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New Pathways in Microsimulation

Dekkers, Gijs, Keegan, Marcia and ODonoghue, Cathal
Ashgate: Aldershot, 2014
ISBN 9781409469315 (pb)

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Reviewed by Andreas Koch
University of Salzburg

Cover of book In terms of epistemology and methodology, microsimulation has certainly evolved over the past two decades or so. While Troitzsch et al. yet in 1996 pointed to some deficiencies in social microsimulation such as the mutual ignorance of “data based dynamical microsimulation” (with a lack of social interaction between agents which are conceptualised as black boxes) and “concept driven microsimulation” (with validation problems due to a lack of high resolution data) (p. v), Conte et al. (1998) just two years later drew the distinction between multi-agent systems and agent-based social simulation by referring the notion of the “artificial” to agents in the former and to society in the latter case (p. 3). Meanwhile, disaggregated data acquisition and data availability is easier to achieve, both because of open data access policies and existing social network data from sources like Facebook or Twitter, so that the level of artificiality has been reduced continuously.

Even though significant progress in the field of microsimulation can be noticed, Dekkers, Keegan, and O’Donoghue do not conceal in their volume that microsimulation has to face some challenges in the future. One reason for the publication of selected papers, which were presented during the 2011 International Microsimulation Association (IMA) conference, is dedicated precisely to this aim: to illustrate “new pathways” of an already well-established technology (in addition, IMA publishes the International Journal of Microsimulation with a wide range of special issues and general topics: http://www.microsimulation.org/ijm/).

The book consists of 20 chapters that can be grouped into the sections "austerity", "health", "pensions", "poverty" and "methodology". Relatively great concern is given to methodological questions and new approaches, for example by applying “binary alignment methods” (the chapter by Jinjing Li and Cathal O’Donoghue) or “backward simulations” as a validation technique (the chapter by Mark Birkin and Nicolas Malleson on urban neighbourhood dynamics). All of the carefully selected chapters make it clear that microsimulation has achieved a state of scientific maturity, leaving space for further explorations. One direction for such new fields of research is given with comparative studies of static and dynamic microsimulations, most notably in policy assessments. Another idea is to make social microsimulation more spatially explicit by drawing particular attention to local or regional disparities of, among others, urban neighbourhood dynamics or regional differences in tax policies (this is presented in a chapter by Cantó et al., who address this issue in the context of child poverty in Spain). Last but not least, it is the application of additional statistical techniques that helps to enhance the modelling of social phenomena, utilising microsimulation in a highly sophisticated way. One such example is presented in the chapter by Gijs Dekkers, who uses the kernel density technique to study the impact of demographic ageing on pension inequality in Belgium.

Beyond the illustration of the diversity of methodological discourses about appropriate ways of dealing with microsimulation, the book is particularly useful because of its explicit orientation towards social problems, such as inequality, poverty, ageing, and pensions, and how policies might handle them. Two minor criticisms must be mentioned, however: first, most contributions do not sufficiently delineate the software tools used in order to compute their microsimulation models. This makes it rather difficult for newcomers in this field to cope with microsimulations in their own fields of research. Second, it is a somewhat positivistic epistemology that characterises the chapters, emphasising convincingly what has been achieved or what can be solved with respect to the model purpose. However, the volume lacks a (self-)critical evaluation of the transfer of the “model world” towards the “empirical world”. Nevertheless, I can strongly recommend this book to JASSS readers interested in attempts to tackle social problems computationally.

* References

CONTE, R., Gilbert, N. and J. S. Sichman (1998). MAS and Social Simulation: A Suitable Commitment. In: Sichman, J. S., Conte, R. and Gilbert, N. (eds.): Multi-Agent Systems and Agent-Based Simulation. Lecture Notes in Artificial Intelligence 1534. Springer, Berlin, Heidelberg, p. 1-9.

TROITZSCH, K. G., Mueller, U., Gilbert G. N. and J. E. Doran (eds.) (1996). Social Science Microsimulation. Springer, Berlin, Heidelberg.


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