In this chapter we outline a theoretical perspective in which personality (relatively normal or dysfunctional) is the ultimate outcome (i.e. equilibrium state) of a mutualistic, dynamical system in which the building blocks of personality (i.e. components) interact with one another over time. These interactions give rise to dynamical couplings between thoughts, feelings, behaviours and environment. These couplings arise through multiple potential mechanisms, for example resource competition and a drive for consistency. As a result of particular architectures of the dynamical system, dysfunctional states can become stable features of the system, and we recognize these states as personality disorders. By means of a toy simulation dynamical model, we show some of the, potentially many, roads to developing personality disorders. Finally, we highlight four implications of our systems perspective on personality disorders on future research.

The comorbidity of personality disorders and mental disorders is commonly understood through three types of theoretical models: either (a) personality disorders precede mental disorders, (b) mental disorders precede personality disorders, or (c) mental disorders and personality disorders share common etiological grounds. Although these hypotheses differ with respect to their idea of causal direction, they all imply a latent variable perspective. In this chapter, we aim to provide another meta-theoretical and methodological perspective on this issue. We start this chapter by explicating a relationalist ontology of this type of comorbidity in which we understand mental states and personality traits as ontologically related systems. Using psychometric network models, we endeavor to bridge to the empirical and clinical world and provide an example of a network model of the relations between major depression disorder (MDD) and borderline personality disorder (BPD). The results identify direct associations between symptoms of MDD and BPD.

This introductory note provides an overview of the book’s original and timely framework with which to debunk Orientalism in how we read (Turkey’s) political history and present. The main argument is that political contestation is driven by shifting alliances for and against a more pluralistic society, not by forever polarized camps.

This chapter introduces an original and timely theoretical toolkit. The purpose: to challenge misleading readings of (Turkey’s) politics as driven by binary contests between “Islamists” vs. “secularists” or “Kurds vs. Turks.” Instead, it introduces an alternative “key”[1] to politics in and beyond Turkey that reads contestation as driven by shifting coalitions of pluralizers and anti-pluralists. This timely contribution to conversations in political science (e.g., comparative politics; political theory) is supplemented by an original analytical-descriptive framework inspired by complex systems thinking in the natural and management sciences. The approach offers a novel methodological framework for capturing causal complexity, in Turkey and other Muslim-majority settings, but also in any political system that is roiled by contending religious and secular nationalisms as well as actors who seek greater pluralism.

Structure matters for understanding behavior. This chapters introduces the main theme of the book, provides a number of stories about the importance of structure, and outlines the main structure of the book.

Links between the Arctic and the Earth’s climate system generate several paradoxes. Despite the low level of anthropogenic emissions of GHGs from the Arctic, it plays a critical role in the dynamics of the Earth’s climate system. The principal drivers of climate change are non-Arctic, but climate change impacts show up sooner and more dramatically in the Arctic, making it ground zero in efforts to address the challenge of climate adaptation. Ironically, these changes have also increased the accessibility of the massive reserves of hydrocarbons located in the circumpolar north. The Arctic Council has sought to address these concerns by monitoring the course of climate change in the Arctic and bringing together representatives of major GHG emitters to consider options for addressing climate change, but the council is limited in terms of authority and resources; Russia’s war in Ukraine has disrupted its activities. The paradoxical links arising in this case are characteristic of complex systems more generally and highlight the importance of developing the ability to respond agilely when needs and opportunities to deal effectively with rapidly changing conditions arise.

Chapter 13 discusses the concept of group behavior. We begin by describing group behavior in the context of a unit our readers can readily relate to, the family. We address the subject of culture and how it influences group behavior. We discuss the concept of systems thinking (viewing a particular situation not in isolation but in connection to and interrelated with other situations) beginning with general systems theory and moving into the realm of complex systems to better understand individual behavior in groups.

Simulating religion through computer modelling can demonstrate how fragmentary theories relate, untangle individual lines of causal influence, identify the relative importance of causal factors and enable experimentation that would never be possible (or ethical) in the real world. This chapter reviews the application of computational modelling and simulation to religion, presents findings from specific simulation studies and discusses some of the philosophical issues raised by this type of research. Social simulations are artificial complex systems that we can use to study real-world complex systems. The best of these simulation models are carefully validated in relation to real-world data. Multilevel validation justifies confidence that the causal architecture of the simulation reflects real-world causal processes, thereby delivering an invaluable proxy system into the hands of researchers who study religion.

In the preceding chapters we have covered the core principles and methods of epidemiology and have shown you some of the main areas where epidemiological evidence is crucial for policy and planning. You will also have gained a sense of the breadth and depth of the subject from the examples throughout the book. To finish, we take a broader look at the role of epidemiological practice and logic in improving health. There is a growing desire for public health and medical research to be ‘translational’; that is, directly applicable to a population or patient. The process, whereby research evidence is used to change practice or policy, is known as ‘translation’, and the research outputs from epidemiology are critical at all stages (see Box 16.2); indeed, epidemiology has been described as ‘the epicenter of translational science’ (Hiatt, 2010).

Network science has exploded in popularity since the late 1990s. But it flows from a long and rich tradition of mathematical and scientific understanding of complex systems. We can no longer imagine the world without evoking networks. And network data is at the heart of it. In this chapter, we set the stage by highlighting network sciences ancestry and the exciting scientific approaches that networks have enabled, followed by a tour of the basic concepts and properties of networks.