Social Policy

Agent-Based Computational Demography: Using Simulation to by Francesco C. Billari, Alexia Prskawetz

By Francesco C. Billari, Alexia Prskawetz

Agent-Based Computational Demography (ABCD) goals at beginning a brand new flow of study between social scientists whose pursuits lie in knowing demographic behaviour. The booklet takes a micro-demographic (agent-based) viewpoint and illustrates the prospects of computing device simulation as an relief in idea development. The chapters of the ebook, written via top specialists both in demography or in agent-based modelling, handle numerous key questions. Why will we want agent-based computational demography? How can ABCD be utilized to the research of migrations, family members demography, and ancient demography? What are the peculiarities of agent-based types as utilized to the demography of human populations? ABCD is of curiosity to all scientists attracted to learning demographic behaviour, in addition to to desktop scientists and modellers who're searching for a promising box of application.

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Extra resources for Agent-Based Computational Demography: Using Simulation to Improve Our Understanding of Demographic Behaviour

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Insofar as it reflects the real world, it suggests how 28 Burch, 1'. K. lever s can be mo ved to alter direction in accord with policy requirem ents , Th e question is always how clo sely this con structed machine resembles the one operated by nature . 285 ). Note the equ ation of model and theo ry in the closing sentence. 4 A Model -Based View of Science The logical empiricist view of science has dom inated soc ial scie nce, including demogra phy, in the latte r hal f of the 20th century. According to this view, theory - a summary of what is known in a field - must be based on valid empirica l generalizations or laws.

But these two categories are not mutually exclusive; there are many hybrid or intermediate types. The distinction between theory and model is appropriate in this context, but as noted ea rlier, not in all contexts. Th ere are many modelling exercises in which a particular data set has the upper hand , as it were , with little input from theo ry. An extreme case is that of approximating Junctions, where a functi onal form is found that best represents a data set (two or thre e-dimensional ), without any regard to the substantive meaning of the function or its par ameters .

The important distinction is that emp irical models are not derived from assumptions concerning the relationships betw een variables, and they are not based on physical laws or principles. Quite often, empirical models are used as ' submodels,' or parts of a more complicated model. When we have no prin ciples to guid e us and no obvious assumptions sugge st them selve s, 11 See the important distinctio n by Abbott [ I I of the representational versu s the entailment interp retations or uses of the genera l linear model.

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