This chapter presents some basic calculations that show counterintuitive or unexpected results. First, it is shown that the Planck spectrum of light, which played an important role in the history of quantum mechanics, doesn’t say anything about the existence of indivisible particles. Second, a brief discussion of “chaos theory” shows that jumpy and unpredictable behavior can occur in classical systems. Last, the concept of “entanglement” is introduced as a basic property of quantum systems.

We live an unpredictable unrepeatable yet creative existence. Revealing the underlying patterns of our thoughts and behavior, nonlinear dynamics provides models to understand our lives and our creativity as ever-evolving human beings in a constantly changing universe. As the writer James Baldwin said, “the artist must know, and he must let us know, that there is nothing stable under heaven.” The creative trance, modeled as a creative chaos, brings forth new work and personal transformation. With its disciplines of chaos theory and complexity theory, nonlinear dynamics explains processes like creativity that do not progress in a straight line, cannot be predicted, never exactly repeat themselves, and yet have the capacity for originality and transformation. There are aspects of nonlinear dynamics such attractors, fractals, self-organization, emergence, the butterfly effect, and self-organized criticality that strongly correlate with the creative process, insights, the power of memories, global transformation, catharsis, and transforming panic attacks into creativity.

From questions posed by Wilfred Owen at a Craiglockhart War Hospital talk ‘Do Plants Think?’ and Alan Turing’s rhetorical echo ‘Can Machines Think?’ to Dipesh Chakrabarty’s query ‘Who is the we?’ in a postcolonial Anthropocene, times of crisis goad us into recognising wider sentience and reimagining collective agency. This essay considers how a collective comprised of humans, intelligent botanical or zoological life, and AI might respond to climate crisis using two literary case studies. Richard Powers’ The Overstory and Vandana Singh’s ‘Entanglement’ use contemporary tree science and quantum entanglement, respectively, as innovative, interdisciplinary narrative models. These models also dictate the resolutions of their stories – eventualities of collectivity that may still be evolving, but gesture toward potential climate-changed futures. As this essay argues, these two works offer contrasting visions based on how they deploy AI design potentials, the emotional responses of hope and despair, and the spectre of uncertainty as either a creative space for solutions or a reminder of impossible choices. These stories also pose important questions about corporate power, empathy, and social justice, reminding us that reckoning with human cultural diversity is still the soil from which any more-than-human climate collective must grow.

MIT professor Edward Lorenz made a serendipitous discovery about weather in the early 1960s, using a digital computer. He found that a small error in the initial conditions for a weather forecast could grow exponentially into a large error within days. This came to be known as the Butterfly Effect, one of the founding principles of chaos theory. To obtain initial conditions for weather forecasts, data from hundreds of weather balloons launched daily around the world are combined with satellite data through a procedure called data assimilation. The Butterfly Effect limits the useful period of weather prediction to about two weeks in advance. Carrying out an ensemble of weather forecasts, starting from different initial conditions, allows probabilistic predictions that extend beyond this limit. The philosophical concept of determinism is introduced, as is the concept of Laplace’s Demon, motivating the notion of a Climate Demon that predicts climate perfectly.

This paper advocates the theory of 'Complex Dynamic Systems', developed in the sciences as a suitable framework for the understanding of the evolution of varieties and uses of English through time and space. After looking into earlier applications of this theory in linguistics, it surveys core properties of such systems and illustrates their relevance by applications to specific processes of change in the history of English. It then investigates processes of lexicosemantic diffusion and syntactic restructuring in World Englishes within this framework, trying to document its applicability.

On-again/off-again relationships challenge the standard dichotomous definition of relationship stability (i.e., whether the relationship remains intact or dissolves). This chapter reviews the various conceptualizations of stability. Although on-off partners report less relationship stability when using subjective, one-time assessments (e.g., perceived stability, sense of security, or persistence in the relationship), a process-oriented assessment of stability is advocated in which relationship dynamics are measured over time. As argued by chaos theory, fluctuations over time could indicate a stable pattern. For example, research suggests certain fluctuations are associated with greater stability in on-off relationships. A process-oriented perspective could thus provide a more nuanced assessment of relationship stability for both on-off and non-cyclical relationships. Additional considerations for future research are also offered.

Recently, there has been increased focus on sub-threshold stages of mental disorders, with attempts to model and predict progression to full-threshold disorder. Given this considerable research attention and clinical significance, it is timely to analyse the assumptions of theoretical models in the field. Research into predicting onset of mental disorder has shown an overreliance on one-off sampling of cross-sectional data (i.e., a "snapshot" of clinical state and other risk markers) and may benefit from taking dynamic changes into account. Cross-disciplinary approaches to complex system structures and changes, such as dynamical systems theory, network theory, instability mechanisms, chaos theory and catastrophe theory, offer potent models that can be applied to emergence (or decline) of psychopathology, including psychosis prediction and transdiagnostic symptom emergence. Staging provides a useful framework to research dynamic prediction in psychiatry. Psychiatric research may benefit from approaching psychopathology as a system rather than a category, identifying dynamics of system change (e.g., abrupt/gradual psychosis onset), factors to which these systems are most sensitive (e.g., interpersonal dynamics, neurochemical change), and individual variability in system architecture and change. The next generation of prediction studies may more accurately model the highly dynamic nature of psychopathology and system change, with treatment implications, such as introducing a means of identifying critical risk periods for mental state deterioration.

A fundamental aim of diagnosis is to guide treatment planning and predict illness course. Yet for too long psychiatric diagnosis, grounded on traditional silo-based approaches, has lacked clinical utility. This chapter explores the purpose of diagnosis and classification as well as the inability to validate diagnosis in psychiatry. It is proposed that new testable models are needed to improve the utility of diagnosis and support more personalised and sequential treatment selection. A number of new approaches have been put forward, including hierarchical and network-based methods, however at present, these offer limited value in guiding treatment selection. Clinical staging offers a viable solution. Clinical staging in psychiatry recognises that mental disorders are not static and discretely defined entities, but rather they are syndromes that overlap and develop in stages. The model ensures that interventions are proportional to both need and the risk of progressing to later stages and more established syndromes, which are likely to be comorbid, persistent, recurrent and disabling. Ultimately, it advocates a transdiagnostic approach to intervention, with a pre-emptive focus, that is based on risk-benefit considerations and patient needs. Clinical staging also provides a framework in which underlying biological mechanisms can be linked to each stage, to build a personalised and pre-emptive psychiatry.