Although its roots can be traced to the 19th century, progress in the study of nonlinear dynamical systems has taken off in the last 30 years. While pertinent source material exists, it is strewn about the literature in mathematics, physics, biology, economics, and psychology at varying levels of accessibility. A compendium research methods reflect

The goal of the chapters in this SIOP Organizational Frontiers Series volume is to challenge researchers to break away from the rote application of traditional methodologies and to capitalize upon the wealth of data collection and analytic strategies available to them. In that spirit, many of the chapters in this book deal with methodologies that encourage organizational scientists to re-conceptualize phenomena of interest (e.g., experience sampling, catastrophe modeling), employ novel data collection strategies (e.g., data mining, Petri nets), and/or apply sophisticated analytic techniques (e.g., latent class analysis). The editors believe that these chapters provide compelling solutions for the complex problems faced by organizational researchers.

This book capitalizes on the developments in dynamical systems and education by presenting some of the most recent advances in this area in seventeen non-overlapping chapters. The first half of the book discusses the conceptual framework of complex dynamical systems and its applicability to educational processes. The second half presents a set of empirical studies that that illustrate the use of various research methodologies to investigate complex dynamical processes in education, and help the reader appreciate what we learn about dynamical processes in education from using these approaches.

The pandemic, and our response to it, has shown how unpredictable, irrational, illogical, suddenly changing, and muddled human interactions can be in a time of crisis. How can we make sense of such confusing and baffling behavior? This book reveals how chaos and nonlinear dynamics can bring new understanding to everyday topics in social sciences. It brings together chapters from leaders at the intersection of psychology and chaos and complexity theories. Conceptual and user-friendly, it is built around six themes: 1) Seeing nonlinearity, 2) Finding patterns, 3) using Simple models, 4) Intervening nonlinearly, and 6) teaching a new Worldview. It takes no specialized study-although there is more sophisticated material and optional math for those wishing it. The techie will, in addition, find concepts and diagrams to ponder. The volume is engaging, at times startling-whether about the weather, Internet, organizations, family dynamics, health, evolution, or falling in love. It reveals how many social, personal, clinical, research, and life phenomena become understandable and can be modelled in the light of Nonlinear Dynamical Systems (NDS) theory. It even offers a broadening worldview, happening already in other sciences, toward a more dynamic, interconnected, and evolving picture, including process-oriented appreciation of one's own experience. The book offers those in the field of psychology and the social sciences a stunning new perspective on human behaviour.

Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians — with limited knowledge of nonlinear dynamics — to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic (please see www.marco.bittelli.com). The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework — condensed from sound empirical practices recommended in the literature — that details a step-by-step procedure for applying NLTS in real-world data diagnostics.

The systems in which we work continue to evolve, creating emergent problems and often strengthening intractable issues. In order to remain relevant and impactful, the discipline of ergonomics needs its paradigms to evolve too. The aim of this book is to provide researchers and practitioners with new paradigms in the form of ideas, concepts, theories, methods, practices and values. The chapters take the reader on a journey through underlying theories, new ways to apply those theories and emerging domains in which ergonomics is expected to play a greater role. Readers of this book will be inspired by these new paradigms in ergonomics and seek to push the boundaries even further. The lifeblood of the science depends on continual evolvement and developments to take on the challenges we face in complex sociotechnical systems design and evaluation. Perhaps the most significant take-home message from this book is the demonstration of how theory maps onto practice. As such, the only remaining paradigm shift is for these ideas, concepts, methods and practices to be taken up more widely and the discipline advanced, until the next paradigm shift occurs. The chapters were originally published as a special issue in the journal Ergonomics.

This book presents a survey of the aspects of economic complexity, with a focus on foundational, interdisciplinary ideas. The long-awaited follow up to his 2011 volume Complex Evolutionary Dynamics in Urban-Regional and Ecologic-Economic Systems: From Catastrophe to Chaos and Beyond, this volume draws together the threads of Rosser’s earlier work on complexity theory and its wide applications in economics and an expanded list of related disciplines. The book begins with a full account of the broader categories of complexity in economics--dynamic, computational, hierarchical, and structural--before shifting to more detailed analysis. The next two chapters address problems associated with computational complexity, especially those of computability, and discuss the Godel Incompleteness Theorem with a focus on reflexivity. The middle chapters discuss the relationship between entropy, econophysics, evolution, and economic complexity, respectively, with applications in urban and regional dynamics, ecological economics, general equilibrium theory, as well as financial market dynamics. The final chapter works to bring together these themes into a broader framework and expose some of the limits concerning analysis of deeper foundational issues. With applications in all disciplines characterized by interconnected nonlinear adaptive systems, this book is appropriate for graduate students, professors and practitioners in economics and related disciplines such as regional science, mathematics, physics, biology, environmental sciences, philosophy, and psychology.