John Collier

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..

Functionality is essential to any form of anticipation beyond simple directedness at an end. In the literature on function in biology, there are two distinct approaches. One, the etiological view, places the origin of function in selection, while the other, the organizational view, individuates function by organizational role. Both approaches have well-known advantages and disadvantages. I propose a reconciliation of the two approaches, based in an interactivist approach to the individuation and stability of organisms. The approach was suggested by Kant in the Critique of Judgment, but since it requires, on his account, the identification a new form of causation, it has not been accessible by analytical techniques. I proceed by construction of the required concept to fit certain design requirements. This construction builds on concepts introduced in my previous four talks to these meetings.

In Robert West’s talk last week, dynamical systems theory (DST) was applied to a specific problem involving interacting symbolic systems, without much reference to how those systems are embodied or related to other types of systems. Despite this level of abstraction, DST can yield interesting results, though one might be left wondering if it really leads to understanding, or what it all means. In particular, Robert noted problems he has in convincing referees that the sort of explanation he gave can give a useful understanding, and that it doesn’t invoke dubious notions with its references to emergence, holism, and mathematical openness.

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.

Systematics, along with the other comparative biological sciences and certain astronomical disciplines, is much more concerned with form and organization than other biological and physical sciences, in which dynamics plays the central role. Within the biological sciences, Nelson (1970) characterizes disciplines that study diversity and patterns “comparative” and those that search for process and dynamics “general.” The goal of “general” science is to uncover the mechanisms that unify observed phenomena. Whether the physicist sees herself, like Newton (1953: 3-5), to be discovering fundamental causal explanations, or like Duhem (1962: 19-30), to be uncovering a natural classification, dynamical concepts referring to essentially hidden processes are indispensable. Much of comparative biology, in contrast, is primarily descriptive, with explanations taking a secondary role, at least until the diversity is described and comparisons made. Unlike the general sciences, in which elegance and quantitative analysis have long been major indicators of truth, the comparative sciences stress complexity, happenstance and qualitative analysis. In systematics in particular, theory and explanation are often thought to play a minimal role, if any at all1.