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This chapter argues that evolutionary economics should be founded upon complex systems theory rather than neo-Darwinian analogies concerning natural selection, which focus on supply side considerations and competition amongst firms and technologies. It suggests that conceptions such as production and consumption functions should be replaced by network representations, in which the preferences or, more correctly, the aspirations of consumers are fundamental and, as such, the primary drivers of economic growth. Technological innovation is viewed as a process that is intermediate between these aspirational networks, and the organizational networks in which goods and services are produced. Consumer knowledge becomes at least as important as producer knowledge in determining how economic value is generated. It becomes clear that the stability afforded by connective systems of rules is essential for economic flexibility to exist, but that too many rules result in inert and structurally unstable states. In contrast, too few rules result in a more stable state, but at a low level of ordered complexity. Economic evolution from this perspective is explored using random and scale free network representations of complex systems.

This chapter focuses on the key difference between the two paradigms of crisis management described in Chapters 2 and 3. The traditional crisis planning process described in Chapter 2 depends on the ability of managers to predict and control multiple features of crisis situations: how organizations will behave during a crisis, how publics will respond to efforts to contain or repair the crisis, and how the company's actions will affect subsequent outcomes. The complexity view described in Chapter 3 substantially limits actors' ability to either predict or control all the interacting elements that make up a complex system.

This chapter begins by outlining the various challenges faced by the massive modularity hypothesis, most of which turn on one or another form of cognitive flexibility. It outlines the role of mental rehearsal and inner speech in explaining stimulus independence, and it explores how language may play a role in integrating contents deriving from modular systems that would not otherwise communicate with one another. It outlines a two-systems theory of human reasoning processes, and explains how cycles of inner speech might serve to realize the operations of System 2. It also contrasts the views being defended with a variety of other proposals concerning the role of language in human cognition.

The chapter provides a detailed description and critical discussion of Habermas' attempts to make the theory of communicative action and discourse ethics fruitful beyond the narrower moral and ethical realm and, conversely, to find confirmation for it in empirical theories. Habermas refers to the research areas of psychology and social evolution and to political and sociological issues around which the “Critical Theory of Society” is to take concrete form. Concerning psychology, he develops or adopts theories of ego-development, moral development and “communication pathologies”. As it concerns social evolution he proposes theories of hominisation and socio-cultural evolution. In dealing with the political and sociological issues he is most productive, offering a theory of social order, his famous colonialisation thesis, his discourse theory of law and the constitutional state, and his theory of modernity.

This chapter provides a brief introduction to concepts from systems biology; tools from differential equations and control theory; and approaches to the modeling, analysis, and design of biomolecular feedback systems. It begins with a discussion of the role of modeling, analysis, and feedback in biological systems. This is followed by a short review of key concepts and tools from control and dynamical systems theory, which is intended to provide insight into the main methodology described in this volume. Finally, this chapter gives another brief introduction—this time to the field of synthetic biology, which is the primary topic of the latter portion of this book.

This chapter seeks to articulate and clarify cases of perceived differences between dynamical systems theory (DST) and the connectionist (CN) approaches that are not real, as well as cases of perceived differences that are real. It discusses the implications of efforts to integrate the two approaches for developmental science more generally. Clarifying similarities/differences between approaches offers far more that just technical clarity for co-called modeler types; it offers a vision of a new, integrative, developmental theory.

This chapter addresses the question of whether dynamic systems and connectionist approaches are an alternative to good old-fashioned cognitive development (GOFCD). The chapter is organized as follows. The first section discusses what connectionism and dynamic systems bring to the study of cognitive development. The second section examines how connectionist and dynamic systems theories relate to other GOFCD theories of developmental change. The third section evaluates the contribution of connectionism and dynamic systems in more depth by examining explanations of two historically important issues in cognitive development: infants' behavior in the A-not-B task and children's solutions to the balance scale problem. Finally, the chapter considers how well connectionist and dynamic systems approaches address criticisms often leveled at other theories of cognitive development.

This chapter reviews theory and research on System Justification Theory (SJT) and summarizes key implications for law, lawyers, and social justice advocacy. According to SJT, lawyers should attend to all relevant social orders and implicit as well as explicit biases in selecting jurors and developing advocacy strategies. The theory identifies important obstacles to social change, including changes in the law and legal scholarship. This chapter highlights some of the ways in which system justification motives result in behaviors that are unanticipated by current models of legal thinking. It discusses the persuasive power of “reframing,” whereby advocates can deploy narrative to exacerbate or diminish the system-justifying motives of legal and public policy decision-makers.

Dynamical systems thinking can provide metaphors that help ask new questions, generate new experimental paradigms and measures, and lead to new kinds of explanations. This chapter reviews dynamical systems theory (DST) as a set of concepts that formalizes such metaphors and thus becomes a scientific theory of considerable rigor. It considers five concepts: (1) Behavioral patterns resist change; that is, they are stable. This may be mathematically characterized by considering behavioral patterns as the attractor states of a dynamical system. (2) Behavioral change is brought about by a loss of stability. (3) Representations possess stability properties, as well, and can be understood as the attractor states of dynamic fields, that is, of continuous distributions of neuronal activation. (4) Cognitive processes emerge from instabilities of dynamic fields. (5) Learning occurs as changes in behavioral or field dynamics that shift the behavioral and environmental context in which these instabilities occur.

This chapter draws attention to some aspects of development that are considered underrepresented in both dynamic systems theory (DST) and connectionism. The approach taken is that of developmental systems theory, which shares with DST a focus on the whole organism in its environment, and with connectionism a focus on the biological support for psychological change. It adds to those perspectives, a concern with more levels of biological analysis (including genetics), the incorporation of comparative, animal-based research, and attention to evolutionary considerations in the analysis of psychological change.

This chapter discusses the question of how dynamic systems theory can be fruitfully applied to the development of the kind of phenomena and variables that have been of interest for a long time. Examples of these phenomena are (a) the development of language, including the development of the lexicon and syntactic and grammatical knowledge and skill; (b) the development of cognition and thinking, including the emergence and acquisition of cognitive skills and knowledge in various domains; (c) the development of reflective judgment, including metacognition and social understanding; and (d) the development of social skills and behavior. Behind all these phenomena are the development of context-specific but overarching principles of skill formation, such as principles of relationships, systems of relationships, and so on.

This chapter introduces the reader to some of the main conceptual ideas behind dynamical systems theory from the perspective of an experimentalist. It first considers the qualitative approaches used to study complex problems before discussing dynamical systems and bifurcations. In particular, it examines the use of time series to represent solutions and dynamics in the phase space, phase space respresentations of equilibrium and nonequilibrium steady states, the qualitative analysis of steady states, and some of the mechanics of local stability analysis for an equilibrium using the Lotka–Volterra model for an equilibrium steady state. It also explores the relationship between the type of model dynamics and the geometry of the underlying mathematical functions. Finally, it presents an empirical example from ecology, Hopf bifurcation in an aquatic microcosm, to illustrate the main concepts of dynamical systems theory and shows that the mathematics of dynamical systems underlies the dynamics of real ecological systems.

This chapter examines the interplay of dynamical systems and connectionism at the level of both theory and computation. It begins with a discussion of developmental mechanism focusing on two particular processes—one typically associated with connectionist approaches and one typically associated with dynamical systems. It then illustrates potential hybrid approaches with case studies from the field of language acquisition. The first—a model of speech category learning—illustrates how a dynamical systems perspective may inform a classic connectionist mechanism (i.e., statistical learning). The second—a model of early word learning—combines connectionist and dynamical systems principles. Both suggest that we can no longer treat these two paradigms as independent.

Traces the dispersal of power associated with contemporary transformations of the state and discusses the tensions that arise in the shift towards partnership working, public participation, citizen engagement, and the 'responsibilisation' of citizens in modernized social health and welfare systems. Governance theory, it argues, has much to contribute to analyses of the Third Way in that it shifts the focus of attention beyond economic structures or processes towards a broader concern with issues of citizenship, concepts of community, and flows of power beyond the state. However, the chapter critiques a number of assumptions on which it is based and highlights its capacity to de-politicize analyses of the state and to legitimize the Third Way as an inevitable consequence of social and economic change.

This chapter lays the theoretical foundations of the analyses in this book. The book addresses two major themes. First, language does not stand alone as the crowning achievement of infancy. Rather, other equally complex achievements are ongoing and contribute to linguistic development. Second, a set of precursor achievements organized from a dynamic systems perspective can successfully predict, for individual children, the onset of referential word use. The use of dynamic systems theory to study how infants acquire language is discussed. An overview of the succeeding chapters is presented.