We present a tentative proposal for a quantitative measure of autonomy. This is something that, surprisingly, is rarely found in the literature, even though autonomy is considered to be a basic concept in many disciplines, including artificial life. We work in an information theoretic setting for which the distinction between system and environment is the starting point. As a first measure for autonomy, we propose the conditional mutual information between consecutive states of the system conditioned on the history of the environment. This works well when the system cannot influence the environment at all and the environment does not interact synergetically with the system. When, in contrast, the system has full control over its environment, we should instead neglect the environment history and simply take the mutual information between consecutive system states as a measure of autonomy. In the case of mutual interaction between system and environment there remains an ambiguity regarding whether system or environment has caused observed correlations. If the interaction structure of the system is known, we define a “causal” autonomy measure which allows this ambiguity to be resolved. Synergetic interactions still pose a problem since in this case causation cannot be attributed to the system or the environment alone. Moreover, our analysis reveals some subtle facets of the concept of autonomy, in particular with respect to the seemingly innocent system–environment distinction we took for granted, and raises the issue of the attribution of control, i.e. the responsibility for observed effects. To further explore these issues, we evaluate our autonomy measure for simple automata, an agent moving in space, gliders in the game of life, and the tessellation automaton for autopoiesis of Varela et al.

Excerpt: Very broadly speaking, autonomy is self-determination: the ability to do what one does independently, without being forced so to do by some outside power. The “doing” may be mental, behavioural, neurological, metabolic, or autopoietic: autonomy can be ascribed to a system on a number of different levels.

This paper summarizes recent attempts by this author to create a transdisciplinary, non-Cartesian and non-reductionistic framework for information studies in natural, social, and technological systems. To confront, in a scientific way, the problems of modern information technology where phenomenological man is dealing with socially constructed texts in algorithmically based digital bit-machines we need a theoretical framework spanning from physics over biology and technological design to phenomenological and social production of signification and meaning. I am working with such pragmatic theories as second order cybernetics (coupled with autopolesis theory), Lakoffs biologically oriented cognitive semantics, Peirce’s triadic semiotics, and Wittgenstein’s pragmatic language game theory. A coherent synthesis of these theories is what the cybersemiotic framework attempts to accomplish.

Key words: cybersemiotics, information science, second order cybernetics, autopoiesis, triadic sign

The work of physicist and theoretical biologist Howard Pattee has focused on the roles that symbols and dynamics play in biological systems. Symbols, as discrete functional switching-states, are seen at the heart of all biological systems in the form of genetic codes, and at the core of all neural systems in the form of informational mechanisms that switch behavior. They also appear in one form or another in all epistemic systems, from informational processes embedded in primitive organisms to individual human beings to public scientific models. Over its course, Pattee’s work has explored (1) the physical basis of informational functions (dynamical vs. rule-based descriptions, switching mechanisms, memory, symbols), (2) the functional organization of the observer (measurement, computation), (3) the means by which information can be embedded in biological organisms for purposes of self-construction and representation (as codes, modeling relations, memory, symbols), and (4) the processes by which new structures and functions can emerge over time. We discuss how these concepts can be applied to a high-level understanding of the brain. Biological organisms constantly reproduce themselves as well as their relations with their environs. The brain similarly can be seen as a self-producing, self-regenerating neural signaling system and as an adaptive informational system that interacts with its surrounds in order to steer behavior.

Key words: Adaptive systems, biological cybernetics, biological semiotics, dynamical systems, emergence, epistemology, evolutionary robotics, genetic code, neural code, neurocomputation, self-organization, symbols.

This paper has two primary aims. The first is to provide an introductory discussion of hyperset theory and its usefulness for modeling complex systems. The second aim is to provide a hyperset analysis of several perspectives on autonomy: Robert Rosen’s metabolism-repair systems and his claim that living things are closed to efficient cause, Maturana and Varela’s autopoietic systems, and Kauffman’s cataytically closed systems. Consequences of the hyperset models for Rosen’s claim that autonomous systems have non-computable models are discussed.

Key words: Autonomy, Autopoiesis, Catalytic closure, Francisco Varela, Hyperset, Robert Rose

Autonomous systems are the result of self-sustaining processes of constitution of an identity under precarious circumstances. They may transit through different modes of dynamical engagement with their environment, from committed ongoing coping to open susceptibility to external demands. This paper discusses these two statements and presents examples of models of autonomous behaviour using methods in evolutionary robotics. A model of an agent capable of issuing self-instructions demonstrates the fragility of modelling autonomy as a function rather than as a property of a system’s organization. An alternative model of behavioural preference based on homeostatic adaptation avoids this problem by establishing a mutual constraining between lower-level processes (neural dynamics and sensorimotor interaction) and higher-level metadynamics (experience-dependent, homeostatic triggering of local plasticity and re-organization). The results of these models are lessons about how strong autonomy should be approached: neither as a function, nor as a matter of external vs. internal determination.

Key words: biological autonomy, modelling autonomous behaviour, evolutionary robotics, self-setting of goals, behavioural preference.

This paper reviews Pattee’s ideas about the symbolic domain as a phenomenon related to the self-simplifying processes of certain hierarchical systems, such as the living. We distinguish the concepts of constraint, record, and symbol to explain how the Semantic Closure Principle, that is to say, the view that symbols are self-interpreted by the cell, emerges. Related to this, the notion of complementarity is discussed both as an epistemological and as an ontological principle. In the final discussion we consider whether autonomous systems can exist in which constraints are not symbolically preserved, and if biological symbols can be considered to have a descriptive nature.

Key words: Constraint, record, semantic closure, symbol

The concept of autopoiesis, amended as a system theory, is necessary and sufficient to provide an operational definition of life, a set of criteria by which the living are categorically distinguished from the non-living. Limitations are placed on the domains in which autopoiesis may be exhibited.

Key words: Autopoiesis, physical domain, physical realization of autopoiesis, system logic of the living

Life as an autonomous homeostatic system is discussed. A mechanism that drives a homeostatic state to an autonomous self-moving state is examined with two computational cell models. The mechanism is met with Ashby’s ultrastability, where random parameter searching is activated when a system breaks a viability constraint. Such a random search process is replaced by the membrane shape in the first model and by chaotic population dynamics in the second model. Emergence of sensors, motors and the recursive coupling between them is shown to be a natural outcome of an autonomous homeostatic system.

Key words: Autonomy, autopoieisis, self-reproduction, chemotaxisis, sensory–motor coupling