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Agent-Based Modelling in Population Studies - Concepts, Methods, and Applications (Hardcover, 1st ed. 2017)
Loot Price: R4,937
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Agent-Based Modelling in Population Studies - Concepts, Methods, and Applications (Hardcover, 1st ed. 2017)
Series: The Springer Series on Demographic Methods and Population Analysis, 41
Expected to ship within 10 - 15 working days
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This book examines the use of agent-based modelling (ABM) in
population studies, from concepts to applications, best practices
to future developments. It features papers written by leading
experts in the field that will help readers to better understand
the usefulness of ABM for population projections, how ABM can be
injected with empirical data to achieve a better match between
model and reality, how geographic information can be fruitfully
used in ABM, and how ABM results can be reported effectively and
correctly. Coverage ranges from detailing the relation between ABM
and existing paradigms in population studies to infusing
agent-based models with empirical data. The papers show the
benefits that ABM offers the field, including enhanced theory
formation by better linking the micro level with the macro level,
the ability to represent populations more adequately as complex
systems, and the possibility to study rare events and the
implications of alternative mechanisms in artificial laboratories.
In addition, readers will discover guidelines and best practices
with detailed examples of how to apply agent-based models in
different areas of population research, including human mating
behaviour, migration, and socio-structural determinants of health
behaviours. Earlier versions of the papers in this book have been
presented at the workshop "Recent Developments and Future
Directions in Agent-Based Modelling in Population Studies," which
took place at the University of Leuven (KU Leuven), Belgium, in
September 2014. The book will contribute to the development of best
practices in the field and will provide a solid point of reference
for scholars who want to start using agent-based modelling in their
own research.
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