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Nonlinear Difference Equations

Theory with Applications to Social Science Models

  • Book
  • © 2003

Overview

Authors:
  1. Hassan Sedaghat

    1. Department of Mathematics, Virginia Commonwealth University, Richmond, USA

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Part of the book series: Mathematical Modelling: Theory and Applications (MMTA, volume 15)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xv
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  2. Theory

    1. Front Matter

      Pages 1-1
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    2. Preliminaries

      • Hassan Sedaghat
      Pages 3-11

    3. Dynamics on the Real Line

      • Hassan Sedaghat
      Pages 13-69

    4. Vector Difference Equations

      • Hassan Sedaghat
      Pages 71-164

    5. Higher Order Scalar Difference Equations

      • Hassan Sedaghat
      Pages 165-239

  3. Applications to Social Science Models

    1. Front Matter

      Pages 241-241
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    2. Chaos and Stability in Some Models

      • Hassan Sedaghat
      Pages 243-337

    3. Additional Models

      • Hassan Sedaghat
      Pages 339-366

  4. Back Matter

    Pages 367-388
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Keywords

About this book

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.

Authors and Affiliations

  • Department of Mathematics, Virginia Commonwealth University, Richmond, USA

    Hassan Sedaghat

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