Agent-based modelling and simulation (ABM-ABS)
Date:
20/10/2014 - 30/10/2014
Organised by:
Max Planck Institute for Demographic Research, Vienna Institute of Demography and University of Sout
Presenter:
Dr Jakub Bijak, University of Southampton
Thomas Fent (Vienna Institute of Demography)
Alexia Fürnkranz-Prskawetz (Vienna Institute of Demography)
Jutta Gampe (MPIDR, Rostock)
Jason Hilton (University of Southampton)
Anna Klabunde (MPIDR, Rostock)
Sebastian Klüsener (MPIDR, Rostock)
Bernhard Rengs (Vienna Institute of Demography)
Eric Silverman (University of Southampton)
Frans Willekens (MPIDR, Rostock)
Sabine Zinn (MPIDR, Rostock)
Level:
Intermediate (some prior knowledge)
Contact:
For information about the course and the application process for the course contact Heiner Maier (idem@demogr.mpg.de).
For information about the bursaries please contact Amos Channon (a.r.channon@soton.ac.uk)
Venue: Online
Description:
Agent-based or individual-based models describe how populations evolve, patterns (e.g. social networks) emerge, and collective features (e.g. norms) are established as outcomes of actions and interactions at the micro-level. Simple heuristics or rules govern the actions and interactions. Agents may be humans, institutions or organizations. They have attributes, capacities (e.g. human capital: education and health) and resources (time and capital: physical, financial, social and cultural). Agents are autonomous - they determine their own actions -, connected - they interact with each other and with the environment - and adaptive – they change their behaviour in response to changes in their own characteristics, in that of other agents or in the environment. Feedback and memory are key concepts.
Agent-based modeling (ABM) is approached as an extension of microsimulation. ABM adds to microsimulation (a) individual and group decision processes and (b) social interaction processes.
The course offers four important skills that help to be innovative in research:
- Simulation Skills
- An agent-based modelling language
- Software skills
Skills in the design and analysis of computer experiments
Learning Outcomes
The course consists of two sections. The first section focuses on micro-simulation and R. The second section focuses on ABM-ABS and NetLogo. Lectures are in the morning. Afternoons are for computer tutorials, assignments and projects.
Course Aims and Objectives
For more information about the course please go to http://tinyurl.com/goabm
Evaluation will be based on active participation in the computer lab and on the outcome of the mini-project. Reports of the mini-project should be prepared in the weeks following the course and should be submitted before 31st December 2014. Reports are submitted by individuals or groups, provided the members of the group collaborated on a mini-project during the course. A certificate is awarded upon successful completion of the required coursework.
Recruitment of students
Applicants should either be enrolled in a PhD program (those well on their way to completion will be favored) 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. Please begin your email message with a statement saying that you apply for course IDEM 112 – Agent-based Modeling and Simulation. You also need to include the following three documents, either in the text of the email or as attached documents. (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 R and survival analysis. Please indicate (a) whether you would like to be considered for financial support and (b) if you would be able to come without financial aid from our side.
Send your email to Heiner Maier (idem@demogr.mpg.de).
Application deadline is 20 August 2014.
Applicants will be informed of their acceptance by 5 September 2014.
Applications submitted after the deadline will be considered only if space is available.
Prerequisites
An intermediate knowledge of R or, alternatively, a working knowledge of R in combination with skills in another programming language, is a course prerequisite. If you never used R in your research work, please make sure you have a sufficient knowledge before the course starts, e.g. by attending a free online course such as the ones offered by Roger Peng and colleagues from John Hopkins University: http://jhudatascience.org/. Alternatively or additionally you can also use the tutorial website from UCLA (http://www.ats.ucla.edu/stat/r/) or any other R-tutorial which goes into sufficient detail.
No prior knowledge of NetLogo is required.
A basic knowledge of survival analysis is preferable, but not mandatory. No prior knowledge of multistate modeling is required.
Cost:
There is no tuition fee for this course. Students are expected to pay their own transportation and living costs. However, a limited number of scholarships are available on a competitive basis for outstanding candidates and for those applicants who might otherwise not be able to come.
The Southampton ESRC DTC (http://www.southampton.ac.uk/esrcdtc) alongside the EPSRC-funded Care Life Cycle project (http://www.southampton.ac.uk/clc/) are offering two bursaries of £1000 each to cover travel, accommodation and susbsistence for any UK registered PhD student. To apply for these bursaries please send the same three documents needed for the application for the course (CV, supervisor letter and statement of research) to Esrcdtc@soton.ac.uk and copy in a.r.channon@soton.ac.uk by the 29th August.
Website and registration:
http://www.southampton.ac.uk/esrcdtc/news/events/2014/10/20_rostock.page?
Region:
Europe
Keywords:
Quantitative Data Handling and Data Analysis, Event History Analysis, Simulation , Quantitative Software
Related publications and presentations:
Quantitative Data Handling and Data Analysis
Event History Analysis
Simulation
Quantitative Software