The increase in emerging infectious diseases has led to the allocation of significant time and resources for the development of pandemic preparedness plans worldwide. Nevertheless, real-time management of emerging disease outbreaks is often marked by confusion and uncertainty as decision-makers are challenged to make impactful decisions with little time and incomplete information. Health authorities typically approach such threats by individual level interventions, such as vaccines and antivirals. This does not, however, detail how these targeted interventions and countermeasures should be used to optimally benefit total population health. Mathematical modelling of complex systems represents the bridging science that is needed. This chapter discusses the conceptual design and structure of mathematical models of communicable diseases, using transmission dynamics in the context of respiratory-borne pathogens within human populations. It demonstrates the necessity of assembling appropriate expertise related to mathematical modelling, epidemiology, public health, virology, and clinical management to ensure valuable quantitative decision-support tools to assist policymakers at the time of crisis.
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Dr Babak Pourbohloul, Dr Krista M. English and Dr Nathaniel Hupert
Professor Yakov Shapiro and Associate Professor J. Rowan Scott
The synthesis of complexity and nonlinear science with evolutionary theory informs both functional neuroscience and psychotherapeutic exploration of conscious and unconscious processes. The nonlinear dynamical systems approach allows psychiatric practitioners to shift from categorical diagnostic and treatment algorithms to integrative process models of individual and group dynamics. Dynamical systems therapy (DST) represents a complexity derived application that conceptualizes individuals as Complex Adaptive Systems with emergent properties of subjective and cultural experience. It puts self-organization and flexible adaptation to changing environmental demands at the cornerstone of psychological health. Within the DST model, recurrent patterns of feeling, thinking and relating can be analysed by using modified fitness diagrams (adaptive A-landscapes), which integrate objective, subjective and intersubjective clinical data. This approach allows to chart the patient’s unique life trajectory through attractor/repellor configurations and reveals a paradigm shift from reductionism towards systemic psychobiology conceptualized as an integrative scientific perspective that incorporates emergent levels of psychobiological complexity.
Eve Mitleton-Kelly, Alexandros Paraskevas and Christopher Day
Professor Jeffrey A. Goldstein
Understanding emergence along the lines of self-organization has become so ubiquitous the two terms have just about become synonymous. However, the usual connotations of self-organization result in a misleading account of emergence by downplaying the radical novelty characterizing emergent phenomena. It is this radical novelty which generates the necessary explanatory gap between the antecedent, lower level properties of emergent substrates and the consequent, higher level properties of emergent phenomena. Without this explanatory gap, emergent phenomena are not unpredictable, are not non-deducible, are not irreducible, and thus are not truly emergent. For emergent phenomena to be genuinely emergent, processes of emergence must accomplish the seemingly paradoxical feat of producing an explanatory gap while simultaneously maintaining some degree of continuity with the substrate level.
To apply insights from complexity science to the real-world requires practitioners to judge which tools and methods are appropriate to use in some situations and when, where and how to apply them. This involves characterisation of complex situations; guidance on the type of adaptiveness needed; and understanding of the principles driving change. To that end, this chapter first provides a model of practice, and a framework for judging appropriateness of tools based on that model (with three examples of the framework in use). The chapter then offers a critique of two sets of example tools: examining the applicability of autonomous agents and multi-agent systems to a range of situations; and explaining how to employ multi-modal, multi-level influence networks to bring about ongoing change. Finally, the chapter presents a list of principles of practice, drawn from experience in the field, to be used to inform real-world decision- and policymaking.
Hazel Stuteley and Dr Jonathan Stead
It is an inescapable fact that for decades, costly interventions seeking to ‘fix’ Britain’s most disadvantaged communities, where poverty, crime, unemployment and poor health are rife, have largely failed. The ongoing human cost is heart-breaking and the cost to the public purse is immense. The layers of problems presented are undoubtedly complex and appear to be intractable. ‘C2’ (Connecting Communities), makes this very complexity the answer to this challenge. Using a practical seven-step application of complexity science to underpin a delivery framework C2 offers an intervention, delivered on site and in the classroom by a small team of experienced practitioners, working alongside local residents and service providers to enable them to implement the framework. The C2 team is drawn from a range of community leaders, academics and frontline primary healthcare professionals, who have consistently created the conditions for transformative outcomes in many communities across the UK in the last decade, using this approach.
Theory and Applications
Edited by Eve Mitleton-Kelly, Alexandros Paraskevas and Christopher Day
Fatimah Abdul Razak, Xiaogeng Wan and Professor Henrik Jeldtoft Jensen
It is fair to say that the raison d'être for science is to identify causations. This despite of the serious philosophical difficulties in defining causation and the scientific subtleties encountered when determining specific causal scenarios. Pragmatic information theoretic approaches were pioneered by Granger and have undergone much recent development in relation to studies of time series from complex systems such as the brain or finance. Here we first review the information theoretic and probabilistic approach to causality measures. We explain the difference between direct and indirect measures. Finally, we present an application of some of the measures to a real system. In particular, we discuss the findings on analysis of music performance where causation amongst the musicians and audience was studied by use of EEG time series.
Most social science research applying complexity principles concentrates on the complexity of the external world. There has been relatively little work on the inner complexity of human beings, and how this influences their behaviour in the world. This chapter introduces a visual methodology called Landscape of the Mind (referred to as LoM), which is designed to focus on these issues. Case studies and examples are given to illustrate its practical applications and show how it can be used to catalyse change in organisations, with particular reference to the implications for leadership and innovation.
Professor Yaneer Bar-Yam
The structure of scientific inquiry is being transformed by broad relevance of the strategies and methods of complex systems science for understanding physical, biological and social systems. Disciplinary and cross-disciplinary interactions are giving way to trans-disciplinary and unified efforts to address the relevance of large amounts of information to description, understanding and control of complex systems. From the study of biomolecular interactions to the workings of the mind to global socio-economic risks, pandemics and environmental disasters, complexity has arisen as a unifying feature of challenges to understanding and action. In this arena, information, structure, function and action are entangled. New approaches that recognize the importance of collective patterns of behaviour, the multi-scale space of possibilities, and evolutionary or adaptive processes that select systems or behaviours that can be effective are central to advancing our understanding and capabilities.