Abstract. Temporal pattern learning, control and prediction, and chaotic data analysis share a common problem: deducing optimal equations of motion from observations of time-dependent behavior. Each desires to obtain models of the physical world from limited information. We describe a method to reconstruct the deterministic portion of the equations of motion directly from a data series. These equations of motion represent a vast reduction of a chaotic data set’s observed complexity to a compact, algorithmic specification. This approach employs an informational measure of model optimality to guide searching through the space of dynamical systems. As corollary results, we indicate how to estimate the minimum embedding dimension, extrinsic noise level, metric entropy, and Lyapunov spectrum. Numerical and experimental applications demonstrate the method’s feasibility and limitations. Extensions to estimating parametrized families of dynamical systems from bifurcation data and to spatial pattern evolution are presented. Applications to predicting chaotic data and the design of forecasting, learning, and control systems, are discussed. 1.

: Biodiversity is a data-intense science, drawing as it does on data from a large number of disciplines in order to build up a coherent picture of the extent and trajectory of life on earth. This paper argues that as sets of heterogeneous databases are made to converge, there is a layering of values into the emergent infrastructure. It is argued that this layering process is relatively irreversible, and that it operates simultaneously at a very concrete level (fields in a database) and at a very abstract one (the coding of the relationship between the disciplines and the production of a general ontology). Finally, it is maintained that science studies as a discipline is able to (and should) make a significant contribution to the design of robust and flexible databases which recognize this performative character of infrastructure. Introduction The form of scientific work which has been most studied by sociologists of science is that which leads from the laboratory to the scientific pap...

Inferring models from given data leads through many different changes in representation. Most are subtle and profitably ignored. Nonetheless, any such change affects the semantic content of the resulting model and so, ultimately, its utility. A model's semantic structure determines what its elements mean to an observer that has built and uses it. In the search for an understanding of how large-scale thermodynamic systems might themselves take up the task of modeling and so evolve semantics from syntax, the present paper lays out a constructive approach to modeling nonlinear processes based on computation theory. It progresses from the microscopic level of the instrument and individual measurements, to a mesoscopic scale at which models are built, and concludes with a macroscopic view of their thermodynamic properties. Once the computational structure of the model is brought into the analysis it becomes clear how a thermodynamic system can support semantic information processing. * Inte...

Abstract. This paper, combining the standpoints of philosophy and Artificial Intelligence with theoretical psychology, summarises several decades of investigation by the author of the variety of functions of vision in humans and other animals, pointing out that biological evolution has solved many more problems than are normally noticed. For example, the biological functions of human and animal vision are closely related to the ability of humans to do mathematics, including discovering and proving theorems in geometry, topology and arithmetic. Many of the phenomena discovered by psychologists and neuroscientists require sophisticated controlled laboratory settings and specialised measuring equipment, whereas the functions of vision reported here mostly require only careful attention to a wide range of everyday competences that easily go unnoticed. Currently available computer models and neural theories are very far from explaining those functions, so progress in explaining how vision works is more in need of new proposals for explanatory mechanisms than new laboratory data. Systematically formulating the requirements for such mechanisms is not easy. If we start by analysing familiar competences, that can suggest new experiments to clarify precise forms of these competences, how they develop within individuals, which other species have them, and how performance varies according to conditions. This will help to constrain requirements for models purporting to explain how the competences work. For example, Gibson’s theory of affordances needs a number of extensions, including allowing affordances to be composed in several ways from lower level proto-affordances. The paper ends with speculations regarding the need for new kinds of information-processing machinery to account for the phenomena.

This paper provides an introduction to data practices in biodiversity science. This is an area where multiple scientific domains are in constant interaction, and use data from multiple sources in that process. There is a consequent huge proliferation of technical standards in the field.

“It is quite true what Philosophy says: that Life must be understood backwards. But that makes one forget the other saying: that it must be lived—forwards. The more one ponders this, the more it comes to mean that life in the temporal existence never becomes quite intelligible, precisely because at no moment can I find complete quiet to take the backward- looking position” Sören Kierkegaard, 1990. ii Design, once considered solely as a practical planning activity for handcrafts, has evolved into a comprehensive human-centered thinking activity. The next step in this evolution is establishing a Science of Design, an endeavor that fascinated design theorists including Herbert Simon and Horst Rittel. However, even terminologically, Science and Design have long been considered as the opposite extremes of the spectrum of human thinking. Traditionally, Science exploits strictly analytical and deterministic

Einstein initially objected to the probabilistic aspect of quantum mechanics— the idea that God is playing at dice. Later he changed his ground, and focussed instead on the point that the Copenhagen Interpretation leads to what Einstein saw as the abandonment of physical realism. We argue here that Einstein’s initial intuition was perfectly sound, and that it is precisely the fact that quantum mechanics is a fundamentally probabilistic theory which is at the root of all the controversies regarding its interpretation. Probability is an intrinsically logical concept. This means that the quantum state has an essentially logical significance. It is extremely difficult to reconcile that fact with Einstein’s belief, that it is the task of physics to give us a vision of the world apprehended sub specie aeternitatis. Quantum mechanics thus presents us with a simple choice: either to follow Einstein in looking for a theory which is not probabilistic at the fundamental level, or else to accept that physics does not in fact put us in the position of God looking down on things from above. There is a widespread fear that the latter alternative must inevitably lead to a greatly impoverished,

This contribution to the debate on confidence limits focuses mostly on the case of measurements with `open likelihood', in the sense that it is defined in the text. I will show that, though a prior-free assessment of confidence is, in general, not possible, still a search result can be reported in a mostly unbiased and efficient way, which satisfies some desiderata which I believe are shared by the people interested in the subject. The simpler case of `closed likelihood' will also be treated, and I will discuss why a uniform prior on a sensible quantity is a very reasonable choice for most applications. In both cases, I think that much clarity will be achieved if we remove from scientific parlance the misleading expressions `confidence intervals' and `confidence levels'.

I will discuss only one of the several entwined strands of the philosophy of

Contents 1 Induction: The Problem and How To Solve It 7 1.1 The Problem of Induction . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Hypothetical Imperatives for Inductive Inference . . . . . . . . . 9 1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.1 Means-Ends Vindications of Traditional Proposals . . . . 11 1.3.2 Novel Solutions to Traditional Problems . . . . . . . . . . 11 1.3.3 New Questions and Answers . . . . . . . . . . . . . . . . . 12 1.3.4 Analysis of Inductive Problems from Scientific Practice . 14 1.3.5 Rational Choice in Games . . . . . . . . . . . . . . . . . . 14 1.4 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 A Model of Scientific Inquiry 17 2.1 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 A Model of Scientific Inquiry . . . . . . . . . . . . . . . . . . . . 18 2.3 Examples of Inductive Problems and Scientific Methods . . . . . 26