Nonlinear Difference Equations | Theory with Applications to Social Science Models

H. Sedaghat

ISBN 10: 1402011164 / ISBN 13: 9781402011160

Published by Springer Netherland, 2003

About this Item

Nonlinear Difference Equations | Theory with Applications to Social Science Models | H. Sedaghat | Buch | xv | Englisch | 2003 | Springer Netherland | EAN 9781402011160 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Seller Inventory # 102517399

Bibliographic Details

• Title: Nonlinear Difference Equations | Theory with Applications to Social Science Models
• Publisher: Springer Netherland
• Publication Date: 2003
• Language: English
• ISBN 10: 1402011164
• ISBN 13: 9781402011160
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 27D millimeters 241 millimeters width by 160 millimeters height by 27 millimeters depth
• Weight: 776 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

It is generally acknowledged that deterministic formulations of dy­ namical phenomena in the social sciences need to be treated differently from similar formulations in the natural sciences. Social science phe­ nomena typically defy precise measurements or data collection that are comparable in accuracy and detail to those in the natural sciences. Con­ sequently, a deterministic model is rarely expected to yield a precise description of the actual phenomenon being modelled. Nevertheless, as may be inferred from a study of the models discussed in this book, the qualitative analysis of deterministic models has an important role to play in understanding the fundamental mechanisms behind social sci­ ence phenomena. The reach of such analysis extends far beyond tech­ nical clarifications of classical theories that were generally expressed in imprecise literary prose. The inherent lack of precise knowledge in the social sciences is a fun­ damental trait that must be distinguished from "uncertainty. " For in­ stance, in mathematically modelling the stock market, uncertainty is a prime and indispensable component of a model. Indeed, in the stock market, the rules are specifically designed to make prediction impossible or at least very difficult. On the other hand, understanding concepts such as the "business cycle" involves economic and social mechanisms that are very different from the rules of the stock market. Here, far from seeking unpredictability, the intention of the modeller is a scientific one, i. e.