Introduction: Agent-Based Computational Demography - Springer Link

Introduction: Agent-Based Computational Demography Francesco C. Billari I, Fausta Ongaro/ and Alexia Prskawetz' * Institute of Quantitative Methods, Bocconi Universi ty, viale Isonzo 25, 1-20135 Milano, Italy, e-mail: f ran ce sco.bill a ri@uni -bocco ni.it Department of Statistical Sciences, University of Pado va, via Cesare Battisti 241/243 , 1-35121 Padova, Italy, e-mail : ongaro@stat. unipd.it Max Planck Institute for Demographic Research, Konrad-Zuse-Str, I, D-18057 Rostock, Germany, e-mail : fuern kr a n z@demog r .mpg . de

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Abstract. Originating from developments in computer science (also applied to natural science s), a computational approach to the study of human behavior has developed that has gathered impetus in the literature during the 1990s. Agent-based computational models have become more and more used in the social sciences (i.e . in economics with the idea of Agentbased Computational Economics (ACE) , and in sociology with the idea of Social Simulation) . Different to the approach based on statisti cal analysis of behavioral data that aims to understand why specific rules are applied by humans, agent -based computational models presuppose (reali stic) rules of behavior and try to challenge the validity of these rules by showing whether they can or cannot explain macroscopic regularit ies. In this introductory chapter, we argue that in order to study human populations, agent-based approaches are particularly usefu l from various theoretical perspectives . We urge demographers and other scholars interested in population stud ies to look at Agent-Based Computational Demography (ABCD) as a promi sing stream of research, which can improve our understanding of demographic behavior. We review and use the chapters of this book to substantiate our argumentations.

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Introduction: Demographers Are Almost Absent in a Flourishing Field of Research

Recently, the literature on agent -based modelling in social sciences has flourished . Thi s has particularly been the case in economics I , political science'", and - to a lesser extent - sociology' . During the 1990s, this computational approach to the study of human behavior developed through a vast quantity of literature. These include approaches that go from the so-called evolutionary computation (genetic algorithms " Views expressed in this paper are attributable to the authors and do not necessarily reflect the views of the institutions they belong to. I See i.e. the special issue on agent-based computational economics of the Journal of Economic Dynamics and Control [38), especially the introduction by Leigh Tesfatsion, as well as the website maintained by Lesfatsion ht tp: / /www . econ .iasta te . e du/tes f a tsi/ a ce . ht m. 2 See i.e. the review paper by Johnson [25). 3 See i.e. the recent review paper by Macy and Willer [30], or the review of Halpin [18].

F. C. Billari, et al. (eds.), Agent-Based Computational Demography © Springer-Verlag Heidelberg 2003

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and evolution of groups of rules) to the study of the social evolution of adaptive behaviors, of learning, of innovation, or of the possible social interactions connected to the theory of games. Different to the approach of experimental economics and other fields of behavioral science that aim to understand why specific rules are applied by humans, agent-based computational models pre-suppose rules of behavior and verify whether these micro based rules can explain macroscopic regularities. The development in computational agent-based models has been made possible by the important progress in information technology (in hardware as well as software agent technology), and by the presence of some problems that are unlikely to be resolved by simply linking behavioral theories and empirical observations through adequate statistical techniques. The crucial point is to use computing as an aid to the development of theories of human behavior: The main emphasis is placed on the explanation rather than on the prediction of behavior, and the model is based on individual agents, that is, agent-based modelling . In this introductory chapter, we argue that demographers and other scholars interested in population studies should consider Agent-Based Computational Demography (ABCD) as a promising stream of research that can improve our understanding of demographic behavior. The above-mentioned emphasis on individual agents and the traditional centrality of description and prediction in demography might explain the currently limited presence of demographic experts in this field. It therefore seems useful to us to depart from a parallel between the logic of scientific reasoning by statistical models, much more familiar among population scientists and the logic of scientific reasoning by simulations. In a monograph dedicated to simulation for soc ial scientists, Gilbert and Troitzsch [17] highlight both the complementary nature and the similarities - from a logical point of view - of the methods used to study social processes based on statistical and simulation models. A statistical model is necessarily an abstraction of a social process for which data is collected. The worthiness of applying the statistical model lies principally in the similarity between what it predicts and what is actually observed in the gathered data . Analogously, a simulation model constitutes the abstraction of an objective. This can be, for instance, a social/demographic process, but possibly also something that is not in reality verifiable, as for example in the case of the reconstruction of prehistoric societies. Computer simulation is used to perform the logical function of estimating the parameters in the approach for statistical models. The degree of similarity required between simulated data and gathered data is, however, less than that required for statistical modelling . If simulation is used, it is in fact because the empirical data only approximately, or on the surface, correspond to the objective. Simulation models provide a means to derive patterns of behavior (including demographic behavior, as we shall argue later on) . As outlined in Axelrod [I , p. 4], agent-based computational modelling may be compared to the principles of induction and deduction . "Whereas the purpose of induction is to find patterns in data and that of deduction is to find consequences of assumptions, the purpose of of agent-based modelling is to aid intuition" . As with deduction, agent-based modelling starts with assumptions . However, unlike

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deduction, it does not prove theorems . The simulated data of agent-based models can be analyzed inductively, however the data are not from the real world as in case of induction. We will take a look in this chapter at the issue of the use of simulations based on agents for theoretical purposes in a demographic perspective. We will place special emphasis on the link between hypotheses on micro- founded behavior and macro level demographic outcomes . We will show how the experience of scholars from other disciplines can be useful to demography but also how the experience of population scholars can be useful in a field that has autonomously developed quickly and in very broad terms. In Section 2, we will start with some observations on the gap between theory and analyzes of demographic phenomena, with particular attention given to micro-macro problems. In Section 3, we start outlining some additional gains of agent -based computational demography. In Section 4, we point towards difficulties and challenges for agent-based computational demography. We shall then look at demographic applications by briefly reviewing the applied chapters of the book in Section 5. This will help to carry out, in the final section, a critical reflection on the function of agent-based simulation for theoretical purposes in demography. It will also examine the possibility of a role for establishing agent-based computational demography as a stream of research .

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The Gap Between Theory and Techniques in Demography

Simulation is little used in demography as an aid to explaining behavior, partly because of the lack of general explanatory paradigms in demography. The explanation of macro (population-level) situations with micro-founded theories is without doubt one of the central points . With the latter end especially in mind, economists have the distinct advantage of starting from a strong mainstream paradigm that helps in linking theory and techniques. As Burch discusses in Chapter 2 of this book, in demography, there still seems to be a certain gap between the applied methodologies and the various theories of behavior [8]. His argument is that a strict adherence of demography to logical empiricism has hampered the development of "demographic theory" . Burch's idea of theory corresponds to an "imaginative construction in response to data, a construction that is true by definition, but not a true descrip tion of the real world" . By illustrating the epistemological directions of demographic research from a demographer's point of view, Chapter 2 helps create a foundation to make the case for agent-based computational demography within the framework of a philosophy of science. Let us concentrate on a specific issue concerning the gap between theory and techniques: the micro-macro problem. A well-developed, and developing, multilevel statistical approach can be used to test multilevel theories of demographic behavior when sufficient data are available . If there now seems to be general agreement on the fact that the explanation of demographic behavior requires a micro-founded approach, some key points contribute to the gap between this micro-demographic

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theory and demographic techniques. Here we shall list three particularly important problematic points : I. Poor level of precision in theoretical construction s: In demography, theories often offer little help in comparisons and understanding because of their basic vagueness. This has been clearly shown and thoroughly discussed by Burch [7J, and is implicit in the idea that many determinants whose link with behavior is weak are currently used [23J. This is certainly in opposition with mainstream economics (a field where formalization is a pre-requisite for a theory) , and is similar to what happens in sociology.

2. Insufficient theory at the basis of the applications of statistical models and of data collection: For example, the importance of the time dimension as the basis of the life course approach is not fully exploited in the design of surveys. Several scholars have argued for designing research plans that are more targeted to answering specific research questions (see e.g. [29]). 3. Non- (or difficult) observability, or insufficient accounting for observability of important quantities used in the theory: Decision making processes, social norms and social interaction processes in general are central elements of theories, but it is very difficult to analyze their impact using statistical models. Panel data sometimes provide some help but they also have their limitations.

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The Added Gains of Agent-Based Computational Demography

If we consider the set of demographic techniques, simulation is certainly not unknown . Demography has for a long time been using simul ation techniques, and microsimulation has become one of the principal techniques in this discipline, being a widely discussed and applied instrument in the study of family and kinship networks and family life cycle [19,20,35,41,44) . Microsimulation has also been widely used, in the field of human reproduction [32,34), migratory movements [12) or whole populations [28), and its role has been discu ssed in the context of longitudinal data analysis [46] (see for instance the review by van Imhoff and Post [42]). Microsimulation has been used to study and predict the evolution of population using a model for individuals. In fact, microsimulation is as important to the analysis of event histories as macrosimulation is to traditional, aggregate-level, demographic analysis. We argue that it would be useful - particularly in the study of micro -macro relationships and dynamic feed-backs - to build Agent-Based Computational Demography as a computational approach to demography, with emphasis placed on simulation based on agent s, including empirically-based microsimulation as a "special" case. Before moving onto the "target examples" included in this book, we shall try to list, in a schematic way, variou s advantages of adding ABCD to the set of demographic methods. The discussion will be broadened later on. I. In ABeD models , it is relatively easy to include feedback mechanisms and to integrate micro -based demographic behavioral theories (and results from

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individual-level statistical models of demographic behavior such as event history models) with aggregate-level demographic outcomes. This ability to include feedback is possibly the most important gain of ABeD models . In such models, space and networks can be formal ized as addit iona l entities through which the agen ts will interac t. As com pared to mathematical modelling, it is relatively easy to introd uce heterogeneous agents that are not fully rational. Hence, the paradigm of the representative, fully rational agent that has and often still penetrates many economic and sociological applications can easily be relaxed in agent-based modelling. Whe n building agent -based computational models, it is indispensable to ado pt simple formu lations of theore tical statements. Althoug h agen t-based modelling employs simulation, it does not aim to provide an accurate representation of a particular empirical applica tion. Instea d, the goa l of agent -based modell ing shoul d be to enrich our understandi ng of fundamental processes that may appear in a variety of app lications. This requi res adhering to the KISS princip le, whic h stands for the army slogan "keep it simp le, stupid " [I ]. 4 Using agent- based approaches, it is poss ible to construct models for which explicit analytical solutions do not exist, for example social interaction and generally non-linear models. Agent-based models are used to understand the functioning of the model and the precision of theories need not be limited to mathematical tractabi lity. Simp lifyi ng assump tions like homogenous agents can then eas ily be relaxed in the framework of an agent -based computational mode l. But as Axtell [2] notes, even when models cou ld be solved analyti cally or num erically, agent-based modelli ng techn iques may be applied since their out put is mostly visual and therefore easie r to communicate to peo ple outside academia. Besides these two applications, agent -based modell ing techn iques are usefu l for problems where "writing down equations is not a useful activity " . In many applications, the transient, out-of-equil ibrium behavior is of great er interest as the long run behavior of the model. By apply ing an agent-based modelling strategy, the dynamic path of the process under study can be followed. It is possib le to tryout the stud y of the hypotheses on decisional process es and behavior at an individual level, adopting a bottom-up approach . Thi s includes the possibility of studying the effect of cultural com ponents at an intergenerationa l level or at a level of social interaction r14], social norms [I I], value orientations and other quan tities which are hardly observable in quantitative studies. As nicely summarized in Epstein and Axtell [14] (p . 20), the aim is not (necessarily) to expla in socia l phenomena but to let it grow. The bottom- up approach may be contrasted to the top-down approach app lied in systems theory, which assumes aggregate state variables whose evolution is given by a set

Thi s approac h is however challenged within the agent-based modelling literature, especially among scholars with a primary interest in cogni tive processes. Conte [I OJ for instance, suggested to rephra se the principle to mean "keep it as simple as suitable" . Her idea is that agents need to have cogni tive properties which are not necessarily that simple. For cognitive scientists adhering to KISS principle, see i.e. the simple heuristics approach developed by the "ABC group" [16J.

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of equations. These system s of equations are then simu lated over time with the aim to replicate the observed social processes that may exhibit a wide range of dynamic behaviors ranging from convergent, oscillatory to even chaotic time paths of the underlying state variables. 7. It is possible to conceive artificial societies that need not necessarily resemble present societies; such artificial societies can be seen as computational laboratorie s or may allow to reproduce past macro-events from the bottom-up .

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Difficulties and Challenges for Agent-Based Computational Demography

Chapter 3, by Chattoe [9] helps in outlining the difficulties with modelling demographic beha vior using agent-based models, with reference to the diffusion of new contraceptive practices. Such difficulties become major challenges for scholars interested in the field. Chattoe lists and discusses five such difficulties. First, information and contraceptive activities have a "thin" character within the wider framewo rk of social action , i.e. only a small proportion of the overall social interactions are cau sally influencing contraceptive practices. Second, if the gap between theory and techniques exists in demography, it continues to exist when the technique is agentbased modelling . Third, if one does not adhere to KISS-type principles, it is particularly difficult to repr esent the con textu al nature, in time and space, of social action. Fourth , the same is true if one wants to represent the compl exit y of decision-making proc esses adequately, so that different factor s (i.e. norms , cost-benefit analysis, practices) are simul taneou sly modelled . Fifth , it is not necessar ily easy to collect the data that are necessary for building and testing agent-based models . Chapter 4, by Jager and Janss en [24 ] starts from a general discussion on the behavioral theories, most ly from a psychological and social-psychological perspective, needed to model demographic transitions . It target s the diffusion of innovative behavior such as contraceptive use. Jager and Janss en emphasize the need to use a meta-th eory of behavior as a framework for develop ing the rules governing agent-based model s. They illustrate the "consumat" approach as a general idea that can be applied to the diffusion of contraceptiv e use, and emphasize the importance of cognitive processes and the possibility of including such processes in complex agent-based models .

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Agent-Based Models in Demography: Examples in this Book

In this section, we briefly review the app licat ions of agent-based models in demography that are included in this book . We also put them in the context of the existing literature .

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Agent-Based Models in Spatial Demography

One of the mod els that historic ally is fundam ental to the current development of agent-based modelling is that of Schellin g [37], originally conceived to reflect upon

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racial segregation in cities in the United States . 5 The macro issue, or emergent feature, is the distribution in space of a population, which is heterogeneous by some characteristics and the subsequent spatial segregation which often arises . Schelling attempts to find mechanisms at a micro level that can explain such a distribution . The starting hypothesis is that the behavior of agents is based on a tolerance "threshold"; an agent does not tolerate living in an area in which there is not at least a determined percentage of people belonging to his race or ethnic group, and this causes himlher to move to another area. The prevailing idea in studies on space segregation of the population based on race, before the work of Schelling, was that tolerance of the agents was very low, and that this explained space segregation. The principal result of Schelling's model is that even with a relatively high tolerance, a space segregation of the population between blacks and whites is created as an emergent phenomenon. In general, the result for which this model is important - placing it at the root of recent developments in agent -based modelling - is that from micro strategies whose effect is not at all clear a priori, we can see important consequences at the macro level, which go towards the observed direction . Several models of spatial dynamics have been inspired by Schelling's model. The chapters by Heiland (22] and Benenson et al. (4] belong to this research agenda. In Chapter 5, Frank Heiland (22] applies an agent-based dynamic programming model to simulate state-level migration patterns from East to West Germany. In addition to conventional push and pull factors such as unemployment and income differentials between sending and receiving states, moving costs and job search costs are assumed to determine the decision to migrate . Contrary to conventional migration models, agents are assumed to be heterogeneous with respect to their location (in which eastern German state the potential migrant resides) and mobility (agents differ with respect to their moving cost) . The simulation results indicate that heterogeneity in mobility is crucial to explain the decline in cast-west migration that was observed after 1989. Stylized facts of the immigration pattern are well captured by the agent model, such as the fact that people who are unemployed are more likely to emigrate. Similar to the model by Schelling, the macrostructure of the distribution of migrants from eastern Germany to various states in western Germany, emerges out of micro level strategies of heterogeneous agents . In Chapter 6, Benenson, Omer and Hatna [4] use ABCD to explain the residential migration in the Yaffo area of Tel-Aviv. Different to Heiland [22], they assume that migration decisions are not related to distance and are based on residential dissonance which depends on agents characteristics (as represented by the architectural style of their building) and their neighborhood. Householders are classified in three cultural groups (Jews, Arab Moslem s and Arab Christians) and modelled as reactive agents who recognize the architectural style of their building they live in and the cultural affiliation of their neighbors. Residential choice is modelled in three 5

Some implementations of Schelling's model are available and usable online, i.e. the one by Raj R. Singh at ht t p: / /web.mi t. edu /ra j s i ngh /www /al ife / schelli ng.html or the one by Denis Phan (in French) at ht tp: / /www -e co. e nst -b retagne . fr rphan / c omp l e xe / s c he l l i ng . ht ml

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stages: Agents decide on whether to migrate. In a second step , agents randomly select houses with vacant dwellings and in a third step agents occupy the vacant dwellings (either by randomly selecting a vacancy or by first ordering the vacancies by attractiveness). The model fits well the real world residential dynamics in Yaffo, and demonstrates that the approach of ordering vacancies (as opposed to randomly selecting a dwelling among vacancies) is the one that better fits actual data . Similar to Schelling's initial model of spatial segregation, this paper demonstrates that the residential distribution of householders across the Yaffo area may be explained by micro level decisions of households that are heterogeneous with respect to their cultural affiliation.

5.2 Agent-Based Models in Family Demography Agent-based modelling can contribute to research in family demography because decision-making processes are a key object of study in this field, and because the interaction between individuals (i.e. on the partnership formation market) is a fundamental aspect of family demography. In this book, the applications presented concern partnership formation (Chapter 7 by Todd and Billari [40]), and the long-term persistence of social norms concerning partnership formation (Chapter 8 by Billari, Prskawetz and Ftirnkranz [6]) . The two chapters have rather different focuses : Chapter 7 focuses on decision-making during the life course, while Chapter 8 focuses on the long-term evolution of norms as life-course decision-making component. Todd [39) discusses decision-maki ng processes in partnership formation and used a simple agent-based mode l to study such processes, based on satisficing heuris tics. Todd 's choice is to start with potentially simpler algorithms of choice and to study the consequences through simulating mating through different stra tegies . He shows by simulation that a faster choice is not necessarily sub optimal, but can give "satisfactory" results (as identified by Simon), or even better results, when one changes point of view. In line with Axelrod's KISS principle, Todd starts with simple rules on searching that can be psychologically plausible. Todd's approach is used to compare decision-making rules with population-level outcomes by Billari [5], who emphasized the role of the heterogeneity of individuals. Chapter 7, by Todd and Billari [40) goes deeper by explicitly combining the micro-level problem of uncovering the mechanisms by which people select mates or marriage partners and the macro-level problem of understanding the emergence of population-level patterns of marriage. The agent-based mode l is developed by hypothesizing that individual agents search for mates by using individual aspiration-based satisficing heuristics, which are simple, psychologically plausible mechanisms suggested by the framework of bounded rationality. The population-level patterns resulting from the model in different types of simulations are compared with the general features of observed patterns of age-at-marriage. These patterns have historically been addressed by demographers and sociologists by using macro-level models. Todd and Billari show that the psychologically-plausible approach based on heuristics produces patterns that resemble those of observed population when the heterogeneity across individuals is introduced.

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Chapter 8, by Billari, Prskawetz, and Fiimkranz [6] addresses an issue that has more general implication than those in family demography: the long-term persistence of cultural norms on demographic behavior. The issue of age-norms is of central importance in research on the life course, where the debate focuses on whether such norms may resist in the presence of a general individualization process pervading post-modem societies. The authors develop an agent-based simulation model to study the evolution across generations of age-at-marriage norms. In their definition, norms are constraints built in the programming of agents, and they can be transmitted to the next generation only by those agents who are able to marry and thus have children. Social influence may also playa role, because agents can derive their norms in conformity to the majority of the population. The Chapter shows, with this highly stylized model, that under certain conditions, the long-term persistence of age-at -marriage is assured. It also shows the importance of path dependence in this type of processes, because the initial situation has often an impact on long-term outcomes.

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Agent-Based Models of Historical Processes and PopulationDevelopment-Environment Interactions

Agent-based models have been applied to study and understand small-scale human and primate societies (an excellent review on this effort is issued by the Santa Fe Institute [26]). The definite strength of applying artificial societies is that they allow for controlled experiments, e.g. how changes in behavior of agents or the environ ment may have contributed to the macroscopic regularities of societies one observes. This is particularly relevant for ancient/historical societies for which we often have only indirect data available . One of the most famous examples in this field is the study by Dean et a!. [13] on the Anasazi society, which aimed to propose a set of agents rules to explain the settlement and farming dynamics of the Anasazi society ([3]) . The implementation is based on the well-known Sugarscape model created by Epstein and Axtell [14] . The main purpose of this model is to show that "a wide range of important social , or collective, phenomena can be made to emerge form the spatio-temporal interaction of autonomous agents operating on landscapes under simple local rules" [14]. The last three chapters of the book are closely related to the sugarscape model and its application to historical societies and sustainability issues. In Chapter 9 it is shown how agent based modelling may contribute to improve exiting demographic tools that are used to simulate historical societies . Chapter 10 continues to show the use of agent based modelling to better understand the demographic and economic development in the aftermath of mortality crises, while Chapter II investigates the role that hierarchical agent structures may play for long run sustainability. To model historical populations. demographers have commonly applied the toolkit of microsimulations. However as indicated in Chapter 9 by Murphy [31], these models often produce greater variability as observed in the dynamics of human populations. Referring to the concept of agent -based modelling, the author proposes including feedback mechanisms from the macro level to demographic deci-

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sion processes at the micro level. In fact, these mechanisms were already discussed by Malthus in terms of positive and preventive checks. Based on Hajnal's NorthWestern European household system , the authors introduce a feedback mechanism from population size to nuptiality in a standard microsimulation model that aims to replicate the long term population growth of a relatively small community of a pretransitional population with stationary levels of fertility and mortality. Their simulation results show that nuptiality adjustments are sufficient to maintain population homeostasis even in small populations that would be highly unstable otherwise. The author then compares alternative strategies to maintain communities and lineages (e.g . a stem family system that promotes nuptiality of one son) and shows that this adds only small reductions in variability in addition to the feedback mechanism already introduced before. From this exercise it clearly follows that common methods applied in demography - as it is definitely the case for microsimulation - may be improved by admitting an explicit link from the macro to the micro level which is one of the basic underlying paradigms of agent based modelling. In Chapter 10 Ewert , Roehl and Uhrmacher [15] apply a multi-agent based model to investigate the demographic and economic consequences of mortality crises in pre-modern European towns . Their approach is novel in this field, modelling individual agents in the town's population that interact as consumers and suppliers via several markets and the existence of a planning agent (the local authority) who may intervene into market and social structure. Disasters are then introduced as shocks that will imply a change in the actions of the agents according to their preferences. The authors present simulations for three different types of mortality crises as induced by different disasters (e.g . a famine, a plague and a storm tide) and show that, depending on the type of mortality crises , different actors are affected differently. While a pure macro level viewpoint indicates that European mortality crises have only had a temporary effect on population and economic development, the work by Ewert and his colleagues highlights the importance of modelling heterogeneous agents, since different groups of the population may have been affected differently. This work constitutes a clear designed example for the application of agent based models, as it aims to develop a set of micro -level decisions and interactions that may explain the observed macro-level effects of mortality crises . The final chapter of the book (Chapter J1 by Konig, Mohring and Troitzsch [27]) is an extension of the well-know sugarscape model as discussed in Epstein and Axtell [14] . By assuming that some agents may act as co-ordinators while others choose to be subordinates, the authors model the emergence of a hierarchy and show that sustainability can more easily be achieved than in a society with isolated agents. Based on simulations, the authors show that agents are more likely to subordinate during times when the average food supply per agent is low, while they are more likely not to subordinate or unsubordinate when average food supply increa ses. This paper nicely demonstrates that agent based models allow construction of alternative informational structures that may be more sophisticated as simple macro-micro linkages.

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6 Discussion: The Potential Roles of Agent-Based Computational Demography Demographic applications of simulation (microsimulation) were traditionally created for numerous purposes: In depth studies of the (generally macro) consequences of certai n behaviors at a micro level, predictions, analysis of the impact of policies, evaluation or estimate of missing data or possib ly low quality data . Such models do not exclude the possibility of being particularly comp licated, with several processes along the time axis, multiplying the number of states that individuals can cover. This introduces the possibility of obtaining summary measures of the phenomena of interest. In all these operations, the change between individua ls' states is regulated by a predefined group of transi tion rates or probabilities estimated a priori by the researcher, and which are the motor of the simulation. ABCD, the stream of research discussed here, is pointed toward a sligh tly differen t level. As with the traditional approach, our proposal is also an instrument for modelling reality with the ultimate aim of better understanding the social processes under way, perhaps through forms of predictions. As opposed to the traditional approach, here the simulation is used first of all to develop and explore theories rather than to evaluate empirically the consequences of given rates/probabilities. These are undoubtedly close to the empir ical basis typical of demography - to the individu als and the logic that guides their behavior (socia l simulation or agent-based modelling). Operationally, the path followed with this approach requires us to: elaborate a theory that explains individual behavior (the influencing variables can belong to micro and/or macro levels); formalize the theory and express it in a procedure that can be translated into a com puter programme; run the programme to observe the simulated behavior with a sufficiently high number of agents or runs to guarantee a result uninfluenced by chance errors; and analyze the results - at a macro and/or micro level. The goal should be obtaining - if necessary with empirical com parisons - elements that help confirming (a stage which, however, is elusive) or rejecting the theory, hopefully with suggestions for its improvement. Some characteristics of this approach that are included in wider range choices are worth recall ing, at least to stimulate a discussion in demography. We conclude by discussing the characteristics of ABCD .

6.1

A Central Role for Individual Agents

In the artificial societies designed in social simulations, the agents are individ ual actors that follow rules of behavior and possibly interact with other individual subjects. This does not mean that macro level units (social rules, institutions, etc.) cannot be added to such elements, only that a dynam ic relation is taken on between micro and macro based on individual action . Complex versions of social simulation (see e.g. [36]) provide for dynamic feed-backs between both components. The problem is, if there is a problem, to expla in how individuals affect collective entities with their behavio r and how these entities influence the actions of individuals. Demographers are familiar with models that try to explain individua l behav ior by incorporating micro

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and macr o factors: Multilev el statistical models are an example of this and have been flourishing in the field. Much less widespread are approa ches in which the products at a micro level enter the cycle and with time influence micro behavi or (disregarding for the time being the macro-macro relations ). In traditional microsimulation, the analysis usuall y stops at the macro demographic results of individual behav ior, without considering their possible feedback on agents . Operations of this natur e can however be provided in a simulation context such as the one proposed here (cf. Chapter 9 by Murphy [31]) .

6.2 A Central Role for the Heterogenei ty of Agents A definite strength of agen t-based modell ing is that we do not need any assumption on the hom ogeneity of agents. There is no doubt that the heterogeneity of popu lations is at the core of populati on models (i.e. [43]). Although it is standard to differentiate the population by age, sex, family status, education and variou s other demographic and socia l characteristics, it is often difficult to control for those factors that cannot be observed ("unobserved hetero geneity") . Agent-based models that can be used as computational laboratories offer a tool to pin down factors that may accoun t for the unob served heterogeneity. A set of simu lation runs that differ in the degree of heterogeneity among agent s may be applied with the aim to better approximate macroscopic regularities of the society. Chap ter 7 by Todd and Billari [40] is an example.

6.3 " Social" Interaction among Agents Demographic processes such as fertilit y, nuptiality and divorce have stimulated the development of two-sex model s in formal demography, as well as other formal mod els of social interaction. The se mode ls pose special prob lems that are illuminated when one adop ts an agent-based appro ach [9]. These model s also quickly become analyti ca lly difficult to solve and at a certain degree of realism they require the use of simulations. Agent -based mode ls provide a tool to stud y demographic processes as outcome of interacting agent s (see i.e. the development of hierarchies among agent s in Chapte r 11 by Konig, Mohring and Troitz sch [27]) .

6.4 Focus on Dynamics Rather than on Equilibria Formal demog raphy has long been centered on the stab le and stationary population mode l since these model s permit analytical results . However, the dyna mics of popu lation processes and population struc tures require a dynam ic and out of equilibrium modelling strategy . Since agent -based computational models follow this paradigm, they are well suited for the study of demographic processes. The question whether those dynamic changes are due to compositional changes or possibly due to changing behavioral rules of the individual age nts also can be studied with in the fram ework of agent-based computational model s as well.

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Explanation via Social Mechanisms

Agent-based simulation may be a useful instrument for formulating more precise and rigorous theories. The translation of the theory into a model to use on the computer does not only imply the need to select the "potential" variables in use (independently of the fact they are empirically observable) but also to organize them according to a system of relations based on the social mechanism concept. The explanations for mechanisms obviously have an autonomous dignity, but the social simulation adapts well to accept and test them . Let us examine some definitions of mechanisms from the volume by Hedstrom and Swedberg [2 J]. According to Elster mechanisms are half way between laws and descriptions. They are "frequently occurring and easily recognizable causal patterns that are triggered under generally unknown conditions or with indeterminate consequences. They allow us to explain but not to predict" (p. 45, emphasis in the original text). Hemes believes they are "an abstract representation that gives the logic of a process that could have produced the initial observation" (p. 78). In the approach based on social mechanisms, the basic idea is that individual behavior can and must be explained by various individual and "environmental" determinants. The aim of the analysis is to estimate the causal influence of the different variables representing that determinant. The agents' changes of state do not therefore depend on more or less correctly estimated rates/probabilities but on individualization and the subsequent formalization of explanatory mechanisms. Explanation via social mechanisms highlights significant connections between events and in this way differentiate themselves from both theories based on laws and those centred on variables (based on statistical associations between variables). Other disciplines, such as biology, behavioral psychology and economics, are much more familiar with this approach . Demography has in the last decade recorded interesting advances in both theories and methods (recent considerations on this can be found in Palloni [33]). The approach by mechanisms, more oriented towards the generation of theories, may offer another contribution to the explanation of demographic behav ior.

6.6

Simplicity of the Propositions and Emergent Phenomena

In some of the applications in the following chapters (i.e. [4]), the explanation of behavior is based on simp le propositions about individual behavior. However, the consequences at a macro level of such behavior are not simple . The basic idea is that the analytical models that explain the actions should only include elements essential to the analysis of the problem and that micro behaviors can also produce complex macro situations (and then feedback). This is an unusual formulation for demography, though it is not new in disciplines such as physics and biology. In some ways, is the exact opposite of the one centred on variables mentioned above .

6.7

A Demographic Laboratory

In social sciences, experimental research is often difficult to practise for obvious ethical reasons but also due to the difficulty of alignin g with the speed of evolution

14

Billari, F.e., Ongaro, F. and Prskawetz, A.

of the processes . Developments in recent years in empirical analysis (on sources and meth od s) have allow ed us to keep the relevant factor s unde r better control. However, at least four types of probl ems rema in that can easil y be adequately resolved with such an approac h: I . Investigating the role off actors that are not empi rically observable. Numerous efforts have been made in this direction, extending the field of variables sub ject to these observation s, singling out variables that are more and more proxy than the unkn own ones, introducing unob served heterogeneity into statistical models, developin g the concep t of latent variab les, etc . Resu lts from these investigatio ns are not always fully satisfactory: Concepts such as those for social norms, intergenerational transmiss ion, or preferences, individual willingness to change - used more and more in theoretical reasoning to explain demographic behav ior - are still often too difficult to treat empirically. 2. Studying rare events. A careful study of these event s would require the use of specific observations which , because of cost reasons, arc often disregarded. 3. Following the evolution of life courses through time. The study of the evo lution of life courses poses some prob lems : among them , the difficulty in understanding the effect of the explanatory components through time ; the difficulty of collecting individual variab les expressing propensity, states of mind , etc . in a time varyi ng form . These cannot, in fact, be the object of retrospective surveys, in additi on , with panels, the observation window s cannot extend over time periods that are too broad. Agent-based computational model s provide the researcher with a privileged artificial observatory without the limits of the empirical approach . Here it is possible to introduc e observable or non-observable variables, substitute or complicate them according to interpretative necessities or app ly them to a sufficiently wide number of subjects or for a sufficiently long per iod of time . The result s of this experiment may also supply some ideas for the more specific individualization of variables for insert ion into the em pirical survey. 4 . Approaching demographic behavior f rom evolutionary perspectives. The view that adopting an evolution ary point of view would add sub stantial power to our unde rstanding of demographic behavior has been discussed by various authors (i.e. [45]). Agent-based modell ing con stitutes a clear opportunity to test evolu tionary idea s on demographic behav ior.

6.8

Conclusions

How should the field of demography react with respect to agent-based modelling? We have attempted to demonstrate here that dem ography, by its specific nature, can activel y contribute to the promotion of theories formula ted in other disciplinary contexts . It can introdu ce the heterogeneity of agents, as regards the time of the search, into Todd's model on search ing for a partn er. It can link the results of its application at a macro level back to a well-known curve (marriage by age). Demography offe rs promises for further defining such a model. Mo re generally, the

Introduction

15

intere st of demographers in agent-based simulation lies in the possibility of having at their disposa l an extra instrument for better specifying the mechanisms at the route of demographic phenomena. This can be obtained by looking at interpretative logic proposed by other schola rs of human behavior (psychologists, sociologists, economists) or by trying to better clarify the environmental and individual explanatory factors evoked by demography ignoring empirical ties. There is no hiding the fact that the widespread use of this type of instrument in the demographic community may hit some obstacles: What is required is a new perspective for observations of the phenomena and some not indifferent investmen ts in training in fields (analytical mode ls, methods of simulation) that demographers do not traditionally frequent. The advantages, however, would be a further source of reflection on the theoretic aspects of the discipline and an incentive for interdisciplinary involveme nt. Joining the AB community would boost the principal of interdisciplinary research, thereby lowering disciplinary borders. As Epstein and Axtell say: "Herbert Simon is fond of arguing that the social sciences are, in fact, the hard sciences . For one, many crucially important social proce sses are complex. They are not neatly decomposable into separate subprocesses - economic, demographic, cultura l, spatialwhose isolated analyzes can be aggregated to give an adequate analysis of the social process as a whole. And yet, this is exactly how social science is organized, into more or less insular depar tments and jo urnals of economics, demography, political science, and so forth (...)"[14] (Ch. I) .

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