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 (...) many such species are polymorphic. (shrink)

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 (...) observations of other members of the cluster comparatively uninformative. Yet, scientific practice often, and increasingly often, singles out as natural groups of properties that are not redundant, but synergic: instantiations of properties in synergic clusters are not necessarily informative about instantiations of other properties in the cluster; rather, it is their *joint* instantiation that plays the explanatory role for which the natural kind is recruited. (shrink)

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 (...) of information about the world. Since, on algorithmic information theory, a string is algorithmically random just if it is incompressible, the thesis entails that empirical data sets consist of algorithmically random strings of digits. Rather than constituting compressions of empirical data, scientific laws and theories pick out patterns that data sets exhibit with a certain noise.Author Keywords: Algorithmic randomness; Compression; Empirical data; Information; Law; Pattern. (shrink)

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 (...) of counterfactual robustness, causal profiles, causal connectivity, and privileged grain size. By doing so, I show how the philosophical notion of causation can be rendered in a format that is amenable for direct application of mathematical techniques from information theory such that the resulting informational measures are causal informational measures. This account provides a metaphysics of causation that supports interventionist semantics and causal modeling and discovery techniques. (shrink)

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 (...) string as a purely formal construct, lacking a physical interpretation, is either close to zero, or equal to the string's algorithmic complexity, or arbitrary, depending on the auxiliary criterion chosen to pick out the regular component of the string. Because of this flaw, the concept of effective complexity is unsuitable as a measure of information content. (shrink)

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 (...) must be regarded as containing all possible structures. The remainder of the article is devoted to elucidating the meaning and implications of the latter claim. (shrink)

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 (...) re- and self- organisation. But there is at present no general analysis of these capacities or of the requisite organisation involved. We define what distinguishes AAA systems from other kinds of systems by characterising their central properties in a dynamically interpreted information theory. (shrink)

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 (...) may seem that there is no way to deal with these conditions, but if we think of systems as dynamical non-equilibrium autonomous entities, there are ways to deal with the unexpected and irregular by taking advantage of self-organising and self-preserving capacities of such systems. A generalised force acting on a system far from equilibrium will cause the system to reorganise itself in the direction of the generalised force in such a way as to minimise its effects (Nicolis and Prigogine, 1977), but there can be unpredictable effects in different generalised directions in the system’s phase space. In order to preserve system integrity, these effects must be damped or used for further self-reorganisation, possibly starting a cascade effect that leaves the system in a substantially different state in which it can handle further instances of this sort of information. This model is similar to and extends the theoretical model of accommodation and assimilation of Piaget, derived from his observations of the development of intelligence in children. (shrink)