John Collier

Integrating concepts of maintenance and of origins is essential to explaining biological diversity. The unified theory of evolution attempts to find a common theme linking production rules inherent in biological systems, explaining the origin of biological order as a manifestation of the flow of energy and the flow of information on various spatial and temporal scales, with the recognition that natural selection is an evolutionarily relevant process. Biological systems persist in space and time by transfor ming energy from one state to another in a manner that generates structures which allows the system to continue to persist. Two classes of energetic transformations allow this; heat-generating transformations, resulting in a net loss of energy from the system, and conservative transformations, changing unusable energy into states that can be stored and used subsequently. All conservative transformations in biological systems are coupled with heat-generating transformations; hence, inherent biological production, or genealogical proesses, is positively entropic. There is a self-organizing phenomenology common to genealogical phenomena, which imparts an arrow of time to biological systems. Natural selection, which by itself is time-reversible, contributes to the organization of the self-organized genealogical trajectories. The interplay of genealogical (diversity-promoting) and selective (diversity-limiting) processes produces biological order to which the primary contribution is genealogical history. Dynamic changes occuring on times scales shorter than speciation rates are microevolutionary; those occuring on time scales longer than speciation rates are macroevolutionary. Macroevolutionary processes are neither redicible to, nor autonomous from, microevolutionary processes.

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..

We propose an objective and justifiable ethics that is contingent on the truth of evolutionary theory. We do not argue for the truth of this position, which depends on the empirical question of whether moral functions form a natural class, but for its cogency and possibility. The position we propose combines the advantages of Kantian objectivity with the explanatory and motivational advantages of moral naturalism. It avoids problems with the epistemological inaccessibility of transcendent values, while avoiding the relativism or subjectivism often associated with moral naturalism. Our position emerges out of criticisms of the contemporary sociobiological views of morality found in the writings of Richard Alexander, Michael Ruse, and Robert Richards.

Richard Alexander's second book on biology and morality is a continuation and amplification of the project he reported on in Darwinism and Human Affairs1. The Biology of Moral Systems is more abstract than the earlier book. It does not broach any new empirical ground, but puts Alexander's views into a broader context of philosophical and sociological discussions of morality. It discusses and criticizes alternative philosophical and biological views of morality, and presents his views on the significance of biology to moral issues in law, democracy and pursuit of the Good. In one interesting section that I will not be able to discuss here, Alexander provides an evolutionary hypothesis to explain each of Kohlberg's stages of moral development (pp. 131ff). The book ends with a discussion of some specific moral problems.

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.

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.

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..

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..