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  1. José Hernández-Orallo & David L. Dowe (2013). On Potential Cognitive Abilities in the Machine Kingdom. Minds and Machines 23 (2):179-210.
    Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different (...)
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  2. Simon Musgrave & David L. Dowe (2010). Kinship, Optimality, and Typology. Behavioral and Brain Sciences 33 (5):397-398.
    Jones uses a mechanism from the linguistic theory, Optimality Theory, to generate the range of kin systems observed in human cultures and human languages. The observed distribution of kinship systems across human societies suggests that some possibilities are preferred over others, a result that would indicate Jones' model needs to be refined, especially in its treatment of markedness.
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  3. David L. Dowe (2008). Minimum Message Length and Statistically Consistent Invariant (Objective?) Bayesian Probabilistic Inference—From (Medical) “Evidence”. Social Epistemology 22 (4):433 – 460.
    “Evidence” in the form of data collected and analysis thereof is fundamental to medicine, health and science. In this paper, we discuss the “evidence-based” aspect of evidence-based medicine in terms of statistical inference, acknowledging that this latter field of statistical inference often also goes by various near-synonymous names—such as inductive inference (amongst philosophers), econometrics (amongst economists), machine learning (amongst computer scientists) and, in more recent times, data mining (in some circles). Three central issues to this discussion of “evidence-based” are (i) (...)
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  4. David L. Dowe, Steve Gardner & and Graham Oppy (2007). Bayes Not Bust! Why Simplicity Is No Problem for Bayesians. British Journal for the Philosophy of Science 58 (4):709 - 754.
    The advent of formal definitions of the simplicity of a theory has important implications for model selection. But what is the best way to define simplicity? Forster and Sober ([1994]) advocate the use of Akaike's Information Criterion (AIC), a non-Bayesian formalisation of the notion of simplicity. This forms an important part of their wider attack on Bayesianism in the philosophy of science. We defend a Bayesian alternative: the simplicity of a theory is to be characterised in terms of Wallace's Minimum (...)
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