There seem to be as many definitions of constructivism as there are minds to construct them. At least this is how it felt after reading the chapters by Duit (chap. 14), Saxe (chap. 15), and Spivey (chap. 16). Or perhaps there are as many questionings of constructivist ideas as practices in the field of education. For example, a teacher experiences a different set of constraints than a researcher or a designer, and these constraints in turn shape theories of learning in different ways.

The concept of agency is of crucial importance in cognitive science and artificial intelligence, and it is often used as an intuitive and rather uncontroversial term, in contrast to more abstract and theoretically heavily weighted terms such as intentionality, rationality, or mind. However, most of the available definitions of agency are too loose or unspecific to allow for a progressive scientific research program. They implicitly and unproblematically assume the features that characterize agents, thus obscuring the full potential and challenge of modeling agency. We identify three conditions that a system must meet in order to be considered as a genuine agent: (a) a system must define its own individuality, (b) it must be the active source of activity in its environment (interactional asymmetry), and (c) it must regulate this activity in relation to certain norms (normativity). We find that even minimal forms of proto-cellular systems can already provide a paradigmatic example of genuine agency. By abstracting away some specific details of minimal models of living agency we define the kind of organization that is capable of meeting the required conditions for agency (which is not restricted to living organisms). On this basis, we define agency as an autonomous organization that adaptively regulates its coupling with its environment and contributes to sustaining itself as a consequence. We find that spatiality and temporality are the two fundamental domains in which agency spans at different scales. We conclude by giving an outlook for the road that lies ahead in the pursuit of understanding, modeling, and synthesizing agents.

Key words: agency, individuality, interactional asymmetry, normativity, spatiality, temporality

The concept of agency is of crucial importance in cognitive science and artificial intelligence, and it is often used as an intuitive and rather uncontroversial term, in contrast to more abstract and theoretically heavy-weighted terms like “intentionality”, “rationality” or “mind”. However, most of the available definitions of agency are either too loose or unspecific to allow for a progressive scientific program. They implicitly and unproblematically assume the features that characterize agents, thus obscuring the full potential and challenge of modeling agency. We identify three conditions that a system must meet in order to be considered as a genuine agent: a) a system must define its own individuality, b) it must be the active source of activity in its environment (interactional asymmetry) and c) it must regulate this activity in relation to certain norms (normativity). We find that even minimal forms of proto-cellular systems can already provide a paradigmatic example of genuine agency. By abstracting away some specific details of minimal models of living agency we define the kind of organization that is capable to meet the required conditions for agency (which is not restricted to living organisms). On this basis, we define agency as an autonomous organization that adaptively regulates its coupling with its environment and contributes to sustaining itself as a consequence. We find that spatiality and temporality are the two fundamental domains in which agency spans at different scales. We conclude by giving an outlook to the road that lies ahead in the pursuit to understand, model and synthesize agents.

This paper traces the development of enactive concepts of value and normativity from their roots in the canonical work of Varela et al. (Embodied mind: cognitive science and human experience. MIT Press, Cambridge, 1991) through more recent works of Ezequiel Di Paolo and others. It aims to show the central importance of these concepts for enactive theory while exposing a potentially troublesome ambiguity in their definition. Most definitions of enactive normativity are purely proscriptive, but it seems that enactive theories of cognitive agency and experience demand something more. On the other hand, it is not clear that anything other than proscriptive normativity can be made compatible with the enactive tenet of autonomy and the rejection of representations.

Key words: Enactive theory, Value, Normativity, Adaptivity, Cognition

An address delivered at the University of Valladolid, Spain. Asks the Question-What is Cybernetics?. Discusses popular notions and genuine difficulties. Looks at the origins, derivations and definitions of cybernetics. Considers intrinsic control and Socio-Economic Governance in realtime. Relates cybernetics to the current world situation.

Key words: cybernetics, systems analysis, management science

The merits of Empirical Modelling (EM) principles and tools as a constructivist approach to computer science education are illustrated with reference to ways in which they have been used in teaching topics related to the standard computer science curriculum. The products of EM are interactive models – construals – that serve a sense-making role. Model-building proceeds in an incremental fashion through the construction of networks of definitions that reflect the observables, dependencies and agents associated with a current situation. The three principal case studies discussed (teaching bubblesort, solving Sudoku puzzles, and recognising groups from their abstract multiplication tables) highlight respects in which EM accounts for aspects of computing that cannot be effectively addressed by thinking primarily in terms of abstractions, procedures and mechanisms. The discussion of EM as a constructivist approach to computer science education is set in the context of an analysis of constructivism in computer science published by Ben-Ari in 2001. Reconciling EM’s constructivist epistemology with this analysis involves recognising its pretensions to a broader view of computer science.

Life is defined by Maturana and Varela as a type of self-organization: autopoiesis in the physical space. This resembles the concept of metabolism, which itself is typically included in definitions of life. Three senses of metabolism are distinguished. If life depends on either autopoiesis or metabolism (in the third sense), then strong A-Life is impossible. The theory of autopoiesis challenges concepts familiar in biology and cognitive science. While its use of informational language is too restrictive, its use of cognitive language is too liberal: life does not imply cognition.

Key words: life, self-organization, autopoiesis, metabolism, strong ALife

This article revisits the concept of autopoiesis and examines its relation to cognition and life. We present a mathematical model of a 3D tesselation automaton, considered as a minimal example of autopoiesis. This leads us to a thesis T1: “An autopoietic system can be described as a random dynamical system, which is defined only within its organized autopoietic domain.” We propose a modified definition of autopoiesis: “An autopoietic system is a network of processes that produces the components that reproduce the network, and that also regulates the boundary conditions necessary for its ongoing existence as a network.” We also propose a definition of cognition: “A system is cognitive if and only if sensory inputs serve to trigger actions in a specific way, so as to satisfy a viability constraint.” It follows from these definitions that the concepts of autopoiesis and cognition, although deeply related in their connection with the regulation of the boundary conditions of the system, are not immediately identical: a system can be autopoietic without being cognitive, and cognitive without being autopoietic. Finally, we propose a thesis T2: “A system that is both autopoietic and cognitive is a living system.”

Key words: Autopoiesis, cognition, life, tessela- tion automaton, random dynamical system, boundary conditions

According to the sensorimotor approach, perception is a form of embodied know-how, constituted by lawful regularities in the sensorimotor flow or in sensorimotor contingencies (SMCs) in an active and situated agent. Despite the attention that this approach has attracted, there have been few attempts to define its core concepts formally. In this paper, we examine the idea of SMCs and argue that its use involves notions that need to be distinguished. We introduce four distinct kinds of SMCs, which we define operationally. These are the notions of sensorimotor environment (open-loop motor-induced sensory variations), sensorimotor habitat (closed-loop sensorimotor trajectories), sensorimotor coordination (reliable sensorimotor patterns playing a functional role), and sensorimotor strategy (normative organization of sensorimotor coordinations). We make use of a minimal dynamical model of visually guided categorization to test the explanatory value of the different kinds of SMCs. Finally, we discuss the impact of our definitions on the conceptual development and empirical as well as model-based testing of the claims of the sensorimotor approach.

Key words: embodied cognition, sensorimotor contingencies, dynamical systems, sensorimotor approach to perception, minimal cognition

The major insight in Robert Rosen’s view of a living organism as an (M, R)-system was the realization that an organism must be “closed to efficient causation”, which means that the catalysts needed for its operation must be generated internally. This aspect is not controversial, but there has been confusion and misunderstanding about the logic Rosen used to achieve this closure. In addition, his corollary that an organism is not a mechanism and cannot have simulable models has led to much argument, most of it mathematical in nature and difficult to appreciate. Here we examine some of the mathematical arguments and clarify the conditions for closure.

Key words: (M, R) systems, Metabolic circularity, Robert Rosen, Definitions of life, Autopoiesis, Autocatalytic sets, Chemoton, Impredicativity