In recent years, some have claimed that a Darwinian perspective will revolutionize the study of human society and culture. This project is viewed with disdain and suspicion, on the other hand, by many practicing social scientists. This article seeks to clear the air in this heated debate by dissociating two claims that are too often assumed to be inseparable. The first is the ‘ontological’ claim that Darwinian principles apply, at some level of abstraction, to human society and culture. The second (...) is the more ‘pragmatic’ claim that this observation necessitates substantial changes in the practices of social scientists. Even if some version of the first claim is true, I argue – which I believe is quite likely – the second does not follow. This observation ought to chasten the most overzealous advocates of Darwinian social science, as well as softening the instinctive resistance of many social scientists and historians to the genuine insights enabled by a Darwinian approach. The conclusion discusses these insights, the most important of which is a methodological prescription for normative theory. (shrink)

This short paper is about the generalized evolutionary approach to the economics of science. Stephen Toulmin and David Hull are pioneers of the former rather than Karl Popper whose falsification thesis was sociologically naive. Useful directions for the future would go beyond the generalities of variation, transmission and selection towards making more explicit use of Darwin’s “two great principles.” The first is “the unity of types” i.e. common descent by employing phylogenetic methods to answer historical questions. The second is “the (...) conditions of existence” i.e. natural selection by making use of general principles of evolutionary ecology and socioecology to answer questions about why something evolved. (shrink)

While evolutionary thinking is increasingly becoming popular in fields of investigation outside the biological sciences, it remains unclear how helpful it is there and whether it actually yields good explanations of the phenomena under study. Here we examine the ontology of a recent approach to applying evolutionary thinking outside biology, the generalized Darwinism approach proposed by Geoffrey Hodgson and Thorbjørn Knudsen. We examine the ontology of populations in biology and in GD, and argue that biological evolutionary theory sets ontological criteria (...) that GD fails to meet. We suggest two options to revise the population concept in GD: reformulating the concept in terms of inheritance and reproduction such that it comes to pick out individuals similar to evolving populations, or trying to build an adequate population concept on a principle of differential retention instead of differential reproduction. 1 Introduction2 Generalized Darwinism2.1 What is generalized Darwinism?2.2 Darwinian principles3 The Ontology of Generalized Darwinism: What Are Populations?3.1 The population concept of generalized Darwinism3.2 The population concept in evolutionary theory4 Locating Evolving Systems in Generalized Darwinism5 Conclusion. (shrink)

An original quantum foundations concept of a deep learning computational Universe is introduced. The fundamental information of the Universe (or Triuniverse)is postulated to evolve about itself in a Red, Green and Blue (RGB) tricoloured stable self-mutuality in three information processing loops. The colour is a non-optical information label. The information processing loops form a feedback-reinforced deep learning macrocycle with trefoil knot topology. Fundamental information processing is driven by ψ-Epistemic Drive, the Natural appetite for information selected for advantageous knowledge. From its (...) substrate of Mathematics, the knotted information processing loops determine emergent Physics and thence the evolution of super-emergent Life (biological and artificial intelligence). RGB-tricoloured information is processed in sequence in an Elemental feedback loop (R), then an Operational feedback loop (G), then a Structural feedback loop (B) and back to an Elemental feedback loop (R), and so on around the trefoil in deep learning macrocycles. It is postulated that hierarchical information correspondence from Mathematics through Physics to Life is mapped and conserved within each colour. The substrate of Mathematics has RGB-tricoloured feedback loops which are respectively Algebra (R), Algorithms (G) and Geometry (B). In Mathematics, the trefoil macrocycle is Algebraic Algorithmic Geometry and its correlation system is a Tensor Neural Knot Network enabling Qutrit Entanglement. Emergent Physics has corresponding RGB-tricoloured feedback loops of Quantum Mechanics (R), Quantum Deep Learning (G) and Quantum Geometrodynamics(B). In Physics, the trefoil macrocycle is Quantum Intelligent Geometrodynamics and its correlation system is Quantum Darwinism. Super-emergent Life has corresponding RGB-tricoloured loops of Variation (R), Selection (G) and Heredity (B). In the evolution of Life, the trefoil macrocycle is Variational Selective Heredity and its correlation ecosystem is Darwin’s ecologically “Entangled Bank”. (shrink)