Courtney M. M. Brown 
Chaos and Catastrophe Theories [EPUB ebook] 

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Chaos and catastrophe theories have become one of the major frontiers in the social sciences. Brown helps to clarify this complex new technique for modeling by approaching it with the following questions: What is Chaos? How can it be measured? How are the models estimated? What is catastrophe? How is it modeled? Beginning with an explanation of the differences between deterministic and probabilistic models, Brown introduces the reader to chaotic dynamics. Other topics covered are finding settings in which chaos can be measured, estimating chaos using nonlinear least squares, and specifying catastrophe models. Finally, the author estimates a nonlinear system of equations that models catastrophe using real survey data. Researchers wanting to understand and make use of this exciting new direction in social measurement and modeling will find this book an excellent and cogent introduction.

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Daftar Isi

Working with Deterministic Mathematical Models
PART ONE
What Is Chaos?
Measuring Chaos
Estimating Chaos Models
PART TWO
What Is a Catastrophe?
Strategies for Specifying Catastrophe Models
Estimating Catastrophe Models

Tentang Penulis

Courtney Brown is an Associate Professor in the Department of Political Science at Emory University.  Dr. Brown has taught differential equation modeling to graduate and undergraduate students for over 20 years.  His teaching and research interests also include other quantitative methods, political musicology, science fiction and politics, electoral behavior, political parties, democratic development, and politics and the environment.  He has authored five books that deal with differential equation models in the social sciences, including three titles for the Quantitative Applications in the Social Sciences series.

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Bahasa Inggris ● Format EPUB ● Halaman 88 ● ISBN 9781506339948 ● Ukuran file 2.2 MB ● Penerbit SAGE Publications ● Kota Thousand Oaks ● Negara US ● Diterbitkan 1995 ● Edisi 1 ● Diunduh 24 bulan ● Mata uang EUR ● ID 5366553 ● Perlindungan salinan Adobe DRM
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