Social Synthesis: Finding Dynamic Patterns in Complex Social Systems (Hardback) book cover

Social Synthesis

Finding Dynamic Patterns in Complex Social Systems

By Philip Haynes

© 2018 – Routledge

206 pages

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Hardback: 9781138208728
pub: 2017-08-28
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About the Book

How is it possible to understand society and the problems it faces? What sense can be made of the behaviour of markets and government interventions? How can citizens understand the course that their lives take and the opportunities available to them?

There has been much debate surrounding what methodology and methods are appropriate for social science research. In a larger sense, there have been differences in quantitative and qualitative approaches and some attempts to combine them. In addition, there have also been questions of the influence of competing values on all social activities versus the need to find an objective understanding. Thus, this aptly named volume strives to develop new methods through the practice of 'social synthesis', describing a methodology that perceives societies and economies as manifestations of highly dynamic, interactive and emergent complex systems. Furthermore, helping us to understand that an analysis of parts alone does not always lead to an informed understanding, Haynes presents to the contemporary researcher an original tool called Dynamic Pattern Synthesis (DPS) - a rigorous method that informs us about how specific complex social and economic systems adapt over time.

A timely and significant monograph, Social Synthesis will appeal to advanced undergraduate and postgraduate students, research professionals and academic researchers informed by Sociology, Economics, Politics, Public Policy, Social Policy and Social Psychology.

Reviews

I highly recommend this book which has several case-studies of complex change over time. Complexity theory fits the social sciences well because there is both stability and instability in the social patterns. Here we find good empirical examples. The author observes patterns over time using three main methods: a complex cluster analysis, the discerning of prime implicants from among the configuration’s characteristics, and Boolean truth-table analysis. The author thus reduces and simplifies the findings. The book makes extensive use of Qualitative Comparative Analysis (QCA) while extending this ‘mixed method’ to an intertemporal range.

Wendy Olsen, Reader in Socio-Economic Research, The University of Manchester, UK

This book responds to two important currents influencing contemporary social science: critical realism and complexity science. It provides an account of a promising new analytic method, Dynamic Pattern Synthesis, and illustrates how one can use the method with examples including the analysis of health and social care. I look forward to the application of this innovative and powerful method to a wide range of policy-relevant topics.

Nigel Gilbert, Professor of Sociology, University of Surrey, UK

Table of Contents

List of Boxes

List of Figures

List of Tables

Acknowledgements

Abbreviations

Introduction

Chapter One: Methodology: towards a representation of complex system dynamics

Introduction

Complexity Science

The classical reductionist method

Beyond reductionist science

Sensitivity to initial conditions

Emergence

Autopoiesis

Feedback

Networks

Summarising the influences of complexity theory

Understanding system change as patterns

Complexity in economic systems

Time and Space

Critical Realism

Case similarity and difference

Convergence and divergence

Complex causation

Methodological conclusions

Mixed methods

Conclusions

Chapter Two: the Method - introducing Dynamic Pattern Synthesis (DPS)

Introduction

Cluster Analysis (CA)

Cluster Analysis: specific approaches

Distance measures

Hierarchical and non-hierarchical cluster analysis

Clustering algorithms

Dendrogram charts

Icicle chart

Using SPSS to calculate and compare cluster methods

Further considerations of the effects of clustering algorithms

Understanding variable relationships within cluster formulation

Repeating Cluster Analysis over time

Qualitative Comparative Analysis (QCA)

Crisp set QCA

Accounting for time in case based methods

Combining the two methods: Cluster Analysis and QCA

QCA and software packages

Applying QCA

An alternative confirmation method: ANOVA

The application of Custer Analysis and QCA as a combined method

Dynamic Pattern Synthesis: seven cities, three years later

Threshold setting for binary crisp set conversion

Primary Implicant ‘near misses’

Other considerations for the Dynamic Pattern Synthesis

The stability of variables in DPS

Stability of cases in the chosen sample

The size of the chosen sample

The number of time points in the DPS

Conclusion

Chapter Three: macro examples of Dynamic Pattern Synthesis (DPS)

Introduction

Macro case study 1: health and social care in Europe

Macro Case study 1, wave 1, 2004

Macro case study 1, wave 2, 2006

Macro case study 1, wave 4, 2010

Macro case study 1, wave 5, 2013

Macro case study 1: conclusions

Case

Variables

Patterns

Macro case study 2: the evolution of the euro based economies

Macro case study 2, wave 1, 2002

Macro case study 2, wave 2, 2006

Macro case study 2, wave 3, 2013

Macro case study 2: conclusions

Cases

Variables

Patterns

Chapter Four: A meso case study example: London Boroughs

Introduction

Meso case study: 2010

Meso case study, 2011

Meso case study, 2012

Meso case study: conclusions

Cases

Variables

Patterns

Chapter Five: micro case study example: older people in Sweden

Micro case study: older people in Sweden born in 1918

Micro case study: wave 1, 2004

Micro case study, wave 2, 2006

Micro case study, wave 4, 2010

Conclusions for the micro case study

Cases

Variables

Patterns

Chapter Six: Conclusions

Dynamic Pattern Synthesis (DPS) and different dynamic typologies

Variable patterns

Case patterns

The stability of case and variable interactions: towards some typologies

Stable dynamics

Case instability

Cluster resilience

System Instability

Reflections on complexity theory and DPS

Interactions

Short and long range interactions and feedbacks

System openness and dynamics

Case and Data Patterns

Case dynamics and complexity theory

References

Index

About the Author

Philip Haynes is Professor of Public Policy in the School of Applied Social Science at the University of Brighton, UK.

About the Series

Complexity in Social Science

This interdisciplinary series encourages social scientists to embrace a complex systems approach to studying the social world. A complexity approach to the social world has expanded across the disciplines since its emergence in the mid-to-late 1990s, and this can only continue as disciplines continue to change, data continue to diversify, and governance and responses to global social issues continue to challenge all involved. Covering a broad range of topics from big data and time, globalization and health, cities and inequality, and methodological applications, to more theoretical or philosophical approaches, this series responds to these challenges of complexity in the social sciences – with an emphasis on critical dialogue around, and application of these ideas in, a variety of social arenas as well as social policy.

The series will publish research monographs and edited collections between 60,000–90,000 words that include a range of philosophical, methodological and disciplinary approaches, which enrich and develop the field of social complexity and push it forward in new directions.

David Byrne is Emeritus Professor at the School of Applied Social Sciences, Durham University, UK.

Brian Castellani is Professor in Sociology and Head of the Complexity in Health and Infrastructure Group, Kent State University, USA. He is also Adjunct Professor of Psychiatry, Northeastern Ohio Medical University.

Emma Uprichard is Reader at the Centre for Interdisciplinary Methodologies, University of Warwick, UK. She is also director of the Nuffield, ESRC, HEFCE funded Warwick Q-Step Centre aimed at promoting quantitative methods across the social sciences.

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
SOC000000
SOCIAL SCIENCE / General
SOC026000
SOCIAL SCIENCE / Sociology / General