Darwin provided us with a powerful theoretical framework to explain the evolution of living systems. Natural selection alone, however, has sometimes been seen as insufficient to explain the emergence of new levels of selection. The problem is one of “circularity” for evolutionary explanations: how to explain the origins of Darwinian properties without already invoking their presence at the level they emerge. That is, how does evolution by natural selection commence in the first place? Recent results in experimental evolution suggest a (...) way forward: Paul Rainey and his collaborators have shown that Darwinian properties could be exogenously imposed via what they call “ecological scaffolding.” This could solve the “black box” dilemma faced by Darwinian explanations of new levels of organization. Yet, despite “scaffolding” recently becoming a popular term in the study of cognition, culture, and evolution, the concept has suffered from vagueness and ambiguity. This article aims to show that scaffolding can be turned into a proper scientific concept able to do explanatory work within the context of the major evolutionary transitions. Doing so will allow us to once again extend the scope of the Darwinian model of evolution by natural selection. (shrink)

In order to develop a true biological science of consciousness, we have to remove humans from the center of reference and develop a bottom-up comparative study of animal minds, as Donald Griffin intended with his call for a “cognitive ethology.” In this article, I make use of the pathological complexity thesis (Veit 2022a, b, c ) to show that we can firmly ground a comparative study of animal consciousness by drawing on the resources of state-based behavioral life history theory. By (...) comparing the different life histories of gastropods and arthropods, we will be able to make better sense of the possible origins of consciousness and its function for organisms in their natural environments. (shrink)

In a recent special issue dedicated to Dani Rodrik’s (2015) influential monograph Economics Rules, Grüne-Yanoff and Marchionni (2018) raise a potentially damning problem for Rodrik’s suggestion that progress in economics should be understood and measured laterally, by a continuous expansion of new models. They argue that this could lead to an “embarrassment of riches”, i.e. the rapid expansion of our model library to such an extent that we become unable to choose between the available models, and thus needs to be (...) solved to make ‘model pluralism’ viable. Drawing on Veit’s (2019a) ‘model pluralism’ account, this paper argues that model pluralism as a thesis about the relationship between science and nature undermines the very idea of a general model selection framework for policy making. (shrink)

In his 2009 monograph, Darwinian Populations and Natural Selection, Peter Godfrey-Smith accuses biologists of demonstrating ‘Darwinian Paranoia’ when they engage in what he dubs ‘agential thinking’. But as Daniel Dennett points out, he offers neither an illuminating set of examples nor an extended argument for this assertion, deeming it to be a brilliant propaganda stroke against what is actually a useful way of thinking. Compared to the dangers of teleological thinking in biology, the dangers of agential thinking have unfortunately rarely (...) been discussed. Drawing on recent work by Samir Okasha, I attempt to remedy this omission, through analyzing the nature of agential thinking, and providing a philosophical treatment of the unexamined dangers in this peculiar, yet tempting way of thinking. (shrink)

Metaphors are found all throughout science: in published papers, working hypotheses, policy documents, lecture slides, grant proposals, and press releases. They serve different functions, but perhaps most striking is the way they enable understanding, of a theory, phenomenon, or idea. In this paper, we leverage recent advances on the nature of metaphor and the nature of understanding to explore how they accomplish this feat. We attempt to shift the focus away from the epistemic value of the content of metaphors, to (...) the epistemic value of the metaphor’s consequences. Many famous scientific metaphors are epistemically good, not primarily because of what they say about the world, but because of how they cause us to think. Specifically, metaphors increase understanding either by improving our sets of representations, or by making it easier for us to encode and process data about complex subjects by changing how we are disposed to conceptualize those subjects. This view hints towards new positions concerning testimonial understanding, factivity, abilities, discovery via metaphor, and the relation between metaphors and models. (shrink)