interest.


Land-use Modeling Techniques and Applications (Graduate class), Spring 10, Fall 10


Description: The course surveys literature on spatially disaggregated empirical models of Land-Use Change (LUC). The course will begin with a discussion of factors that are hypothesized to drive land-use change across multiple spatial, institutional, and human scales and a discussion of issues related to Land-Use and Land-Cover Change (LUCC) modeling. The majority of the course will be spent reviewing techniques for land-use modeling, including statistical and regression models, cellular automata, mathematical programming and other optimization methods, agent-based models, and integrated models. We will conclude with a discussion of the strengths, weaknesses, and potential complementarities of the models discussed. The role of geographic information systems (GIS) as a tool for data management, analysis and visualization in land-use modeling will be discussed throughout the course.



Geographical Information Systems and Agent-based Modeling (Graduate class), Fall 10


Description: Designed to introduce students in computational social sciences and the social sciences in general to the principles and concepts of geographical information systems (GIS) and science and how spatial data can be used in the creation of spatially explicit agent-based models. Emphasis is placed on spatial processes, and linking agents to spatially explicit environments. Applications covered include: land use and land use change, urban growth, urban change, segregation, conflict, and humanitarian relief. Key areas of discussion will be on:


Introduction to Computational Social Science (Graduate class), Fall 11.


Description: This course is a graduate-level survey of computational approaches to social science research, with emphasis on methods, tools, software frameworks, and complexity theory as these apply to the investigation of social phenomena. For our purposes, "the social sciences" include anthropology, communication, economics and finance, geography, history, linguistics, political science, sociology, and social psychology, informed by developments in psychology, cognitive science, neuroscience, and related branches of behavioral science.


Computational social science (CSS) is at the interdisciplinary frontier in the social sciences. 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 computational social science.


Building  Virtual  Worlds (Graduate class), Spring 12


Description: This course is a graduate level survey of building virtual worlds for social science research, with emphasis on tools, software frameworks, and applications. building virtual worlds has the following objectives:

  1. To understand the motivation for the use of virtual worlds in social science theory and research, including some historical aspects (Why use virtual worlds in the social sciences?). 
  2. To learn about the variety of research programs utilizing virtual worlds across the social science disciplines, through a survey of virtual world literature (What has been accomplished thus far?).  
  3. To understand the distinct contribution that  virtual worlds can  make to the social sciences (What  is  their research potential?);  
  4. To provide foundations for more advanced work in virtual worlds.

Click here to see some outputs from the class project.


Introduction to NetLogo (Graduate class, 1 credit hour), Fall 11


Description: This one credit hour offering will focus on NetLogo programming for beginners with applications to the social sciences. It will consist of 1 hour/week meetings. It is intended to provide a working knowledge of NetLogo coding techniques for model construction, output visualization, data analysis, and computational experimentation. It will cover material not dealt with in other computational social science (CSS) courses. Advanced students are welcome to take this class in order to gain a working knowledge of NetLogo.


Assisted by CSS Ph.D. students Matt Koehler and Steve Scott.


Research Colloquium in CSS (Graduate class, 1 credit hour), Spring 11


Description: This one credit hour offering provides students with the opportunity to listen to presentations in specific research areas in computational social science by Center for Social Complexity associated faculty and professional visitors (link to seminars).


Course syllabi and access to supporting material are available upon request.