The socio-economic scenario of a country reflects its social, economic, political, ideological, ethical, cultural, or communicative habits, making its proper analysis for different countries quite challenging. Complexity science has provided some new methods and tools for dealing with this challenge. Country-level Gross Domestic Product (GDP) and population are the two most important issues in the socio-economic context. In order to show the effectiveness of different nonlinear tools in analysing socio-economic data, the authors implemented three popular nonlinear tools: recurrence rate, mean conditional recurrence (MCR) and complex networks (CN) to analyse country level GDP and population data to validate the derived results with the standard conclusions based on general theories of economics. recurrence rate is used to show how two non-identical systems get synchronized through their phase spaces. MCR detects the driver and response system in synchronized states and CN reflects the overall scenarios of the complex systems by its various statistical measures.
Assistant Professor Sanjay Kumar Palit, Associate Professor Santo Banerjee and Assisant Professor Sayan Mukherjee
Dr Lesley Kuhn
This chapter describes and demonstrates a complexity informed qualitative social research approach, associated methods and techniques. To theorize human experience and sense making from findings and ideas of complexity studies in nature, translation and interpretation is required. The chapter explains these translations and interpretations based on the assumption that qualitative research, like complexity, has to take a radically relational approach to interpreting interrelationships between sense makers, fragments of knowledge, cultures, histories, futures and aspirations. It sets out how complexity offers a paradigmatic orientation to assist qualitative researchers in discerning nonlinearly-based pattern and order. Vortical postmodern ethnography as an inquiry approach is described along with the narrative generating method of coherent conversations and the techniques of fractal narrative analysis and attractor narrative analysis. Vortical postmodern ethnography effectively re-conceptualizes a number of problematic issues in ethnographic inquiry. The chapter concludes with a demonstration of this inquiry approach and techniques in a research project.
Professor Henrik Jeldtoft Jensen
Dr Babak Pourbohloul, Dr Krista M. English and Dr Nathaniel Hupert
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.
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.