Generalized Darwinism models cultural development as an evolutionary process, where traits evolve through variation, selection, and inheritance. Inheritance describes either a discrete unit’s transmission or a mixing of traits. In this article, we compare classical models of cultural evolution and generalized population dynamics with respect to blending inheritance. We identify problems of these models and introduce our model, which combines relevant features of both. Blending is implemented as success-based social learning, which can be shown to be an optimal strategy.

Transfer of information between senders and receivers, of one kind or another, is essential to all life. David Lewis introduced a game theoretic model of the simplest case, where one sender and one receiver have pure common interest. How hard or easy is it for evolution to achieve information transfer in Lewis signaling?. The answers involve surprising subtleties. We discuss some if these in terms of evolutionary dynamics in both finite and infinite populations, with and without mutation.

It is not too great an exaggeration to claim that On the Origin of Species was, along with Das Kapital, one of the two most significant works in the intellectual history of the nineteenth century. As George Henry Lewes wrote in 1868, ‘No work of our time has been so general in its influence’. However, the very generality of the influence of Darwin’s work provides the chief problem for the intellectual historian. Most books and articles on the subject assert the (...) influence but remain very imprecise about its nature. It is very difficult indeed to assess what it was about the Darwinian theory which was so influential and how its influence was felt. This problem in Victorian intellectual history intersects with a related one in the history of science. There has been a tendency on the part of historians of science to isolate Darwin in two related ways. The first is to single him out from the mainstream of nineteenth-century naturalism in Britain and allow ‘Darwinism’ to stand duty for the wider movement of which it was in fact but a part. The second is the tendency to single out his evolutionary theory and to demarcate it sharply from those of his predecessors and contemporaries. According to this interpretation Darwin stood alone as a real, empirical scientist and provided the first genuinely scientific hypothesis for the process by which evolution might have occurred. The theories of the other main evolutionists—Erasmus Darwin, Lamarck, Chambers, Spencer, and Wallace—were more or less besmirched by ideological, anthropomorphic, or other ‘non-scientific’ factors or by the uses to which they were put by their authors. Charles Darwin is thus made to stand out as a figure of comparatively unalloyed scientific status and is treated in relative isolation from the social and intellectual context in which he worked and into which his theory was received. (shrink)

Recent debates about memetics have revealed some widespread misunderstandings about Darwinian approaches to cultural evolution. Drawing from these debates, this paper disputes five common claims: (1) mental representations are rarely discrete, and therefore models that assume discrete, gene-like particles (i.e., replicators) are useless; (2) replicators are necessary for cumulative, adaptive evolution; (3) content-dependent psychological biases are the only important processes that affect the spread of cultural representations; (4) the “cultural fitness” of a mental representation can be inferred from its successful (...) transmission; and (5) selective forces only matter if the sources of variation are random. We close by sketching the outlines of a unified evolutionary science of culture. (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)

The social exchange theory of reasoning, which is championed by Leda Cosmides and John Tooby, falls under the general rubric “evolutionary psychology” and asserts that human reasoning is governed by content-dependent, domain-specific, evolutionarily-derived algorithms. According to Cosmides and Tooby, the presumptive existence of what they call “cheater-detection” algorithms disconfirms the claim that we reason via general-purpose mechanisms or via inductively acquired principles. We contend that the Cosmides/Tooby arguments in favor of domain-specific algorithms or evolutionarily-derived mechanisms fail and that the notion (...) of a social exchange rule, which is central to their theory, is not correctly characterized. As a consequence, whether or not their conclusion is true cannot be established on the basis of the arguments they have presented. (shrink)

Certain hypotheses cannot be directly confirmed for theoretical, practical, or moral reasons. For some of these hypotheses, however, there might be a workaround: confirmation based on analogical reasoning. In this paper we take up Dardashti, Hartmann, Thébault, and Winsberg’s (in press) idea of analyzing confirmation based on analogical inference Baysian style. We identify three types of confirmation by analogy and show that Dardashti et al.’s approach can cover two of them. We then highlight possible problems with their model as a (...) general approach to analogical inference and argue that these problems can be avoided by supplementing Bayesian update with Jeffrey conditionalization. (shrink)