Philip Haynes

Social Synthesis

Finding Dynamic Patterns in Complex Social Systems

• Broschiertes Buch

Produktbeschreibung

Social Synthesis argues the importance of an applied social science that appreciates social systems as manifestations of complex systems which are highly dynamic, interactive and emergent.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 204
• Erscheinungstermin: 17. Juli 2019
• Englisch
• Abmessung: 231mm x 152mm x 13mm
• Gewicht: 295g
• ISBN-13: 9780367371241
• ISBN-10: 0367371243
• Artikelnr.: 57146075

Autorenporträt

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

Inhaltsangabe

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