Agent-Based Computational Modeling in Population Studies
Agent-based computational (ABC) modeling is a relatively new approach to research in the social sciences. In ABC modeling, societal phenomena such as the emergence of social institutions, segregation, and the spread of innovations are studied from the ‘bottom up’, by modeling the behavior and interactions of the individuals that make up society. In recent years, this approach has also been applied to a number of demographic issues, from fertility to migration to union formation dynamics, generating novel and important insights that are usually difficult to obtain with more traditional methods. Unfortunately, the spread of the approach within the discipline has been comparatively slow, given that population researchers often lack the knowledge and skills that are necessary to develop and analyze agent-based models.
The course will provide participants with the knowledge and skills that are necessary to implement ABC models. In particular, participants will:
- learn about the benefits of ABC models and the type of research questions that can be addressed with such models;
- get to know existing ABC models in population studies and other social sciences;
- learn to develop their own basic model of population dynamics with the modeling platform NetLogo;
- learn how to design and analyze systematic computational experiments.
The course will be useful for researchers and doctoral students working or studying in the fields of demography, sociology, and related disciplines.
The course consists of five full days and participants should prepare to work full time (40 hours) for the duration of the course. On most days, there will be a lecture in the morning and a computer lab in the afternoon. One exception is the last day, on which participants will get the opportunity to present their own ABC modeling related work/research ideas, on which they will receive feedback from the course instructors and other participants.
The course addresses demographers and researchers from related disciplines, such as sociology or epidemiology. Participants should be familiar with basic multivariate analysis techniques in demographic and social science research (e.g., multiple regression analysis). Students are expected to bring their own laptops with the most recent version of NetLogo installed (https://ccl.northwestern.edu/netlogo/), but no prior knowledge of NetLogo is required. A working knowledge of R is desirable, but not a prerequisite.
- André Grow (MPIDR)
- Jason Hilton (University of Southampton)
Students will be evaluated on the basis of completion of assignments and participation in discussions.
There is no tuition fee for this course. Students are expected to pay their own transportation and living costs.
Recruitment of students
- Applicants should either be enrolled in a PhD program or have received their PhD.
- A maximum of 20 students will be admitted.
- The selection will be made by the MPIDR based on the applicants’ scientific qualifications.
How to apply
- Applications should be sent by email to the MPIDR (address below). Please begin your email message with a statement saying that you apply for course IDEM 112 - Agent-Based Computational Modeling in Population Studies. You also need to attach the following items integrated in *a single pdf file*: (1) A two-page curriculum vitae, including a list of your scholarly publications. (2) A one-page letter from your supervisor at your home institution supporting your application. (3) A two-page statement of your research and how it relates to the course. Please include a short description of your knowledge of multivariate analysis techniques and of ABC modeling. At the very end of your research statement, in a separate paragraph, please confirm that, if admitted, you will be able to come without financial aid from our side. In the same last paragraph, please indicate whether or not you are also applying for the Rostock Retreat on Simulation.
- Send your email to Heiner Maier (idem [at] demogr.mpg.de (Email)).
- Application deadline is 28 February 2019.
- Applicants will be informed of their acceptance by 31 March 2019.
- Applications submitted after the deadline will be considered only if space is available.