Although the idea to study and project the socio-economic and demographic development of a society by simulating a large sample of individuals and their actions and interactions was already expressed in the 1950s, dynamic microsimulation still has yet to find its way into the methodological toolbox of mainstream social scientists. To simulate a society realistically requires detailed data, complicated models, fast computers and extensive testing. The more complex that models get, the more difficult it becomes to understand their operations and to assess their predictive power. One might speculate that microsimulation is too demanding, or that microsimulation models are niche products or dubious black box models, applicable only with caution where other methods are not available. Here, however, we will present an alternative point of view to such speculations.

First, microsimulation is a powerful tool that has already demonstrated its strength in applications of moderate complexity for which other modeling approaches exist--but those other approaches cannot compete in flexibility with the microsimulation approach.

Second, we increasingly face (or recognize) socio-economic challenges for which microsimulation is the only available study tool. Furthermore, microsimulation is an approach that follows naturally from modern research paradigms; it is a complement to detailed data analysis.

Third, microsimulation is an approach whose time has come. More than half a century after the introduction of microsimulation into the social sciences, the main obstacles to this approach have almost disappeared. Computer power has increased exponentially, the collection of individual longitudinal data has become routine, social scientists are trained in longitudinal research, and research itself has moved from a macro to a micro approach and is on the way towards a multilevel integration. The life course perspective has become a dominant paradigm and many of the most pressing problems we face are of a nature which makes dynamic microsimulation the most suitable study approach.

There is also one other former obstacle that has now disappeared. Programming languages, such as Modgen, currently enable researchers with only moderate programming skills – comparable to those needed for statistical software packages – to implement their models.

This material gives an introduction to microsimulation and presents the main underlying ideas as well as the strengths and drawbacks of this approach. It is organized in three parts:

  • first, we start with a definition of dynamic social science microsimulation and a sketch of its history
  • second, we explore three major situations for which microsimulation is an appropriate approach
  • third, we highlight the main strengths and drawbacks of microsimulation. With respect to its strengths, we describe its theoretical strengths from a life course perspective, its practical strengths from a policy makers’ perspective, and its technical strengths. When confronting drawbacks and limitations, we distinguish between intrinsic limitations imposed by randomness and rather transitory limitations imposed by the high demand for data. We also touch briefly on computational and other technical issues, although their corresponding costs are decreasing over time.