Details

Tipping Points


Tipping Points

Modelling Social Problems and Health
Wiley Series in Computational and Quantitative Social Science 1. Aufl.

von: John Bissell, Camila Caiado, Sarah Curtis, Michael Goldstein, Brian Straughan

58,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 07.04.2015
ISBN/EAN: 9781118991992
Sprache: englisch
Anzahl Seiten: 232

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Beschreibungen

<p>This book focuses on the modelling of contemporary health and social problems, especially those considered a major burden to communities, governments and taxpayers, such as smoking, alcoholism, drug use, and heart disease. Based on a series of papers presented at a recent conference hosted by the Leverhulme-funded Tipping Points project at the University of Durham, this book illustrates a broad range of modelling approaches. Such a diverse collection demonstrates that an interdisciplinary approach is essential to modelling tipping points in health and social problems, and the assessment of associated risk and resilience.</p>
List of Contributors xi <p>Acknowledgements xiii</p> <p>Introduction xv</p> <p><b>PART I THE SMOKING EPIDEMIC 1</b></p> <p><b>1 Generalised Compartmental Modelling of Health Epidemics 3</b></p> <p>1.1 Introduction 3</p> <p>1.2 Basic compartmental model of smoking dynamics 5</p> <p>1.3 Properties of the basic model 8</p> <p>1.4 Generalised model inclusive of multiple peer recruitment 10</p> <p>1.5 Bistability and 'tipping points' in the generalised model 15</p> <p>1.6 Summary and conclusions 18</p> <p><b>2 Stochastic Modelling for Compartmental Systems Applied to Social Problems 21</b></p> <p>2.1 Introduction 21</p> <p>2.2 Global sensitivity analysis of deterministic models 23</p> <p>2.3 Sensitivity analysis of the generalised smoking model with peer influence 24</p> <p>2.4 Adding randomness to a deterministic model 26</p> <p>2.5 Sensitivity analysis of the stochastic analogue 28</p> <p>2.6 Conclusion 30</p> <p><b>3 Women and Smoking in the North East of England 32</b></p> <p>3.1 Introduction 33</p> <p>3.2 Background 33</p> <p>3.3 Interrogating the figures 35</p> <p>3.4 Materialist and cultural or behavioural explanations 39</p> <p>3.5 The tobacco industry and the creation of social values 41</p> <p>3.6 Local voices 43</p> <p>3.7 Conclusions 44</p> <p><b>PART II MATHEMATICAL MODELLING IN HEALTHCARE 49</b></p> <p><b>4 Cardiac Surgery Performance Monitoring 51</b></p> <p>4.1 Introduction 52</p> <p>4.2 Statistical framework for monitoring 55</p> <p>4.3 A non-stationary process 61</p> <p>4.4 Dynamic modelling approaches 68</p> <p>4.5 Case example 74</p> <p>4.6 Discussion 75</p> <p>4.7 Conclusion 77</p> <p><b>5 Heart Online Uncertainty and Stability Estimation 82</b></p> <p>5.1 Introduction 83</p> <p>5.2 Monitoring live complex systems 83</p> <p>5.3 The Bayes linear approach 85</p> <p>5.4 The Fantasia and Sudden Cardiac Death databases 86</p> <p>5.5 Exploring ECG datasets 87</p> <p>5.6 Assessing discrepancy 91</p> <p>5.7 Final remarks and conclusion 93</p> <p><b>6 Stents, Blood Flow and Pregnancy 95</b></p> <p>6.1 Introduction 96</p> <p>6.2 Drug-eluting stents 97</p> <p>6.3 Modelling blood flow 101</p> <p>6.4 Modelling a capillary-fill medical diagnostic tool 103</p> <p>6.5 Summary and closing remarks 110</p> <p><b>PART III TIPPING POINTS IN SOCIAL DYNAMICS 113</b></p> <p><b>7 From Five Key Questions to a System Sociology Theory 115</b></p> <p>7.1 Introduction 116</p> <p>7.2 Complexity features 117</p> <p>7.3 Mathematical tools 119</p> <p>7.4 Black Swans from the interplay of different dynamics 122</p> <p>7.5 Validation of models 125</p> <p>7.6 Conclusions: towards a mathematical theory of social systems 126</p> <p><b>8 Complexity in Spatial Dynamics: The Emergence of Homogeneity /Heterogeneity in Culture in Cities 130</b></p> <p>8.1 Introduction 131</p> <p>8.2 Modelling approach 132</p> <p>8.3 Description of the model 134</p> <p>8.4 Sensitivity analysis and results 138</p> <p>8.5 Discussion and conclusions 141</p> <p><b>9 Cultural Evolution, Gene–Culture Coevolution, and Human Health 146</b></p> <p>9.1 Introduction 147</p> <p>9.2 Cultural evolution 149</p> <p>9.3 Epidemiological modelling of cultural change 153</p> <p>9.4 Gene–culture coevolution 157</p> <p>9.5 Conclusion 163</p> <p><b>10 Conformity Bias and Catastrophic Social Change 168</b></p> <p>10.1 Introduction 168</p> <p>10.2 Three-population compartmental model 171</p> <p>10.3 Basic system excluding conformity bias 173</p> <p>10.4 Including conformity bias 174</p> <p>10.5 Comparative statics 176</p> <p>10.6 Summary 178</p> <p>10.7 Conclusions 179</p> <p>Appendix 10.A: Stability in the conformity bias model 180</p> <p><b>PART IV THE RESILIENCE OF TIPPING POINTS 183</b></p> <p><b>11 Psychological Perspectives on Risk and Resilience 185</b></p> <p>11.1 Introduction 185</p> <p>11.2 Forensic psychological risk assessments in prisons 186</p> <p>11.3 Suicide in prisons 187</p> <p>11.4 Biases in human decision making – forensic psychologists making risky decisions 189</p> <p>11.5 The Port of London Authority 192</p> <p>11.6 Final thoughts and reflections 194</p> <p><b>12 Tipping Points and Uncertainty in Health and Healthcare Systems 196</b></p> <p>12.1 Introduction: 'tipping points' as 'critical events' in health systems 197</p> <p>12.2 Prediction, prevention and preparedness strategies for risk resilience in complex systems 198</p> <p>12.3 No such thing as a 'never event'? 200</p> <p>12.4 Local versus large-scale responses to risk 202</p> <p>12.5 Conclusions: the ongoing agenda for research on tipping points in complex systems 204</p> <p>Endnotes and acknowledgements 205</p> <p>References 205</p> <p>Index 209</p>
<p><b>J. J. Bissell</b>, Department of Mathematical Sciences, University of Durham, UK.</p> <p><b>C. C. S. Caiado</b>, Department of Mathematical Sciences, University of Durham, UK.</p> <p><b>S. E. Curtis</b>, Institute of Hazard, Risk and Resilience, University of Durham, UK.</p> <p><b>M. Goldstein</b>, Department of Mathematical Sciences, University of Durham, UK.</p> <p><b>Brian Straughan</b>, Department of Mathematical Sciences, University of Durham, UK.</p>

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