# Tipping Points: Modelling Social Problems and Health

###### John Bissell, et al.
Publisher:
John Wiley
Publication Date:
2015
Number of Pages:
232
Format:
Hardcover
Series:
Wiley Series in Computational and Quantitative Social Science
Price:
90.00
ISBN:
9781118752753
Category:
Monograph
[Reviewed by
Chris Thron
, on
09/16/2016
]

This book is not really about tipping points: the book’s subtitle, “Modelling Social Problems in Health”, should have been the title. Each chapter is based on a different paper originally presented at a conference which was funded as part of the so-called “Tipping Points Project” — so that the authors of each paper apparently had to find some way to jump on the “tipping point” bandwagon. Although tipping points do appear in various ways in the different chapters, they are in general not the major focus. Furthermore, the term “tipping point” is interpreted in vastly different ways by the different authors. For some, it takes the conventional mathematical meaning of a parameter value (or set of values) which marks a discontinuous changeover in the stable solutions of a dynamical system. For others, it denotes a threshold in a medical condition which, when attained, indicates that active intervention is required. For still others, it is used as a synonym for a rare, critical event, in general one that should be prevented.

Despite the inappropriate title and inconsistent terminology, I enjoyed reading the book. The chapters are in general well-written (especially the more mathematical ones) and thought-provoking. The mathematical content could be characterized as specific applications of conventional modelling techniques. Multicompartment models (systems of ODEs) feature prominently. The epidemiological smoking model (which figured in three of the twelve chapters) was clearly presented, and the model behavior was well-documented. It was also nice to see papers by researchers in non-mathematical disciplines treating the same topics from different perspectives. The conference must have been great fun for the participants.

Chapter 1 gives an introduction to an epidemiological smoking model which also appears in two subsequent chapters. The model is a conventional 3-compartment model with bilinear terms, and a straightforward stability analysis is given. A generalized version has multiple nontrivial equilibria, whose stability properties are characterized in terms of reproduction numbers.

Chapter 2 discusses sensitivity analysis of a stochastic version of the model presented in Chapter 1. Although the chapter doesn’t have much to do with tipping points, it does give a very easy-to-follow example of the use of Sobol indices to characterize the model’s sensitivity to parameters. Some minor quibbles: the values of Bij are not specified in Figure 2.2, and Figure 2.3(c) has a strange “bar code” pattern which is not discussed in the text, and is most likely a printing error.

Chapter 3 is non-mathematical, and explores possible reasons for the high incidence of smoking among women in northeast England. The variety and subtlety of factors involved should give pause to those who expect sociological modeling to develop into a kind of social physics, like the “psychohistory” imagined by Isaac Asimov in his “Foundation” science fiction series.

Part II (Chapters 4–6) has very little in common with Part I, either mathematically or in application. The applications are medical signal processing and blood flow, and the models are statistical fits and PDEs respectively.

I was frankly rather dismayed by Chapter 4, which talks about the assessment of cardiac surgeons’ performance based on patient survival statistics. Detailed statistical analyses are used to assess mean risk of death for various types of operations — but very simplistic and misleading methods are used to compare each physician’s performance against the means thus obtained. For each physician, (success minus failures) minus mean risk is plotted as a function of operation number — this will produce a mean-zero random walk for a physician whose performance matches the average. The authors do not acknowledge (and I suspect many administrators do not recognize) that random walks increasingly diverge from the mean over time, so that larger and larger deviations from the mean are no indication of significance. This would mean that many useless “interventions” would be conducted simply because of random variation. Furthermore, the interventions would be considered “successful” simply because of regression to the mean. One is reminded of W. Edward Deming’s “red bead” experiment.

Chapter 5 involves medical signal processing, and describes a Bayesian update-based algorithm for recognizing dangerous discrepancies in ECG patterns. Chapter 6 is essentially a short tutorial covering the derivation of various diffusion and fluid flow equations used in biomedical applications.

Part III (Chapters 7–10), like Part I, is concerned with modeling social phenomena. Indeed, there are many interrelations between these two sections; and it would have been helpful to place them together, rather than interposing Part II which has no relation to either.

Chapter 7 speculates on the design of a “system sociology approach”. It presents a modelling approach which may be productive in certain situations. But the stated ambition to develop a comprehensive mathematical theory of social systems (akin to the mathematical models of fundamental physics) is doomed to failure, for a number of reasons. A one-size-fits-all modeling methodology cannot possibly account for the enormous variety of social phenomena. Social systems do not always consist of fixed components interacting according to fixed rules, as physical systems do. Basic social structures may merge, fragment, or redistribute; rules of interaction may change dynamically. It follows that many social systems do not fit the authors’ paradigm of being divisible into “functional subsystems” consisting of “particles” (basic social units). Furthermore, even those systems which do fit this paradigm may not be Markov, or first order in time, or based on statistically independent particle interactions — all of which are presumed by the authors. Furthermore, the whole approach is based on the possibility of obtaining “a mathematical description of individual-based interactions according to a detailed interpretation of the dynamics at the microscopic scale”. But such “detailed interpretations” are rarely forthcoming. Unlike physical interactions, individual social interactions are hopelessly complicated. In general, it is more productive to treat microscopic interactions as a “black box”.

Although the article’s jumping-off point is Taleb’s concept of a “black swan” I suspect that Taleb himself, as a champion of heuristic methods, would strongly disapprove of the authors’ methodology. He would likely comment that they are following the same garden path as those economists who package economic behavior into neat analytical models, only to find out that events “out of the blue” knock their predictions cockeyed. It is surprising that the authors say nothing about agent-based models, which have shown some signs of being better equipped to account for “black swan” behavior than multicompartment models.

Chapter 8 (like Chapter 2) presents another sensitivity analysis via Sobol indices, this time for an agent-based model. This second application (to a very different type of model) illustrates the power and generality of this method. There is an error in the values of $\mu$ in the caption of Figure 8.4, but the values are given correctly in the text. We also find another strained interpretation of “tipping point”, this time as an elbow in a system behavior versus parameter curve.

Chapter 9 discusses the interplay between genetic and cultural evolution in humans, referred to as “coevolution”. Many practical examples of coevolution are mentioned and some multicompartment models are presented, but without any further analysis.

Chapter 10 discusses a multicompartment opinion dynamics model which bears some resemblance to the smoking model discussed previously, except that bilinear terms are replaced by nonlinear functions with sigmoidal dependence to account for the effects of “conformity bias”. The introduced nonlinearity is shown to give rise to critical parameter values at which the system’s steady-state behavior undergoes radical change.

Section IV (Chapters 11–12) centers around the ‘critical event’ incarnation of tipping point. The two articles in this section are non-mathematical, and describe (in rather general terms) risk management issues in different contexts. Chapter 11 discusses the management in suicide risk in prisons, and appends a rather vague discussion of risk management in the London Port Authority. The main point with respect to suicide risk appears to be that instead of trying to identify risky individuals, it’s better to focus on improving the prison environment. This is probably a controversial point in the field, and it’s hard to assess the validity without hearing the other side of the story. The chapter refers to “tipping points” as conditions “beyond which we are unable to get back to a steady or improved environmental state” — but offers no evidence that such “tipping points” exist, in the context of prison environments. Chapter 12 gives interesting anecdotes concerning risk management in a psychiatric inpatient facility, and then applied lessons learned to risk management in general.

Chris Thron is a former systems engineer at Motorola/Freescale, and currently associate professor of Mathematics at Texas A&M University-Central Texas. His research interests include various applications of modeling and simulation including mathematical epidemiology (malaria and plant disease), agent-based modeling of social systems, and network design and performance optimization. He is the author, with Justin Hill, of Elementary Abstract Algebra: Examples and Applications.

List of Contributors xi

Acknowledgements xiii

Introduction xv

PART I THE SMOKING EPIDEMIC 1

1 Generalised Compartmental Modelling of Health Epidemics 3

1.1 Introduction 3

1.2 Basic compartmental model of smoking dynamics 5

1.3 Properties of the basic model 8

1.4 Generalised model inclusive of multiple peer recruitment 10

1.5 Bistability and 'tipping points' in the generalised model 15

1.6 Summary and conclusions 18

2 Stochastic Modelling for Compartmental Systems Applied to Social Problems 21

2.1 Introduction 21

2.2 Global sensitivity analysis of deterministic models 23

2.3 Sensitivity analysis of the generalised smoking model with peer influence 24

2.4 Adding randomness to a deterministic model 26

2.5 Sensitivity analysis of the stochastic analogue 28

2.6 Conclusion 30

3 Women and Smoking in the North East of England 32

3.1 Introduction 33

3.2 Background 33

3.3 Interrogating the figures 35

3.4 Materialist and cultural or behavioural explanations 39

3.5 The tobacco industry and the creation of social values 41

3.6 Local voices 43

3.7 Conclusions 44

PART II MATHEMATICAL MODELLING IN HEALTHCARE 49

4 Cardiac Surgery Performance Monitoring 51

4.1 Introduction 52

4.2 Statistical framework for monitoring 55

4.3 A non-stationary process 61

4.4 Dynamic modelling approaches 68

4.5 Case example 74

4.6 Discussion 75

4.7 Conclusion 77

5 Heart Online Uncertainty and Stability Estimation 82

5.1 Introduction 83

5.2 Monitoring live complex systems 83

5.3 The Bayes linear approach 85

5.4 The Fantasia and Sudden Cardiac Death databases 86

5.5 Exploring ECG datasets 87

5.6 Assessing discrepancy 91

5.7 Final remarks and conclusion 93

6 Stents, Blood Flow and Pregnancy 95

6.1 Introduction 96

6.2 Drug-eluting stents 97

6.3 Modelling blood flow 101

6.4 Modelling a capillary-fill medical diagnostic tool 103

6.5 Summary and closing remarks 110

PART III TIPPING POINTS IN SOCIAL DYNAMICS 113

7 From Five Key Questions to a System Sociology Theory 115

7.1 Introduction 116

7.2 Complexity features 117

7.3 Mathematical tools 119

7.4 Black Swans from the interplay of different dynamics 122

7.5 Validation of models 125

7.6 Conclusions: towards a mathematical theory of social systems 126

8 Complexity in Spatial Dynamics: The Emergence of Homogeneity /Heterogeneity in Culture in Cities 130

8.1 Introduction 131

8.2 Modelling approach 132

8.3 Description of the model 134

8.4 Sensitivity analysis and results 138

8.5 Discussion and conclusions 141

9 Cultural Evolution, Gene–Culture Coevolution, and Human Health 146

9.1 Introduction 147

9.2 Cultural evolution 149

9.3 Epidemiological modelling of cultural change 153

9.4 Gene–culture coevolution 157

9.5 Conclusion 163

10 Conformity Bias and Catastrophic Social Change 168

10.1 Introduction 168

10.2 Three-population compartmental model 171

10.3 Basic system excluding conformity bias 173

10.4 Including conformity bias 174

10.5 Comparative statics 176

10.6 Summary 178

10.7 Conclusions 179

Appendix 10.A: Stability in the conformity bias model 180

PART IV THE RESILIENCE OF TIPPING POINTS 183

11 Psychological Perspectives on Risk and Resilience 185

11.1 Introduction 185

11.2 Forensic psychological risk assessments in prisons 186

11.3 Suicide in prisons 187

11.4 Biases in human decision making – forensic psychologists making risky decisions 189

11.5 The Port of London Authority 192

11.6 Final thoughts and reflections 194

12 Tipping Points and Uncertainty in Health and Healthcare Systems 196

12.1 Introduction: 'tipping points' as 'critical events' in health systems 197

12.2 Prediction, prevention and preparedness strategies for risk resilience in complex systems 198

12.3 No such thing as a 'never event'? 200

12.4 Local versus large-scale responses to risk 202

12.5 Conclusions: the ongoing agenda for research on tipping points in complex systems 204

Endnotes and acknowledgements 205

References 205

Index 209