Abstract
This chapter aims to contribute to the debate on the role of model-based approaches, such as agent-based modelling, in the future of demography. First we call attention to the developments of the discipline since the seventeenth century, and we describe its four successive paradigms related to the period, cohort, event-history and multilevel perspectives. We argue that these paradigms are complementary and that demography, since its beginnings, has subscribed to the classical scientific research programme launched by the promoters of modern science. Next, we examine how simulation modelling developing in population sciences recently, may help to respond to three main challenges: how to overcome complexity in social research; how to reduce its uncertainty; and how to reinforce its theoretical foundations. We sketch a model-based research programme for demography, looking specifically at interactions between various population systems. We then show how this approach might conform to the classical scientific research programme, in order to take advantage of its benefits.
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Notes
- 1.
Citations in this and in the next paragraph come from Bacon (1620), aphorisms 24, 39 and 40.
- 2.
- 3.
These fields were not so clearly defined at this time: scientists were working in different social or biological sciences and in statistics simultaneously.
- 4.
Similarly, acknowledgement of the role of space in demography has led to the multi-regional perspective within the cohort paradigm (Rogers 1975), later extended to the multi-state case.
- 5.
- 6.
- 7.
Following Huneman (2014), we give these terms slightly different meanings than for example Thagard (1993, p. 6), for whom the weak simulation is “a calculating device drawing out the consequences of mathematical equations that describe the process simulated,” while a strong simulation “itself resembles the process simulated” (see also Brenner and Werker 2007).
- 8.
- 9.
The problem here is not the empirical basis of such models – quite the contrary – but unrealistic mechanisms. Particularly problematic are Markovian assumptions of the lack of memory, where simulations are based on homogenous matrices of transition probabilities. Examples of micro-simulation models that allow for heterogeneous transition patterns or mechanisms, e.g. of partnership formation, include SOCSIM (http://lab.demog.berkeley.edu/socsim/).
- 10.
After Franck (2002a), we interpret validation as a continuous process, rather than an achievable state.
- 11.
Reverse engineering denotes today diverse research practices varying with the areas of application. We refer to its initial sense.
- 12.
Bacon’s induction is regularly confounded with induction by philosophers in its usual sense of generalisation. Bacon wrote: “In establishing axioms, another form of induction must be devised than has hitherto been employed, and it must be used for proving and discovering not first principles (as they are called) only, but also the lesser axioms, and the middle, and indeed all. For the induction which proceeds by simple enumeration is childish; its conclusions are precarious and exposed to peril from a contradictory instance; and it generally decides on too small a number of facts, and on those only which are at hand” (Bacon 1620; aphorism 105).
- 13.
Formulating and testing hypotheses is not wrong, in our opinion, as long as it is based on empirical observations. However, throughout the present chapter we plead for abandoning the hypothetical-deductive approach and for substituting it with the classical induction.
- 14.
The property itself may not be generalized, of course.
- 15.
The principles are traditionally named theories; this tradition goes back to Plato’s theoria, and reserves to the term theory the restricted sense of a corpus of principles. This is far from its present use describing as a ‘theory’ every sort of conceptual hypothesis, or model, or explanatory ‘mechanism’.
- 16.
See for example the following definition of demography (IUSSP 1982): “the scientific study of human populations primarily with respect to their size, their structure and their development; it takes into account the quantitative aspects of their general characteristics”.
- 17.
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Acknowledgments
JB and ES acknowledge the Engineering and Physical Sciences Research Council (EPSRC) grant EP/H021698/1 “Care Life Cycle”. We thank Frans Willekens and Anna Klabunde for discussions and to the two anonymous reviewers for helpful suggestions. All the views and interpretations are those of the authors and should not be attributed to any institution with which they are affiliated. All the errors remain exclusively ours.
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Courgeau, D., Bijak, J., Franck, R., Silverman, E. (2017). Model-Based Demography: Towards a Research Agenda. In: Grow, A., Van Bavel, J. (eds) Agent-Based Modelling in Population Studies. The Springer Series on Demographic Methods and Population Analysis, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-32283-4_2
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