IDEM 112
A stochastic process approach to agentbased modeling and simulation
Course coordinator: Anna Klabunde
Start: October 19th 2015
End: October 30th 2015
Location: Max Planck Institute for Demographic Research (MPIDR), Rostock
Instructors:

Keynote lecture: Icek Ajzen (University of Massachusetts)

Keynote lecture: Joan Walker (University of California, Berkeley)

Jakub Bijak (University of Southampton)

Jutta Gampe (MPIDR, Rostock)

Anna Klabunde (MPIDR)

Matthias Leuchter (MPIDR)

Matteo Richiardi (University of Oxford, Institute for New Economic Thinking)

Tom A.B. Snijders (University of Groningen and University of Oxford)

Frans Willekens (MPIDR, Rostock)
Course description
Agentbased or individualbased 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 microlevel. 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 act and behave in response to changes in their own characteristics, in that of other agents or in the environment. Feedback and memory are key concepts. Actions and interactions are often outcomes of choice, but are almost always affected by chance, too.
This is an advanced course. We want to help spread important innovations in agentbased modeling and place the methodology on a more stable probabilistic and choicetheoretical grounding. The course will rest on three pillars:
(a)Probability theory, especially the theory of stochastic processes. This is based on the fact that the quantile function (inverse distribution function) is the workhorse of all microsimulation models. We understand an agentbased model as an instance of a stochastic (random) process and it is the aim of this course to connect ABM and probability theory more closely than this is usually done. We start from simple stochastic process models that capture random factors and systematic factors, such as individual characteristics. Individual actions and interactions are determined by values drawn randomly from probability distributions.
(b)Decision theory / choice theory / action theory. We extend stochastic process models by introducing behavior in the form of choice. Action is an outcome of random choice processes that include cognition and social interaction.
We reflect on how agentbased models could be more closely connected to existing research on decision theory. We study two decision theories which are broad enough to incorporate a wide range of different decision contexts. The first one is the theory of planned behavior (TPB), introduced in social psychology by Icek Ajzen. The other one is discrete choice theory whose most prominent originators are Daniel McFadden and Moshe BenAkiva. Recent contributions to discrete choice theory (hybrid choice models) have many elements in common with the TPB.
(c)Social network theory. Almost all agentbased models have elements of social interaction, frequently modeled as networks. Stochastic actorbased models for social network dynamics are introduced as an example of how agentbased models can aid statistical inference.
We aim at overcoming the artificial divide between microsimulations and agentbased modeling and show that these methodologies are derived from common ancestors and use a common set of tools from mathematics, statistics and computer science.
Much more so than in other courses on ABM, the topics of calibration, estimation and validation of agentbased models, as well as sensitivity analysis and the design of computer experiments, are treated extensively.
This is a course for PhD students, PostDocs and senior researchers who are currently involved in an agentbased modeling project. We expect participants to have some background knowledge already and to already have decided on a programming language or modeling platform of choice, as this will not be covered in the course.
The course offers four important skills that help to produce agentbased models which are innovative, insightful and statistically sound:

Knowledge in probability theory. We provide an introduction to stochastic processes and convolution. This enables you to start out from truly simple, analytically solvable models whose complexity can then be raised gradually.

Tools for statistical inference in and through agentbased models. It is possible to estimate parameters in agentbased models for example via simulated minimum distance and Bayesian methods, as well as to use agentbased models to identify behavioral rules which are likely data generating mechanisms for real data sets.

Skills in modeling choice behavior. You will be able to connect behavioral theories from fields such as psychology and economics to their mathematical implementations. You will learn about different ways of modeling choice so that you can choose the one adequate for your model.

Design of computer experiments, calibration and validation. You will learn to efficiently explore the parameter space in order to thoroughly understand model behavior, to conduct a systematic sensitivity analysis, and to calibrate a model to data.
Organization
The course consists of six days, each dedicated to a different topic, with two days for selfstudy and assignments inbetween. Lectures are in the morning. Afternoons are for computer tutorials. The course will be followed by a threeday workshop providing participants and other researchers an opportunity to present and discuss their work. On the first day of the course, professor Icek Ajzen will deliver a keynote address entitled “The Theory of Planned Behavior: a unifying framework to predict behavior.”
The workshop is coorganized by the IUSSP Panel on Microsimulation and Agentbased Modeling. The subject of the workshop is “The science of choice.” Professor Joan Walker will deliver a keynote address entitled “Modeling choice.” It will cover current innovations in discrete choice models. A separate call for papers for the workshop has been issued. Course participants should participate actively in the workshop.
Lectures and computer lab
The lectures are generally from 9:00 – 12:00; computer labs are from 14:00 to 17:00.
Monday, October 19
9.0010.00 Introduction to computers and network
10.0012.00 Lecture: Introduction to stochastic processes (Frans Willekens)
13.0015.30 Computer lab: Introduction to stochastic processes (Frans Willekens)
Outline and Reading List (PDFDatei, 180 kB)
16.0017.30 Süßmilch lecture: The Theory of Planned Behavior: a unifying framework to predict behavior (Icek Ajzen)
Tuesday, October 20
9.00 – 12.00 Lecture and computer lab: Eventhistory models (Jutta Gampe)
14.00  17.00 Lecture and computer lab: Eventhistory models (Jutta Gampe)
Wednesday, October 21
9.3011.00 Süßmilch lecture: Studying social influence in networks using the stochastic actororiented model (Tom A.B. Snijders)
11.3013.00 Lecture: Actororiented dynamic social network modeling, part II (Tom A.B. Snijders)
14.0017.00 Computer lab: SIENA package for the statistical analysis of longitudinal social network data (Tom A.B. Snijders)
Outline and Reading List (PDFDatei, 99 kB)
Thursday, October 22
9.0012.00 Lecture: Agentbased modeling in continuous time – the example of international migration (Anna Klabunde)
14.0017.00 Computer lab: Agentbased modeling in continuous time – the example of international migration (Anna Klabunde and Matthias Leuchter)
Outline and Reading List (PDFDatei, 274 kB)
Friday, October 23
9.0012.00 Lecture: Calibration, estimation and validation of agentbased models (Matteo Richiardi)
14.0017.00 Computer lab: Calibration, estimation and validation of agentbased models (Matteo Richiardi)
Outline and Reading List (PDFDatei, 81 kB)
Monday, October 26
9.3011.00 Süßmilch lecture: Agentbased modelling in demography: Epistemological and methodological challenges (Jakub Bijak)
Outline and Reading List (PDFDatei, 67 kB)
11.3013.00 Lecture: Design and analysis of computer experiments (Jakub Bijak)
14.0017.00 Computer lab: Implementation and analysis of computer experiments (Jason Hilton)
Tuesday, October 27
Selfstudy and assignments
Wednesday, October 28
Keynote lecture: Modeling Choice (Joan Walker)
Thursday, October 29
Second day of workshop
Friday, October 30
Third day of workshop
Course prerequisites
Course prerequisites are (a) experience with agentbased modeling or the successful completion of an introductory course on agentbased modeling, (b) the knowledge of one programming language or agentbased modeling platform, and (c) a working knowledge of R.
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 https://www.coursera.org/course/rprog. Alternatively or additionally you can also use the tutorial website from UCLA (http://www.ats.ucla.edu/stat/r/) or any other Rtutorial which goes into sufficient detail.
Assessment
Evaluation will be based on active participation in the lectures, computer lab, and workshop, and on successful completion of the required assignments.
Readings
General Readings (PDFDatei, 59 kB)
Financial support
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.
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 15 students will be admitted.
The selection will be made by the MPIDR based on the applicants’ scientific qualifications.
How to apply
Application is no longer possible.
More Information
The science of choice  How to model the decisionmaking process? Workshop at the MPIDR, October 2830  Final Program (PDFDatei, 389 kB)