Model-Based Demography: Essays on Integrating Data, Technique and Theory

Front matter

Demographic Research Monographs

Thomas K. Burch

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Preface

The papers in this collection — most of them previously published — are the fruits of an intellectual odyssey over the last decades of my career as a sociologist/demographer. Beginning in the late 1980s, longstanding questions about the status of demography as a science came to the surface, and I began to pursue them actively. Looking back, I realize that at some point I became less a demographer and more a demography critic — cf. art critic or music critic — and an amateur philosopher of science.

My central concern has been with the role and status of theory in demography. For some, it was enough that demography did rigorous analysis of data using standard demographic and statistical techniques, notably emerging methods of multivariate analysis as applied to micro-data files. Theoretical explanations and models of behavioral processes often were left to other disciplines. Becker and the microeconomists had become the leading theorists of demographic behavior, while social demographers made relatively little systematic use of the large fund of relevant theory from sociology, social psychology, and cultural anthropology.

Microeconomic theory enjoyed widespread acceptance, if not consensus, among economists. And it was stated in clear, unambiguous form, often mathematically. Social-behavioral theory, by contrast, was formulated with less rigor, in loose verbal form, and commanded nothing approaching consensus.

As a graduate student in sociology and demography in the late 1950s, I had taken several excellent courses on social theory and cultural anthropology (Wilbert Moore; Marion J. Levy, Jr.; and Melvin Tumin) and trained in demography and statistics with leaders in the field — Frank Notestein, Ansley Coale, and Frederick Stephan. But there was little integration. My dissertation was a largely technical work on measurement of internal migration, with virtually no behavioral content and no theory. Some of my sociology professors were dismayed. My demography and statistics professors were satisfied if not ecstatic. As I pursued my career, I lived this schizoid life as an empirical demographer with an interest in theory — a small example of the split between theory and empirical research famously described by Robert Merton (1957). With a primary commitment to demography, my

Vili Preface relationship to theory, like that of the discipline, was characterized by ambivalence and malaise.

In reviewing the development of my thinking on these matters, I can single out three works as crucial. Robert Hanneman’s Computer-Assisted Theory Building: Modeling Dynamic Social Systems (1988) provided a detailed introduction to dynamic systems modeling as a potential theoretical tool for demographers and other empirical social scientists. It promised rigor in the statement and manipulation of theoretical models — including complex dynamic models with feedbacks and delays — and reoriented thinking away from comparative statics and equilibrium toward process and change. To this day, I remain puzzled why social scientists, including demographers, have made so little use of this powerful analytic tool.

An earlier work — discovered much later and by accident — was Explanation in Social Science: A System Paradigm by Eugene Meehan, a political scientist (1968). Meehan provided a convincing critique of logical positivism as a dead-end approach to social science and set forth a practical alternative involving ‘systems’ — roughly equivalent to theoretical models. He also insisted on the importance of purpose or aim, as well as logical consistency with data, in evaluating models. A model well-suited to one purpose may not be adequate for another.

Ronald Giere’s Science Without Laws (1999) appeared to me to support Meehan’s general approach, while placing it in the context of late twentieth-century philosophy of science. Accessible to the nonprofessional philosopher, this work argues that the model, not the law, is the central element in science. Models are not ‘true’ in any strong sense of that word. They simply fit some portion of the real world closely enough in certain respects to make them useful for certain purposes. At best, they embody ‘realism without truth.’

Taken together, these works convinced me that demography had more and better theory than generally recognized and pointed the way toward fruitful systematization and codification. Demography could be a full-fledged discipline, with its ample foundation of empirical data and technique balanced by a rich body of theory.

From time to time, I have wondered whether I had touched bottom with respect to the philosophical and methodological issues involved in demography as a science. Eventually, I realized there probably is no bottom. Professional students of science — philosophers, sociologists of science, and cognitive psychologists — disagree on many points. It is not likely that I would be able beat them at their own game and come up with a definitive view on science. I agree with Paul Teller (2001), who has warned against ‘the perfect model model’ of science, and with Samir Okasha who writes: ‘Like most philosophical questions, these questions do not admit of final answers, but in grappling with them we learn much about the nature and limits of scientific knowledge’ (2002, p. 39).

In any case, I am convinced that the model-based view of science as developed by Giere and others has much to offer demography as a liberating view of demographic theory. Its acceptance and routine application to our work could lead to a rich collection — a toolkit — of useful theoretical models, general, middle range, and

Preface ix

“low range.” As noted above, we can achieve a better balance among data, technique, and theory and become a complete science of human population.'

Even after a career of nearly 60 years in demography, however, I may be presumptuous to sit in judgment on the discipline and to suggest directions for its future development. But I have been encouraged by many other demographers who, over the years, have expressed their concern for the character and status of the field, their lingering feeling that something was missing. It seems to me that the model-based approach to science will encourage and enable us to provide what has been missing, notably a carefully crafted body of theory.

But just as there is no perfect model in science, there is no perfect model of science. And I am not a philosopher of science nor familiar with the practice and accomplishments of all the sciences, social, behavioral, biological, and physical. I can do no better than to close with a quote from E.O. Wilson. In the Preface to On Human Nature, in which he argues for the usefulness of evolutionary biology for understanding human behavior, he comments: “I might easily be wrong” (p. x). But it will be enough if this work promotes a lively discussion of what demography is and might become.

As lightly edited versions of papers written at different times and in different contexts, many of the following chapters repeat central ideas, for example, the contrasts between logical empiricism and the model-based approach to science, or the idea that much of ‘technical’ demography can be viewed as theory. Sometimes, this repetition may seem unnecessary. But it has the advantage that chapters are freestanding, so that the reader can read later chapters without having read all that preceded.

Victoria, BC Thomas K. Burch Canada

Giere, R. N. (1999). Science without laws. Chicago: University of Chicago Press.

Hanneman, R. (1988). Computer-Assisted theory building: Modeling dynamics social systems. Newbury Park: Sage Publications.

Meehan, E. (1968). Explanation in social science: A system paradigm. Homewood: The Dorsey Press.

Okasha, S. (2002). Philosophy of science: A very short introduction. Oxford: Oxford University Press.

Teller, P. (2001). Twilight of the perfect model model. Erkenntnis, 55, 393-415.

Wilson, E. O. (1978). On human nature. Cambridge MA: Harvard University Press.

' Adoption of a model-based view of science has the added advantage of encouraging cooperation and synthesis across disciplines. I develop this thought in: “The model-based view of science: an encouragement to interdisciplinary work.” 21st Century Society 1 (June 2006) 39-58. I was unable to obtain permission to republish in this open-source collection.

Acknowledgments

I begin by acknowledging the support of Jim Vaupel, Founding Director of the Max Planck Institute for Demographic Research, without whom this book would not be. Many years ago, he expressed interest in my work and urged me to bring it together as a book or monograph. In a casual conversation at the Rostocker Ring, in September 2015, I voiced regret that I had never followed through on his suggestion. His reply: ‘It’s not too late.’ Thus ended my retirement for a while.

Frans Willekens, also at Max Planck, worked out the contractual arrangements with the institute and regularly reassured me of the value of the project to the discipline. Given my age, I had some doubts about taking on a substantial editorial/ writing project. But it was clear to me that if Jim Vaupel and Frans Willekens thought it worthwhile, it was worth the time and effort.

Upon Frans Willekens’ retirement from Max Planck, Andre Schmandke took up the administrative tasks and helped negotiate a contract with Springer-Verlag. Further negotiations with Springer-Verlag went smoothly thanks to the prompt, clear, and helpful communications from Evelien Bakker and Bernadette Deelen. Carol Hamill (Victoria, BC) constructed the index; her work reminded me why it’s generally a good idea to go to a professional.

In 1993, in the early stages of this project, I had the privilege of spending a stimulating sabbatical term in the Department of Demography, University of Rome (La Sapienza), at the invitation of Antonella Pinelli. Graziella Caselli, of the same department, would later encourage my work on the model-based approach to teaching demography by invitations to present at two International Union for the Scientific Study of Population (USSP) meetings on the subject, with papers later published in Genus under her editorship (see Chaps. 11 and 12).

During her tenure as Director of the Center for Studies in Demography and Ecology, University of Washington, Martina Morris invited me to become a Regional Affiliate of the center and encouraged my participation in a year-long series of seminar on computer modeling and simulation. It was during visits to the CSDE that I first became aware of Adrian Raftery’s papers on the “two cultures of xi xii Acknowledgments quantitative analysis,” a distinction that helps explain much about contemporary demography and its approach to computer modeling. Chapter 5 in this volume is based on a presentation to this seminar in February 2004. The presentation was repeated in June 2007 at the Universities of Rome and Padua and published in Canadian Studies in Population, at the invitation of Frank Trovato, Editor.

Iam grateful to Francesco Billari, for his favorable response to my early work on marriage models (which led to an ongoing correspondence), but mostly for his pioneering work in bringing agent-based modeling into demography.

John J. Macisco, my oldest friend and demographic colleague, has been a steady source of encouragement over the years, reminding me from time to time that I had a right — but also an obligation — to tell it the way I saw it regarding the scientific status of demography.

Discussions with David Swanson regarding applied demography have provided a constant reminder of the importance of purpose or aim in the evaluation of any scientific analysis, an idea central to the model-based view of science.

Frank Trovato has provided regular encouragement of my work, and was directly instrumental in the writing and/or publication of at least three of the chapters below. The methodological work of Bill Wunsch and Ron Lesthaege have instructed me over the years, but, just as important, have reinforced my confidence in the importance of such work as applied to demography.

My interest in the status of sociology as a discipline has been kept alive over the last 16 years by regular ‘sociology seminars’ at local pubs with my friend Alan Hedley. David Johnston, friend and counsellor, helped me find strengths I didn’t know I had.

Most recently, I have had the good fortune to be in regular correspondence with Daniel Courgeau, Robert Franck, and Eric Silverman, who together and separately are making great strides in advancing the cause of demographic modeling. Chapter 3 derives from a working conference organized by Robert Franck and his edited volume of conference papers, The Explanatory Power of Models. Chapter 4 was first presented in a session on epistemology in demography, organized by Courgeau at the 2005 meetings of the IUSSP in Tours, France. Over the years, he has been generous in sharing his deep insights into the social science enterprise and honest in cautioning me if he saw me moving in a wrong direction. It was also he who introduced me to the ‘popular’ writings on scientific method by the French mathematician Henri Poincaré, whose 1908 book Science and Method in many ways anticipated the central ideas of late twentieth-century philosophy of science.

Lastly, I must acknowledge my congenial and supportive colleagues at the University of Western Ontario (now Western University) over the period 1975-2000 and at the University of Victoria (UVic), from 2001 to the present. I am especially grateful to Zheng Wu, who facilitated my appointment as Adjunct Professor at UVic, providing space, library privileges, and other support for my post-retirement activities.

Acknowledgments xiii

Much of the research for this work was supported by the Social Sciences and Humanities Research Council, Ottawa, Canada.

My apologies in advance to anyone I may have neglected to mention in these acknowledgments. Writing them has reminded me of how much the work of any individual depends on the help and support of others.

Part I A Model-Based View of Demography Data, Models, Theory and Reality: The Structure

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Chapter 1 Demography in a New Key: A Theory of Population Theory

The status of theory in demography has been problematic ever since I can remember. Sixty-five years ago, Rupert Vance, in his Population Association of American presidential address, asked ‘Is theory for demographers?’ (1952). There is ample evidence that many demographers — then and now — would answer ‘Of course, but it’s not a high priority.’ But if demography is a true science — as opposed to a body of techniques or a branch of applied statistics — it must have theory, recognize that it has theory, codify its theory, and seriously teach theory to its students. '

In his presidential Address to the Population Association of America, Nathan Keyfitz (1971) adopted what he termed a ‘liberal view of models.’ In this chapter, I sketch a liberal view of scientific theory, and discuss some of its implications for the way we think about demography and the way we present it to others.

This view of theory is known in philosophy of science circles as the ‘semantic’ view, or more recently and descriptively, the ‘model-based’ view of science. In describing this approach, I draw heavily on the work of Ronald Giere, an American philosopher of science (1988, 1999), but also on some methodological writings of Nathan Keyfitz (1971, 1975). Keyfitz introduced these ideas to demography years ago, although they never became mainstream.”

Based on a presentation to a Symposium on Theory in Demography, part of celebration of the new building of the Max Planck Institute for Demographic Research, 31 March—1 April, 2003, Rostock, Germany; originally published in Demographic Research 9(2003):263-284.

‘For a while, the cover of Demography (official journal of the Population Association of America) defined the field as ‘the statistical study of human populations,’ seeming to imply that demography is a branch of statistics, not a science in its own right.

71 also have benefited greatly from the following: Meehan (1968), Newton (1997), and Cartwright (1983, 1999). For a summary and assessment of the semantic school, see Teller (2001). I am grateful to John Wilmoth for reminding me that Keyfitz had written several papers on the role of models in demography.

© The Author(s) 2018 3 T.K. Burch, Model-Based Demography, Demographic Research Monographs, DOI 10.1007/978-3-319-65433-1_1

In the model-based view, models, not empirical laws, are the central element of scientific knowledge. A model is any abstract representation of some portion of the real world. A model may contain basic principles generally regarded as ‘laws.’ In this case, the laws ‘function as true statements, but not as statements about the world. They are then truths only of an abstract model.* In this context, such statements are true in the way that explicit definitions are true’ (Giere 1999, p. 6). A model contains generalizations, but they are formal generalizations, not empirical ones. Empirical assessment of theory, therefore, relates not to whether a theoretical model is empirically true or false — strictly speaking all theories and models are false because they are incomplete and simplified representations of reality — but ‘how well the resulting model fits the intended aspects of the real world’ (Giere 1999, p. 6). This view stands opposed to many familiar teachings of logical empiricism, by which theory is based on empirical laws, and judged true or false solely by its agreement with data. The model-based view is equally concerned with empirical data, but these are used to judge whether a model fits some portion of the world closely enough for a given purpose, not whether the model is true or false in any absolute sense.

The model-based approach has two general implications for our view of demography

1. Much of formal demography (techniques, methods) can be viewed also as theory, that is, as a collection of substantive models about how populations and cohorts behave;

2. Many theories in behavioral demography which have been rejected because of empirical exceptions or on the grounds they are too simplistic can be viewed as perfectly good theory, especially if they were to be stated more rigorously.

Indeed, at the theoretical level, the classic distinction between formal/technical and substantive/behavioral demography loses much of its force. In both sub-areas of demography, theoretical models have essentially the same epistemological standing, even if they may differ on other dimensions such as scope and complexity, and even if different kinds of day-to-day work may be involved in their development and use.

The word theory is ambiguous in the non-pejorative sense of ‘having two or more meanings.’ It means different things to different people, both in everyday speech and in scientific discourse. It is futile to try to establish the ‘correct’ definition or the ‘true meaning’ of theory. But it is possible to suggest a new — though not entirely new — approach to theory that might prove more fruitful than older ideas to which we are accustomed. In the next section, I summarize the main elements of the model-based view, noting some ways in which it differs from, but also agrees with, logical empiricism. A key part of this exposition is a partial re-definition of such terms as model and theory. But terminology is not crucial,

Cartwright refers to theoretical models as ‘nomological machines,’ that is, models generate laws, not the other way around. See (1999, p. 4).

and some may want to define these words differently, and to preserve a sharp distinction between theory and model. The central ideas I wish to convey are an emphasis on formal demography as substantive knowledge, and a plea that empirical exceptions to otherwise useful behavioral theories should not lead to their discard.

In the logical empiricist view of science, theory comes from data through a process of induction and generalization. Theoretical knowledge and empirical knowledge occupy different but parallel planes, layered upward into ever more general and abstract propositions. In the model-based view, theory and empirical studies occupy non-parallel planes. The planes must intersect, of course, since we are discussing empirical science. But the origin and character of the two kinds of knowledge are qualitatively different. In the model-based view of science, as the name suggests, models, not laws, are the central element of scientific knowledge. The prototype of scientific knowledge is not the empirical or theoretical law, but a model plus a list of real-world systems to which it applies. To quote Giere:

In this picture of science, the primary representational relationship is between individual models and particular real systems, e.g., between a Newtonian model of a two-body gravitational system and the Earth-Moon system. ..Here we have not a universal law, but the restricted generalization that various pairs of objects in the solar system may be represented by a Newtonian two-body gravitational model of a specified type. (Giere 1999, p. 93)

A model is any abstract representation of part of the real world, constructed to understand, explain, predict, or control. Giere distinguishes three types of models:

1. Physical models (for example, an automobile in a wind tunnel);

2. Visual models (for example, maps showing plate tectonics, or a diagram of the demographic transition);

3. Theoretical models (for example, Newton’s Law of falling bodies, or the theory of evolution).

Physical models have little relevance to demography and other social sciences. Visual models have great potential, but are not as widely used as they might be, with the bulk of graphics in demography limited to the representation of data frequency distributions, time series, and age-structures rather than processes or systems.

Theoretical models can be expressed in ordinary language, formal logical systems, mathematics, computer code or diagrams.*

In the model-based view, no sharp distinction is made between model and theory. A collection of small models relating to the same realm can be called theory (for example, the theory of harmonic oscillators, or the theory of population aging).

“The idea that theory consists of purely verbal statements seems peculiar to social science. In the physical sciences, many of the most important theories are in mathematical form — Newton’s law of gravity, Relativity, etc. For a recent indication of this way of thinking, see, for example, Baylis (1994). His book on Theoretical Methods in the Physical Sciences is an introduction to the use of a computer mathematics program, Maple V, to solve substantive problems in elementary physics.

These models typically contain a small number of variables, and are constructed to represent very limited portions of the real world. Or, theory can refer to a system of very general ideas (for example, the theory of relativity, or transition theory) attempting to represent larger, more complex real world systems. The difference is not qualitative, but relates to differences in scope, complexity, and other quantitative dimensions. There may be advantages to preserving fine distinctions among the words theory, model, and theoretical model in some contexts. In this chapter, they are used interchangeably.

Giere draws a useful analogy between scientific models and maps, viewed as simplified representations of our physical surroundings (1999, pp. 25-26; 81-82; 214-215), Like theoretical models, maps vary in purpose and scope. Some maps give a broad overview of nations or of whole states or provinces, and more detailed maps, often as insets, of smaller areas such as cities or metropolitan areas. Some maps are extremely simple. An example is the straight-line map found on metro trains or subways, which show only the stops and transfer points, which is all the rider needs to know. A topological map is useful for backpacking but no substitute for a road map. Maps differ in scope and detail, but all are abstract representations of reality.

A theory or theoretical model is a formal system: a set of propositions involving objects, variables, and relations among them. It must be clear and logically consistent. A model is constructed to represent or explain some empirical reality. But it need not be derived from empirical generalizations. And it does not have to be — indeed it cannot be — empirically true. In Giere’s words, models are true “in the way explicit definitions are true’ (1999, p. 6). They can never be absolutely and literally true because they are always partial and approximate representations of an infinitely complex real world. Scientific theories, he notes elsewhere (1988, p. xvi) can be viewed ‘not as empirical statements but as definitions of models variously related to the real world.’ And so, ‘Science does not deliver to us universal truths underlying all natural phenomena; but it does provide models of reality possessing various degrees of scope and accuracy’ (1999, p. 6). One can have, says Giere, ‘realism without truth.’

Keyfitz, discussing models of the demographic effects of eliminating deaths from heart diseases, comments similarly that his conclusions ‘...are conditional statements, and as such they are true beyond debate, given their assumptions that death rates by age from all other causes and birth rates by age of mother will remain as they are’ (1971, p. 574). Conclusions drawn from a model follow inexorably from assumptions and model structure. Later, he contrasts the firmness of these conclusions with those established by ‘direct observation, which tend to provide enigmatic and inconsistent reports’ (1975, p. 267).

Some authors distinguish theory and model, assigning the latter a role as intermediary between theory and empirical data. See for example, Gould and Tobochnik (1996) and Skvoretz (1998). Their distinction is on a general/specific axis and is not fundamental.

How then does one evaluate a model or theory? A model is a good model — Giere would not say a ‘true’ model — if it fits some portion of the real world (1) closely enough, (2) in certain respects, (3) for a specific purpose. All models are approximations. The question is whether the approximation is good enough for the purpose at hand. All models have a limited number of variables; none can mirror the numberless qualities of the real world. And finally, any model is to be evaluated with reference to the purpose for which it was designed or constructed.

The map analogy cited earlier helps clarify the last point. A highway map and a topographic map can both represent the same area. But the highway map is relatively useless for back-country hiking. It is not an incorrect or false representation, just the wrong one for the purpose. Similarly, a metro map correctly tells a rider where to get on and off the train, but is practically useless when one emerges above ground. A map of city streets is needed.

Over time in any science, some models receive widespread acceptance because they seem to embody central principles, or because they are widely applicable. In physics, classical mechanics provides an example. These models are taught in every introductory physics course. It is well understood that such models do not work as well at the sub-atomic level, or on the scale of the universe. But they are not therefore abandoned, since they serve many purposes in our everyday world.

The fit of a model to the real world is a matter for empirical examination. It is this empirical research that links model and data. But the conclusion that a model does not fit a particular case — perhaps not even closely — is only a conclusion that the model does not fit, not that the model is inherently false or invalid. It may well fit other cases. Decisions about whether or how well models fit the real world are based on scientific judgement, not on purely logical criteria. Giere again: ‘judging the fit of a model to the world is matter of decision, not logical inference’ (1999, p. 7).

The model-based view of theory has developed in opposition to logical empiricism, the dominant philosophy of empirical social science during the second half of the twentieth century. It differs from logical positivism in that the elements of a model do not have to be or be derived from or be logically consistent with broad — some would say universal — empirical generalizations or ‘laws.’ Such generalizations as exist may be incorporated into a model, but they are not essential. Many proponents of the model-based approach conclude that the logical empiricist program has been self- defeating precisely because empirical generalizations in social science are relatively rare. The model-based view agrees with logical empiricism in its emphasis on the importance of empirical observation. It is the