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  1. Toward an Algorithmic Metaphysics.Steve Petersen - 2013 - In David Dowe (ed.), Algorithmic Probability and Friends: Bayesian Prediction and Artificial Intelligence. Springer. pp. 306-317.
    There are writers in both metaphysics and algorithmic information theory (AIT) who seem to think that the latter could provide a formal theory of the former. This paper is intended as a step in that direction. It demonstrates how AIT might be used to define basic metaphysical notions such as *object* and *property* for a simple, idealized world. The extent to which these definitions capture intuitions about the metaphysics of the simple world, times the extent to which we think the (...)
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  • Informationally-Connected Property Clusters, and Polymorphism.Manolo Martínez - 2015 - Biology and Philosophy 30 (1):99-117.
    I present and defend a novel version of the homeostatic property cluster account of natural kinds. The core of the proposal is a development of the notion of co-occurrence, central to the HPC account, along information-theoretic lines. The resulting theory retains all the appealing features of the original formulation, while increasing its explanatory power, and formal perspicuity. I showcase the theory by applying it to the problem of reconciling the thesis that biological species are natural kinds with the fact that (...)
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  • Synergic Kinds.Manolo Martínez - forthcoming - Synthese:1-16.
    According to the *homeostatic property cluster* family of accounts, one of the main conditions for groups of properties to count as natural is that these properties be frequently co-instantiated. I argue that this condition is, in fact, not necessary for natural-kindness. Furthermore, even when it is present, the focus on co-occurrence distorts the role natural kinds play in science. Co-occurrence corresponds to what information theorists call *redundancy*: observing the presence of some of the properties in a frequently co-occurrent cluster makes (...)
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  • Real Patterns.Daniel C. Dennett - 1991 - Journal of Philosophy 88 (1):27-51.
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  • Algorithmic Randomness in Empirical Data.James W. McAllister - 2003 - Studies in History and Philosophy of Science Part A 34 (3):633-646.
    According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers (...)
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  • Patterns, Information, and Causation.Holly Andersen - 2017 - Journal of Philosophy 114 (11):592-622.
    This paper articulates an account of causation as a collection of information-theoretic relationships between patterns instantiated in the causal nexus. I draw on Dennett’s account of real patterns to characterize potential causal relata as patterns with specific identification criteria and noise tolerance levels, and actual causal relata as those patterns instantiated at some spatiotemporal location in the rich causal nexus as originally developed by Salmon. I develop a representation framework using phase space to precisely characterize causal relata, including their degree (...)
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  • Effective Complexity as a Measure of Information Content.James W. McAllister - 2003 - Philosophy of Science 70 (2):302-307.
    Murray Gell-Mann has proposed the concept of effective complexity as a measure of information content. The effective complexity of a string of digits is defined as the algorithmic complexity of the regular component of the string. This paper argues that the effective complexity of a given string is not uniquely determined. The effective complexity of a string admitting a physical interpretation, such as an empirical data set, depends on the cognitive and practical interests of investigators. The effective complexity of a (...)
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  • What Do Patterns in Empirical Data Tell Us About the Structure of the World?James W. McAllister - 2011 - Synthese 182 (1):73-87.
    This article discusses the relation between features of empirical data and structures in the world. I defend the following claims. Any empirical data set exhibits all possible patterns, each with a certain noise term. The magnitude and other properties of this noise term are irrelevant to the evidential status of a pattern: all patterns exhibited in empirical data constitute evidence of structures in the world. Furthermore, distinct patterns constitute evidence of distinct structures in the world. It follows that the world (...)
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  • Rainforest Realism and the Unity of Science.Don Ross, James Ladyman & John Collier - 2007 - In James Ladyman (ed.), Every Thing Must Go: Metaphysics Naturalized. Oxford University Press.
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  • Saving the Phenomena.James Bogen & James Woodward - 1988 - Philosophical Review 97 (3):303-352.
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  • Complexly Organised Dynamical Systems.John Collier & Cliff Hooker - 1999 - Open Systems and Information Dynamics 6 (3):241–302.
    Both natural and engineered systems are fundamentally dynamical in nature: their defining properties are causal, and their functional capacities are causally grounded. Among dynamical systems, an interesting and important sub-class are those that are autonomous, anticipative and adaptive (AAA). Living systems, intelligent systems, sophisticated robots and social systems belong to this class, and the use of these terms has recently spread rapidly through the scientific literature. Central to understanding these dynamical systems is their complicated organisation and their consequent capacities for (...)
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  • Logical Depth and Physical Complexity.C. H. Bennett - 1988 - In R. Herken (ed.), The universal Turing machine, a half century survey. Oxford University Press. pp. 227-257.
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  • Dealing with the Unexpected.John Collier - unknown
    Typically, we think of both artificial and natural computing devices as following rules that allow them to alter their behaviour (output) according to their environment (input). This approach works well when the environment and goals are well defined and regular. However, 1) the search time for appropriate solutions quickly becomes intractable when the input is not fairly regular, and 2) responses may be required that are not computable, either in principle, or given the computational resources available to the system. It (...)
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