Theories in “soft” areas of psychology lack the cumulative character of scientific knowledge. They tend neither to be refuted nor corroborated, but instead merely fade away as people lose interest. Even though intrinsic subject matter difficulties (20 listed) contribute to this, the excessive reliance on significance testing is partly responsible, being a poor way of doing science. Karl Popper’s approach, with modifications, would be prophylactic. Since the null hypothesis is quasi-always false, tables summarizing research in terms of patterns of “significant differences” are little more than complex, causally uninterpretable outcomes of statistical power functions. Multiple paths to estimating numerical point values (“consistency tests”) are better, even if approximate with rough tolerances; and lacking this, ranges, orderings, second-order differences, curve peaks and valleys, and function forms should be used. Such methods are usual in developed sciences that seldom report statistical significance. Consistency tests of a conjectural taxometric model yielded 94 % success with zero false negatives.

as a testbed for designing intelligent agents. The system consists of an overall agent architecture and five components within the architecture. The five components are: 1. Goal-Directed Learning (GDL), a decision-theoretic method for selecting learning goals. 2. Probabilistic Bias Evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals. 3. Uniquely Predictive Theories (UPTs) and Probability Computation using Independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories. 4. A probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories. 5. A decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA is given as input an initial planning goal (its ove

Mathematics can provide precise formulations of relatively vague concepts and problems from the real world, and bring out underlying structure common to diverse scientific areas. Sometimes very natural mathematical concepts lie neglected and not widely understood for many years, before their fundamental relevance is recognised and their explanatory power is fully exploited. The notion of definability in a structure is such a concept, and Turing’s [77] 1939 model of interactive computation provides a fruitful context in which to exercise the usefulness of definability as a powerful and widely applicable source of understanding. In this article we set out to relate this simple idea to one of the oldest and apparently least scientifically approachable of problems — that of realistically modelling how mental properties supervene on physical ones. Mathematics can provide precise formulations of relatively vague concepts and problems from the real world, and bring out underlying structure common to diverse scientific areas. Sometimes very natural mathematical concepts lie neglected and not widely understood for many years, before their fundamental relevance is recognised and their explanatory power is fully exploited. Previously we have argued that the notion of definability in a structure is such a concept, and pointed to Turing’s [77] 1939 model of interactive computation as providing a fruitful context in which to exercise the usefulness of definability as a powerful and widely applicable source of understanding. Below, we relate this simple idea to one of the oldest and apparently least scientifically approachable of problems — that of realistically modelling how mental properties supervene on physical ones. We will first briefly review the origins with René Descartes of mind-body dualism, and the problem of mental causation. We will then summarise the subsequent difficulties encountered, and their current persistence, and the more recent usefulness of the concept of supervenience in

Computational science is a productive intellectual activity. It produces highly useful computer programs that require much creativity and ingenuity to develop. Moreover, computation is a powerful theoretical tool for natural scientists. However, can computational science have a scientific foundation, quite apart from its roles as a juxtaposition of disciplines and as another theoretical tool for scientists? That is, can computational science develop concepts that enable a broad systematic understanding of inference and discovery in science? This paper makes a case for an affirmative answer that relies on the concept of "generic scientific task." We will argue that theoretical understanding is to be attained by identifying and automating such tasks. To develop the idea, we configure samples of previous work in computational science (broadly construed), lay a road map to guide further research, and suggest experimental tools to generate research problems and to re-deploy proven technique...

Knowledge systems as currently configured are static in their concept sets. As knowledge maintenance becomes more sophisticated, the need to address issues concerning dynamic concept sets will naturally arise. Such dynamics is properly called ontology revision, or in the simpler case, expansion. A number of sub-disciplines in artificial intelligence, philosophy and recursion theory have results that are relevant to ontology expansion even though their motivations were quite different. More recently in artificial intelligence ontologies have been explicitly considered. This paper is partly a summary of early results, and partly an account of ongoing work in this area. Keywords: ontology, concept formation, theoretical term, predicate invention, theory change, induction, type hierarchy, action. (Appears in Proceedings of the 3rd International Conference on Conceptual Structures', Springer Lecture Notes in Artificial Intelligence, v. 954, pp 1-14, Ed. G. Ellis, R. Levinson, W. Rich, and...

www.jonathandonner.com/DonnerToyama_ISI2009prepub.pdf for updates concerning the formal proceedings Persistent themes in ICT4D Research: priorities for intermethodological exchange

Consider the classical model of a Turing machine with an oracle. The classical oracle is a one step external consultation device. The oracle may contain either non-computable information, or computable information provided just to speed up the computations of the Turing machine. In this talk we will consider the abstract experimenter (e.g. the experimental physicist) as a Turing machine and the abstract experiment of measuring a physical quantity (using a specified physical apparatus) as an oracle to the Turing machine. The algorithm running in the machine abstracts the experimental method of measurement (encoding the recursive structure of experimental actions) chosen by the experimenter. It is standard to consider that to measure a real number 4 µ, e.g. the value of a physical quantity, the experimenter (now the Turing machine) should proceed by approximations. Thus, besides the value of µ, we will consider dyadic rational approximations (denoted by finite binary strings), and a procedure to measure µ proved to be universal.