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Complexity Hints for Economic Policy

  • Book
  • © 2007

Overview

Authors:
  1. Massimo Salzano

    1. Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno, Italy

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    You can also search for this author in PubMed   Google Scholar

  2. David Colander

    1. Middlebury College, Middlebury, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: New Economic Windows (NEW)

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Table of contents (16 chapters)

  1. Front Matter

    Pages I-XXIII
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  3. Modeling Issues I: Modeling Economic Complexity

    1. Front Matter

      Pages 55-55
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    2. The Complex Problem of Modeling Economic Complexity

      • R. H. Day
      Pages 57-68

    3. Visual Recurrence Analysis: Application to Economic Time series

      • M. Faggini
      Pages 69-92

    4. Complexity of Out-of-Equilibrium Play in Tax Evasion Game

      • V. Lipatov
      Pages 93-117

  4. Modeling Issues II: Using Models from Physics to Understand EconomicPhenomena

    1. Front Matter

      Pages 119-119
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    2. A New Stochastic Framework for Macroeconomics: Some Illustrative Examples

      • M. Aoki
      Pages 121-143

    3. Probability of Traffic Violations and Risk of Crime: A Model of Economic Agent Behavior

      • J. Mimkes
      Pages 145-155

    4. Markov Nets and the NetLab Platform: Application to Continuous Double Auction

      • L. Muchnik, S. Solomon
      Pages 157-180

    5. Synchronization in Coupled and Free Chaotic Systems

      • F. T. Arecchi, R. Meucci, E. Allaria, S. Boccaletti
      Pages 181-198

  6. Applications

    1. Front Matter

      Pages 231-231
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    2. Income Inequality, Corruption, and the Non-Observed Economy: A Global Perspective

      • E. Ahmed, J. B. Rosser Jr., M. V. Rosser
      Pages 233-252

    3. Forecasting Inflation with Forecast Combinations: Using Neural Networks in Policy

      • P. McNelis, P. McAdam
      Pages 253-270

  7. Policy Issues

    1. Front Matter

      Pages 271-271
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Keywords

About this book

To do science is to find patterns, and scientists are always looking for p- terns that they can use to structure their thinking about the world around them. Patterns are found in data, which is why science is inevitably a qu- titative study. But there is a difficulty in finding stable patterns in the data since many patterns are temporary phenomena that have occurred r- domly, and highly sophisticated empirical methods are necessary to dist- guish stable patterns from temporary or random patterns. When a scientist thinks he has found a stable pattern, he will generally try to capture that pattern in a model or theory. A theory is essentially a pattern, and thus theory is a central part of science. It would be nice to have a single pattern - a unified theory - that could serve as a map relating our understanding with the physical world around us. But the physical world has proven far too complicated for a single map, and instead we have had to develop smaller sub maps that relate to small areas of the physical world around us. This multiple theory approach presents the pr- lem of deciding not only what the appropriate map for the particular issue is, but also of handling the map overlays where different maps relate to overlapping areas of reality. It is not only science that is focused on finding patterns; so too are most individuals.

Authors and Affiliations

  • Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno, Italy

    Massimo Salzano

  • Middlebury College, Middlebury, USA

    David Colander

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