John Collier

Complex systems are dynamic and may show high levels of variability in both space and time. It is often difficult to decide on what constitutes a given complex system, i.e., where system boundaries should be set, and what amounts to substantial change within the system. We discuss two central themes: the nature of system definitions and their ability to cope with change, and the importance of system definitions for the mental metamodels that we use to describe and order ideas about system change. Systems can only be considered as single study units if they retain their identity. Previous system definitions have largely ignored the need for both spatial and temporal continuity as essential attributes of identity. After considering the philosophical issues surrounding identity and system definitions, we examine their application to modeling studies. We outline a set of five alternative metamodels that capture a range of the basic dynamics of complex systems. Although Holling’s adaptive cycle is a compelling and widely applicable metamodel that fits many complex systems, there are systems that do not necessarily follow the adaptive cycle. We propose that more careful consideration of system definitions and alternative metamodels for complex systems will lead to greater conceptual clarity in the field and, ultimately, to more rigorous research.

We argue that living systems process information such that functionality emerges in them on a continuous basis. We then provide a framework that can explain and model the normativity of biological functionality. In addition we offer an explanation of the anticipatory nature of functionality within our overall approach. We adopt a Peircean approach to Biosemiotics, and a dynamical approach to Digital-Analog relations and to the interplay between different levels of functionality in autonomous systems, taking an integrative approach. We then apply the underlying biosemiotic logic to a particular biological system, giving a model of the B-Cell Receptor signaling system, in order to demonstrate how biosemiotic concepts can be used to build an account of biological information and functionality. Next we show how this framework can be used to explain and model more complex aspects of biological normativity, for example, how cross-talk between different signaling pathways can be avoided. Overall, we describe an integrated theoretical framework for the emergence of normative functions and, consequently, for the way information is transduced across several interconnected organizational levels in an autonomous system, and we demonstrate how this can be applied in real biological phenomena. Our aim is to open the way towards realistic tools for the modeling of information and normativity in autonomous biological agents.

Evolutionary moral realism is the view that there are moral values with roots in evolution that are both specifically moral and exist independently of human belief systems. In beginning to sketch the outlines of such a view, we examine moral goods like fairness and empathetic caring as valuable and real aspects of the environments of species that are intelligent and social, or at least developing along an evolutionary trajectory that could lead to a level of intelligence that would enable individual members of the species to recognize and respond to such things as the moral goods they in fact are. We suggest that what is most morally interesting and important from a biological perspective is the existence and development of such trajectories, rather than the position of one particular species, such as our own, on one particular trajectory.

Speculation about the evolutionary origins of morality has yet to show how a biologically based capacity for morality might be connected to moral reasoning. Applying an evolutionary approach to three kinds of cases where partiality may or may not be morally reasonable, this paper explores a possible connection between a psychological capacity for morality and processes of wide reflective moral equilibrium. The central hypothesis is that while we might expect a capacity for morality to include aspects of partiality, we might also expect these same aspects of the capacity to produce systemic forms of performance-based error. Understanding these errors helps point the way toward a theory of moral competence that includes aspects of both partiality and impartiality.

We find symmetry attractive. It is often an indicator of the deep structure of things, whether they be natural phenomena, or artificial. For example, the most fundamental conservation laws of physics are all based in symmetry. Similarly, the symmetries found in religious art throughout the world are intended to draw attention to deep spiritual truths. Not only do we find symmetry pleasing, but its discovery is often also surprising and illuminating as well. For these reasons, we are inclined to think that symmetries are informative. On the other hand, symmetries represent a kind of invariance under transformation, i.e., redundancies. Redundancy, in turn, implies that the information content of a symmetrical structure or configuration is less than that of a similar nonsymmetrical structure. Symmetry, then, entails a reduction in information content. These considerations present us with somewhat of a paradox. On the one hand, many symmetries that we find in the world are surprising, and surprise indicates informativeness. On the other hand, the surprise value of information arises because it presents us with the unexpected or improbable, but symmetries, far from creating the unexpected, ensure that the known can be extended through invariant transformations. How can this paradox be resolved? Rhythmic entrainment is the formation of regular, predictable patterns in time and/or space through interactions within or between systems (resulting in symmetry). The result of entrainment is a simplification of the entrained system. It is the complement to symmetry breaking. Entrainment can be either forced or spontaneous, with the spontaneous form being uncontrollable. It results from processes that are called self-organising. Interestingly, spontaneous entrainment require much less power to form and maintain. It is also the source of levels in systems. I finish with some observations for social systems.

Biological systems are typically hierarchically organized, open, nonlinear systems, and inherit all of the characteristics of such systems that are found in the purely physical and chemical domains, to which all biological systems belong. In addition, biological systems exhibit functional properties, and they contain information in a form that is used internally to make required functional distinctions. The existence of these additional biological properties is widely granted, but their exact nature is controversial. I will address first the issue of biological function, and then turn to the issue of information in biosystems.

Developments in science in the last few decades have led to doubts about the validity of the mechanical paradigm that has dominated science since the Scientific Revolution. The new views, coming from recently founded disciplines like non-equilibrium thermodynamics, chaos theory and the theory of dynamical systems, are rooted in physics. Nonetheless, much of their motivation comes from fields as diverse as weather prediction, ecology, economics, the study of traffic flow, and the growth of cities. Although Quantum Mechanics also led to doubts about the validity of the mechanical paradigm, the new views reveal problems within classical physics itself. The implications of these developments for our understanding of space have been largely unexamined. But the close connection between the Newtonian view of space and the mechanical paradigm means that the demise of the mechanical paradigm will require a re-evaluation of our understanding of physical space.

It is generally agreed that organisms are Complex Adaptive Systems. Since the rise of Cybernetics in the middle of the last century ideas from information theory and control theory have been applied to the adaptations of biological organisms in order to explain how they work. This does not, however, explain functionality, which is widely but not universally attributed to biological systems. There are two approaches to functionality, one based on etiology (what a trait was selected for), and the other based in autonomy. I argue that the etiological approach, as understood in terms of control theory, suffers from a problem of symmetry, by which function can equally well be placed in the environment as in the organism. Focusing on the autonomy view, I note that it can be understood to some degree in terms of control theory in its version called second order cybernetics. I present an approach to second order cybernetics that seems plausible for organisms with limited computational power, due to Hooker, Penfold and Evans. They hold that this approach gives something like concepts, certainly abstractions from specific situations, a trait required for functionality in its system adaptive form (i.e., control of the system by itself). Using this cue, I argue that biosemiotics provides the methodology to incorporate these quasi concepts into an account of functionality.

A major goal of science is to discover laws that underlie all regular phenomena. This goal is best satisfied by eternal principles that leave fundamental properties unchanged and unchangeable. Science has been forced to accept that some processes, especially biological processes, are inherently time oriented. It can either forgo the ideal of universal principles, and account for temporality through specific boundary conditions, or else incorporate the sources of change directly into fundamental principles that are the same for all times and places, and for all temporal scales. In the past, unifying principles adequate for biology have caused trouble for physics, and vice versa. Recent work at the intersection of non-equilibrium statistical mechanics and information theory suggests that physics and biology can finally be reconciled.

Anticipation allows a system to adapt to conditions that have not yet come to be, either externally to the system or internally. Autonomous systems actively control the conditions of their own existence so as to increase their overall viability. This paper will first give minimal necessary and sufficient conditions for autonomous anticipation, followed by a taxonomy of autonomous anticipation. In more complex systems, there can be semi-autonomous subsystems that can anticipate and adapt on their own. Such subsystems can be integrated into a system’s overall autonomy, typically with greater efficiency due to modularity and specialization of function. However, it is also possible that semi-autonomous subsystems can act against the viability of the overall system, and have their own functions that conflict with overall system functions.