Stephen M. Downes

I critically examine the semantic view of theories to reveal the following results. First, models in science are not the same as models in mathematics, as holders of the semantic view claim. Second, when several examples of the semantic approach are examined in detail no common thread is found between them, except their close attention to the details of model building in each particular science. These results lead me to propose a deflationary semantic view, which is simply that model construction is an important component of theorizing in science. This deflationary view is consistent with a naturalized approach to the philosophy of science.

I argue that Evolutionary Psychologists’ notion of adaptationism is closest to what Peter Godfrey-Smith (2001) calls explanatory adaptationism and as a result, is not a good organizing principle for research in the biology of human behavior. I also argue that adopting an alternate notion of adaptationism presents much more explanatory resources to the biology of human behavior. I proceed by introducing Evolutionary Psychology and giving some examples of alternative approaches to the biological explanation of human behavior. Next I characterize adaptation and explain the range of biological phenomena that can count as adaptations. I go onto introduce the range of adaptationist views that have been distinguished by philosophers of biology and lay out explanatory adaptationism in detail.

I propose an approach to naturalized philosophy of science that takes the social nature of scientific practice seriously. I criticize several prominent naturalistic approaches for adopting "cognitive individualism", which limits the study of science to an examination of the internal psychological mechanisms of scientists. I argue that this limits the explanatory capacity of these approaches. I then propose a three-level model of the social nature of scientific practice, and use the model to defend the claim that scientific knowledge is socially produced

Alison Gopnik and Andrew Meltzoff have argued for a view they call the ‘theory theory’: theory change in science and children are similar. While their version of the theory theory has been criticized for depending on a number of disputed claims, we argue that there is a fundamental problem which is much more basic: the theory theory is multiply ambiguous. We show that it might be claiming that a similarity holds between theory change in children and (i) individual scientists, (ii) a rational reconstruction of a Superscientist, or (iii) the scientific community. We argue that (i) is false, (ii) is non-empirical (which is problematic since the theory theory is supposed to be a bold empirical hypothesis), and (iii) is either false or doesn’t make enough sense to have a truth-value. We conclude that the theory theory is an interesting failure. Its failure points the way to a full, empirical picture of scientific development, one that marries a concern with the social dynamics of science to a psychological theory of scientific cognition.

The papers in this volume present varying approaches to human aggression, each from an evolutionary perspective. The evolutionary studies of aggression collected here all pursue aspects of patterns of response to environmental circumstances and consider explicitly how those circumstances shape the costs and benefits of behaving aggressively. All the authors understand various aspects of aggression as evolved adaptations but none believe that this implies we are doomed to continued violence, but rather that variation in aggression has evolutionary roots. These papers reveal several similarities between human and nonhuman aggression, including our response to physical strength as an indicator of fighting ability, testosterone response to competition, a sensitivity to paternity, and baseline features of intergroup aggression in foragers and chimps. There is also one paper tackling the phylogeny of these traits. The many differences between human and nonhuman aggression are also pursued here. Topics here include the impact of modern weapons and extremes of wealth and power on both the costs and benefits of fighting, and the scale to which coercion can promote aggression that acts against a fighter’s own interests. Also the implications of large-scale human sociality are discussed.

Alternative splicing allows for the production of many gene products from a single coding sequence. I introduce the concept of alternative splicing via some examples. I then discuss some current hypotheses about the explanatory role of alternative splicing, including the claim that splicing is a significant contributor to the difference in complexity between the human genome and proteosome. Hypotheses such as these bring into question our working concepts of the gene. I examine several gene concepts introduced to cope with processes such as alternative splicing. Next I introduce some hypotheses about the evolution of mechanisms alternative splicing in higher organisms. I conclude that attention to alternative splicing reveals that we adopt an attitude that developmental theorizing must inform evolutionary theorizing and vice versa

Several prominent philosophers of science, most notably Ron Giere, propose that scientific theories are collections of models and that models represent the objects of scientific study. Some, including Giere, argue that models represent in the same way that pictures represent. Aestheticians have brought the picturing relation under intense scrutiny and presented important arguments against the tenability of particular accounts of picturing. Many of these arguments from aesthetics can be used against accounts of representation in philosophy of science. I rely on Dominic Lopes' recent summary of arguments against various views of picturing and reformulate some of them to fit the philosophy of science context. My specific targets here are Giere and Steven French. I go on to argue that assuming all scientific models and images represent in the same way is not the best guide to understanding scientific practice

In this paper I assess Gopnik and Meltzoff's developmental psychology of science as a contribution to the understanding of scientific development. I focus on two specific aspects of Gopnik and Meltzoff's approach: the relation between their views and recapitulationist views of ontogeny and phylogeny in biology, and their overall conception of cognition as a set of veridical processes. First, I discuss several issues that arise from their appeal to evolutionary biology, focusing specifically on the role of distinctions between ontogeny and phylogeny when appealing to biology for theoretical support. Second, I argue that to presuppose that cognition is veridical or "truth-tropic" can compromise attempts to understand scientific cognition both throughout history and in the present. Finally, I briefly sketch an evolutionary approach to understanding scientific development that contrasts with Gopnik and Meltzoff's

LLMs are statistical models of language learning through stochastic gradient descent with a next token prediction objective. Prompting a popular view among AI modelers: LLMs are just next token predictors. While LLMs are engineered using next token prediction, and trained based on their success at this task, our view is that a reduction to just next token predictor sells LLMs short. Moreover, there are important explanations of LLM behavior and capabilities that are lost when we engage in this kind of reduction. In order to draw this out, we will make an analogy with a once prominent research program in biology explaining evolution and development from the gene's eye view.

Debates about human nature inform every philosophical tradition from their inception (see Stevenson 2000 for many examples). Evolutionarily based criticisms of human nature are of much more recent origin. Ironically, most evolutionarily based criticisms of human nature are directed at work whose avowed goal is to biologicize human nature and even to place human nature within an evolutionary frame. Here I will focus on accounts of human nature that begin with and come after E.O. Wilson’s sociobiology. I will also focus on criticisms of human nature that arose first as responses to sociobiology. There are some more recent approaches to human nature that have much in common with the sociobiological approach and I will show that critical arguments developed to target sociobiology have purchase on related recent approaches to human nature. In what follows I will briefly outline some well-known accounts of human nature. Next I will briefly outline some key evolutionarily based arguments against such accounts of human nature. I conclude by summarizing the evolutionary case against biological accounts of human nature and endorsing it.

LLMs are statistical models of language learning through stochastic gradient descent with a next token prediction objective. Prompting a popular view among AI modelers: LLMs are just next token predictors. While LLMs are engineered using next token prediction, and trained based on their success at this task, our view is that a reduction to just next token predictor sells LLMs short. Moreover, there are important explanations of LLM behavior and capabilities that are lost when we engage in this kind of reduction. In order to draw this out, we will make an analogy with a once prominent research program in biology explaining evolution and development from the genes eye view.

Biologists, climate scientists, and economists all rely on models to move their work forward. In this book, I explore the use of models in these and other fields to introduce readers to the various philosophical issues that arise in scientific modeling. I show that paying attention to models plays a crucial role in appraising scientific work.　 After surveying a wide range of models from a number of different scientific disciplines, I demonstrate how focusing on models sheds light on many perennial issues in philosophy of science and in philosophy in general. For example, reviewing the range of views on how models represent their targets introduces readers to the key issues in debates on representation, not only in science but in the arts as well. Also, standard epistemological questions are cast in new and interesting ways when we confront the question, "What makes for a good (or bad) model?"

Fisher’s 1918 paper accomplished two distinct goals: unifying discrete Mendelian genetics with continuous biometric phenotypes and quantifying the variance components of variation in complex human characteristics. The former contributed to the foundation of modern quantitative genetics; the latter was adopted by social scientists interested in the pursuit of Galtonian nature-nurture questions about the biological and social origins of human behavior, especially human intelligence. This historical divergence has produced competing notions of the estimation of variance ratios referred to as heritability. Jay Lush showed that they could be applied to selective breeding on the farm, while the early twin geneticists used them as a descriptive statistic to describe the degree of genetic determination in complex human traits. Here we trace the early history of the heritability coefficient now used by social scientists.

In this paper I examine various ways in whichphilosophers have made connections between truth andnatural selection. I introduce several versions ofthe view that mechanisms of true belief generationarise as a result of natural selection and argue thatthey fail to establish a connection between truth andnatural selection. I then turn to scientific truthsand argue that evolutionary accounts of the origin ofscientific truth generation mechanisms also fail. Iintroduce David Hull's selectionist model ofscientific development and argue that his account ofscientific success does not rely on connecting truthand natural selection. I argue that Hull's model,which severs the connection between truth andselection, can account for some aspects of scientificchange, but it still leaves us plenty of questionsabout what aspects of our individual cognitive make-upcontribute to scientific change and how they do so. I introduce an evolutionary approach to scientificcognition that shows how some of these questions canbe answered without making an explanatory appeal toselection for true belief generating mechanisms.