John Collier

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.

Both natural and engineered systems are fundamentally dynamical in nature: their defining properties are causal, and their functional capacities are causally grounded. Among dynamical systems, an interesting and important sub-class are those that are autonomous, anticipative and adaptive (AAA). Living systems, intelligent systems, sophisticated robots and social systems belong to this class, and the use of these terms has recently spread rapidly through the scientific literature. Central to understanding these dynamical systems is their complicated organisation and their consequent capacities for re- and self- organisation. But there is at present no general analysis of these capacities or of the requisite organisation involved. We define what distinguishes AAA systems from other kinds of systems by characterising their central properties in a dynamically interpreted information theory.

Almost fifty years ago Wilfrid Sellars described two competing ways of imagining the world, the Manifest Image and the Scientific Image. The Manifest Image is an idealization of common sense aided by critical philosophy, whereas the Scientific Image is the product of our best science. The methodologies of the two images are very different: the Manifest Image deals with experience and looks only at relations among bits of experience and analysis of experience into the relations that must lie behind it, whereas the Scientific Image is grounded in explanations of experience, typically causal explanations. This need not be a problem if the two images are compatible. Sellars argued, however, that the Manifest Image implies continuity, but the best science of the time told us that the world is made up of discrete subatomic particles and discrete transitions between quantum states, making the two incompatible. Although Sellars noted that future science might show that the world is continuous, he did not follow this up. Science in the last fifty years has given much more evidence for continuity in the world from complexity studies and Quantum Mechanics, so perhaps the two images can be reconciled after all

Against standard approaches to evolution and ethics, this book develops the idea that moral values may find their origin in regularly recurring features in the cooperative environments of species of organisms that are social and intelligent. Across a wide range of species that are social and intelligent, possibilities arise for helping others, responding empathetically to the needs of others, and playing fairly. The book identifies these underlying environmental regularities as biological natural kinds and as natural moral values. As natural kinds, moral values help to provide more complete explanations for the selection of traits that arise in response to them. For example, helping in an aquatic environment is quite different than helping in an arboreal environment, and so we can expect the selection of traits for helping to reflect these underlying environmental differences. With the human ability to name, talk, and reason about important features of our environment, moral values become part of moral discourse and argument, helping to produce coherent systems of moral thought. Combining a naturalistic approach to morality with an equal emphasis on moral argument and truth, this book will be of interest to philosophers and historians of biology, theoretical biologists, comparative psychologists, and moral philosophers.

Information is commonly understood as knowledge or facts acquired or derived from, e.g., study, instruction or observation (Macmillan Contemporary Dictionary, 1979). On this notion, information is presumed to be both meaningful and veridical, and to have some appropriate connection to its object; it is concerned with representations and symbols in the most general sense MacKay 1969 ). Information might be misleading, but it can never be false. Deliberately misleading data is misinformation. The scientific notion of information abstracts from the representational idea, and includes anything that could potentially serve as a source of information. The most fundamental notion of information, attributed to a number of different authors, is "a distinction that makes a difference" MacKay 1969 ), or "a differnece that makes a difference" Bateson 1973 : 428). Information theory, then, is fundamentally the rigorous study of distinctions and their relations, inasmuch as they make a difference

Wilfrid Sellars described his Manifest Image and Scientific Image as idealizations of our common sense and scientific views of the world, including our own special role in the world as humans. If, as Sellars suggested, there is an irreconcilable conflict between these images, it may not be possible to reconcile science with common sense. The Scientific Image, as we have inherited it, has a strong reductionist element that seems to imply that things are not really as they appear to common sense. Although some amount of discrepancy between common sense and science is not a problem, since we know from experience that we may be mistaken about nearly any given thing, a systematic clash between science and common sense presents problems for the acceptance of one, the other, or both views

In this critical notice, I argue that the semantic view championed by Thompson no logical advantage over the syntactic view of theories, especially in the area of interpretation. Each weakness of the syntactic view has a corresponding weakness in the semantic view. In principle the two are not different in power, but it is sometimes better to adopt one rather than the other, for practical reasons. I agree with Thompson that many issues in the philosophy of biology can be illuminated by the semantic view, but that other things, especially deriving specific prediction, are best understood with the syntactic view

Complexly organized systems include biological and cognitive systems, as well as many of the everyday systems that form our environment. They are both common and important, but are not well understood. A complex system is, roughly, one that cannot be fully understood via analytic methods alone. An organized system is one that shows spatio-temporal correlations that are not determined by purely local conditions, though organization can be more or less localizable within a system. Organization and complexity can vary independently to some extent, but they are interconnected: organisation requires some complexity, but complexity cannot be maximum in an organized system. I will define complexity and organization more precisely, and show how these definitions imply the above properties. Next I will discuss how organized complexity can be modelled, with an eye to limitations on the tractability of both the models and the modelling process. I will finish with some remarks on the limits of our possible understanding of complexly organized systems. Keywords: complexity, organization, modelling, holism, information theory..

The purpose of this paper is to describe some limitations on scientific behaviorist and computational models of the mind. These limitations stem from the inability of either model to account for the integration of experience and behavior. Behaviorism fails to give an adequate account of felt experience, whereas the computational model cannot account for the integration of our behavior with the world. Both approaches attempt to deal with their limitations by denying that the domain outside their limits is a part of psychology. These attempts to turn the shortcomings of the two models into virtues would be more convincing if their limitations were not diametrically opposed. I will argue that in each case the limitations are too restrictive unless the theories are augmented by physiology

There are a number of different species concepts currently in use. The variety results from differing desiderata and practices of taxonomists, ecologists and evolutionary theorists. Recently, arguments have been presented for pluralism about species. I believe this is unsatisfactory, however, because of the central role of species in biological theory. Taking the line that species are individuals, I ask what might individuate them. In other work I have argued that dynamical systems are individuated by their cohesion. I present here a version of a cohesion concept of species that accounts for the advantages of other species concepts, and is open-ended enough to accommodate additions to or changes in biological theory. On my account biological forces combine like Newtonian forces, to work on a common locus. Just as we might have an electromagnetic system, we might have an ecological species, if the dominant source of cohesion is ecology, but there is no certainty that biological species will divide up into elegant single principle types anymore than there is for physical systems

Function refers to a broad family of concepts of varying abstractness and range of application, from a many-one mathematical relation of great generality to, for example, highly specialized roles of designed elements in complex machines such as degaussing in a television set, or contributory processes to control mechanisms in complex metabolic pathways, such as the inhibitory function of the appropriate part of the lac-operon on the production of lactase through its action on the genome in the absence of lactose. We would like a language broad enough, neutral enough, but yet powerful enough to cover all such cases, and at the same time to give a framework form explanation both of the family resemblances and differences. General logic and mathematics are too abstract, but more importantly, too broad, whereas other discourses of function, such as the biological and teleological contexts, are too narrow. Information is especially suited since it is mathematically grounded, but also has a wellknown physical interpretation through the Schr dinger/Brillouin Negentropy Principle of Information, and an engineering or design interpretation through Shannon's communication theory. My main focus will be on the functions of autonomous anticipatory systems, but I will try to demonstrate both the connections between this notion of function and the others, especially to dynamical systems with a physical interpretation on the one side and intentional systems on the other. The former are based in concepts like force, energy and work, while the latter involve notions like representation, control and purpose, traditionally, at least in Modern times, on opposite sides of the Cartesian divide. In principle, information can be reduced to energy, but it has the advantage of being more flexible, and easier to apply to higher level phenomena

In these notes I want to address some issues concerning self-organization that seem to me to apply generally from the micro-physical through the biological and social to the cosmological. That is, they are a part of the general theory of self-organization. I prefer to distinguish the theory of selforganization from the analysis of the concept of self-organization (which Maturana claims is oxymoronic, since there is no self that organizes1). General usage gives us something to which the term 'self-organization' refers. We can set aside the question of whether or not selves can really do such a thing until we know what it is they are supposed to do.2 This approach also allows the possibility that self-organization does not pick out a single natural kind, but may refer to a range of things that are grouped together by a Wittgenstein style “family resemblance”

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 their own conditions so as to increase their functionality (they self-regulate). Living systems self-regulate in order to increase their own viability. These increasingly stronger conditions, anticipation, autonomy and viability, can give an insight into progressively stronger classes of models of autonomy. I will argue that stronger forms are the relevant ones for Artificial Life. This has consequences for the design of and accurate simulation of living systems. Keywords: autonomy, modelling, function, simulation, anticipation, emergence DUBOIS’ CONJECTURE Autonomy basically means self-regulation. Self-regulation implies internal control of system states to achieve greater functionality, either internally or interactively with the environment. Functionality, as a teleological notion, implies that the system must be directed by likely future states; that is, it must anticipate and (possibly) adapt to likely future states. Functionality does not require autonomy (assuming functional goals are externally set), but merely that current states of the system can select suitable future states on the basis of suitable input. In this..

Progress has become a suspect concept in evolutionary biology, not the least because the core concepts of neo-Darwinism do not support the idea that evolution is progressive. There have been a number of attempts to account for directionality in evolution through additions to the core hypotheses of neo-Darwinism, but they do not establish progressiveness, and they are somewhat of an ad hoc collection. The standard account of fitness and adaptation can be rephrased in terms of information theory. From this, an information of adaptation can be defined in terms of a fitness function. The information of adaptation is a measure of the mutual information between biota and their environment. If the actual state of adaptation lags behind the state of optimal adaptation, then it is possible for the information of adaptation to increase indefinitely. Since adaptations are functional, this suggests the possibility of progressive evolution in the sense of increasing adaptation. Keywords: evolution, information, adaptation, fitness, entropy, progress..

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

The subject of this chapter is the identity of individual dynamical objects and properties. Two problems have dominated the literature: transtemporal identity and the relation between composition and identity. Most traditional approaches to identity rely on some version of classification via essential or typical properties, whether nominal or real. Nominal properties have the disadvantage of producing unnatural classifications, and have several other problems. Real properties, however, are often inaccessible or hard to define (strict definition would make them nominal). I suggest that classification should be in terms of dynamical properties of systems, starting with individual systems rather than classes, and working up by abstractions that fit causal generalities. The advantage of this approach is that individuality is testable and revisable as we come to know more about systems. Another advantage is that if anything is real, then it is the dynamical. Once I have presented this approach in general, I will show that the central concept of dynamical cohesion (the "dividing glue") is amenable to giving a principled account of individuation as a process, at the same time explaining the origin of diversity. Some other advantages of this approach are presented, including how it can be used as a basis for testable classifications. This last has moral implications, since cohesion at the individual and the social levels, and their interactions, can impinge on proper moral decisions.

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