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Over the last several decades, mathematical models have become central to the study of social evolution, both in biology and the social sciences. But students in these disciplines often seriously lack the tools to understand them. A primer on behavioral modeling that includes both mathematics and evolutionary theory, Mathematical Models of Social Evolution aims to make the student and professional researcher in biology and the social sciences fully conversant in the language of the field. Teaching biological concepts from which models can be developed, Richard McElreath and Robert Boyd introduce readers to many of the typical mathematical tools that are used to analyze evolutionary models and end each chapter with a set of problems that draw upon these techniques. Mathematical Models of Social Evolution equips behaviorists and evolutionary biologists with the mathematical knowledge to truly understand the models on which their research depends. Ultimately, McElreath and Boyd’s goal is to impart the fundamental concepts that underlie modern biological understandings of the evolution of behavior so that readers will be able to more fully appreciate journal articles and scientific literature, and start building models of their own.
This book combines psychological and mathematical perspectives to analyse how qualitative mathematics can be used to create models of social and psychological processes.
"Highly recommended. . . . This is an important book in putting the burgeoning field of sociodynamics on a solid footing."—Journal of Artificial Societies and Social Simulation This text deals with general modelling concepts in the social sciences, their applications, and their mathematical methods. The author's well-organized approach offers a clear, coherent introduction to terminology, approaches, and goals in modelling. Appropriate for advanced undergraduates and graduate students, it requires a solid background in algebra and calculus. The three-part treatment begins by addressing general modelling concepts, the second part provides applications, and the third discusses mathematical method. Topics include population dynamics, group interaction, political transitions, evolutionary economics, and urbanization. Guiding students through a series of practical applications that illustrate the methods' potential scope, the text concludes with a detailed look at mathematical methods.
This book is devoted to the power of mathematical modelling to give an answer to a broad diversity of real problems including medicine, finance, social behavioural problems and many engineering problems. Mathematical modelling in social sciences is very recent and comes with special challenges such as the difficulty to manage human behaviour, the role of the model hypothesis with the objectivity/subjectivity and the proper understanding of the conclusions. In this book, the reader will find several behavioural mathematical models that in fact may be understood as the so-called epidemiological models in the sense that they deal with populations instead of individuals.
Study of the relevance of mathematics to the social sciences - covers mathematical and statistical methodologies and the use of computer modeling in behavioural studies, social policy formulation and operational research, examines the role of teaching mathematics in the university curriculum, etc.
The mathematical challenges coming from the social and behavioral sciences differ significantly from typical applied mathematical concerns. ?Change,? for instance, is ubiquitous, but without knowing the fundamental driving force, standard differential and iterative methods are not appropriate. Although differing forms of aggregation are widely used, a general mathematical assessment of potential pitfalls is missing. These realities provide opportunities to create new mathematical approaches. These themes are described in an introductory, expository, and accessible manner by exploring new ways to handle dynamics and evolutionary game theory, to identify subtleties of decision and voting methods, to recognize unexpected modeling concerns, and to introduce new approaches with which to examine game theory. Applications range from avoiding undesired consequences when designing policy to identifying unanticipated voting (where the ?wrong? person could win), nonparametric statistical, and economic ?supply and demand? properties.
Olinick’s Mathematical Models in the Social and Life Sciences concentrates not on physical models, but on models found in biology, social science, and daily life. This text concentrates on a relatively small number of models to allow students to study them critically and in depth, and balances practice and theory in its approach. Each chapter concluded with suggested projects that encourage students to build their own models, and space is set aside for historical and biographical notes about the development of mathematical models.