Peter Smith

The first part of this chapter revisits central themes in Armstrong’s defence and development of materialism about the mind. It argues that Armstrong’s focus on ‘peripheral’ mental states involving perception and intention leads to a mishandling of ‘core’ states, especially belief and desire. Frank Ramsey’s belief-desire model, developed decades earlier, enables a better handling of core mental states. The second part of the chapter looks at the present status of materialism—what the position amounts to, and why we might believe it. Parsimony arguments for materialism (used by Armstrong and Smart) are rejected. More compelling arguments make use of continuities between human and non-human mental capacities, and an evolutionary perspective. A form of ‘neutral monism’ is introduced as a better rival to materialism than other nonmaterialist views, but is also rejected.

Familiar summaries of evolution by natural selection hold that one requirement for change of this kind is heredity. Parents must resemble their offspring; like must beget like. However, many life cycles feature long and complex chains in which like begets unlike, before they return to their starting point. Such life cycles are common, being seen in various plants, animals, fungi, and protists. In response to these cases, the chapter offers a framework in which the familiar notion of reproduction is treated as a special case of a broader category.

Dennett argues that Darwinism provides a universal theory of adaptation and improvement in design. In his “Tower of Generate and Test,” Dennett distinguishes four kinds of creatures that realize a Darwinian pattern on different scales and with different degrees of sophistication: Darwinian, Skinnerian, Popperian, and Gregorian creatures. I examine Dennett’s tower in the light of recent work on learning, and in the context of the phylogenetic tree. A class of associative learners—Humean organisms—probably lies between Dennett’s Darwinian and Skinnerian creatures. Various cognitive capacities are also more demanding than instrumental conditioning, but insufficient for Popperian cognition in Dennett’s sense. Creatures corresponding to these intermediate stages are named—Carnapian, Pearlian, Tolmanian. These stages are not arranged in a tower.

This chapter discusses recent debates about scientific models and fictional or imaginary systems. Model-based science often apparently deals in non-actual or fictional systems, and does so by design. This practice raises questions about the relationships that can exist between such models and their real-world targets, especially about the evident empirical utility of some highly idealized scientific models. The chapter considers a range of recent treatments of models and offers an account that gives a central role to counterfactual conditionals. This chapter takes these conditionals to be the typical output of scientific modeling. These conditionals raise many problems of their own, but understanding models in these terms does enable some progress.

This chapter examines the idea that innateness can be understood in terms of genetic coding or genetic programming. A distinction is made between characteristics that are coded for or programmed for by the genes, and characteristics that are not. It is argued that the defensible versions of this distinction line up badly with the idea of innateness. The defensible versions of the idea of genetic coding treat only protein molecules as coded for. The defensible versions of the idea that developmental processes involve something like computation apply only to low-level processes, so the ‘programmed for’ traits are again too low-level to be of interest to nativists. Other versions of the idea that innate traits are ‘programmed to appear’ are in effect a handing-off of the problem to the concept of adaptation or evolutionary design.

Many of our concepts are introduced to us via, and seem only to be constrained by, roughand-ready explanations and some sample paradigm positive and negative applications. This happens even in informal logic and mathematics. Yet in some cases, the concepts in question – although only informally and vaguely characterized – in fact have, or appear to have, entirely determinate extensions. Here’s one familiar example. When we start learning computability theory, we are introduced to the idea of an algorithmically computable function (from numbers to numbers) – i.e. one whose value for any given input can be determined by a step-by-step calculating procedure, where each step is fully determined by some antecedently given finite set of calculating rules. We are told that we are to abstract from practical considerations of how many steps will be needed and how much ink will be spilt in the process, so long as everything remains finite. We are also told that each step is to be ‘small’ and the rules governing it must be ‘simple’, available to a cognitively limited calculating agent: for we want an algorithmic procedure, step-by-minimal-step, to be idiot-proof. For a classic elucidation of this kind, see e.g. Rogers (1967, pp. 1–5). Church’s Thesis, in one form, then claims this informally explicated concept in fact has a perfectly precise extension, the set of recursive functions. Church’s Thesis can be supported in a quasi-empirical way, by the failure of our searches for counterexamples. It can be supported too in a more principled way, by the observation that different appealing ways of sharpening up the informal chararacterization of algorithmic computability end up specifying the same set of recursive functions. But such considerations fall short of a demonstration of the Thesis. So is there a different argumentative strategy we could use, one that could lead to a proof? Sometimes it is claimed that there just can’t be, because you can never really prove results involving an informal concept like algorithmic computability..

Our understanding of communication and its evolution has advanced significantly through the study of simple models involving interacting senders and receivers of signals. Many theorists have thought that the resources of mathematical information theory are all that are needed to capture the meaning or content that is being communicated in these systems. However, the way theorists routinely talk about the models implicitly draws on a conception of content that is richer than bare informational content, especially in contexts where false content is important. This article shows that this concept can be made precise by defining a notion of functional content that captures the degree to which different states of the world are involved in stabilizing senders’ and receivers’ use of a signal at equilibrium. A series of case studies is used to contrast functional content with informational content, and to illustrate the explanatory role and limitations of this definition of functional content. _1_ Introduction _2_ Modelling Framework _3_ Two Kinds of Content _3.1_ Informational content _3.2_ Functional content _4_ Cases _4.1_ Case 1: Simplest case _4.2_ Case 2: Partial pooling _4.3_ Case 3: Bottleneck _4.4_ Case 4: Partial common interest _4.5_ Case 5: Deception _4.6_ Case 6: A further problem arising from divergent interests _5_ Discussion Appendix

How does science work? Does it tell us what the world is "really" like? What makes it different from other ways of understanding the universe? In Theory and Reality, Peter Godfrey-Smith addresses these questions by taking the reader on a grand tour of more than a hundred years of debate about science. The result is a completely accessible introduction to the main themes of the philosophy of science. Examples and asides engage the beginning student; a glossary of terms explains key concepts; and suggestions for further reading are included at the end of each chapter. Like no other text in this field, Theory and Reality combines a survey of recent history of the philosophy of science with current key debates that any beginning scholar or critical reader can follow. The second edition is thoroughly updated and expanded by the author with a new chapter on truth, simplicity, and models in science.

In 1931, the young Kurt Gödel published his First Incompleteness Theorem, which tells us that, for any sufficiently rich theory of arithmetic, there are some arithmetical truths the theory cannot prove. This remarkable result is among the most intriguing (and most misunderstood) in logic. Gödel also outlined an equally significant Second Incompleteness Theorem. How are these Theorems established, and why do they matter? Peter Smith answers these questions by presenting an unusual variety of proofs for the First Theorem, showing how to prove the Second Theorem, and exploring a family of related results (including some not easily available elsewhere). The formal explanations are interwoven with discussions of the wider significance of the two Theorems. This book - extensively rewritten for its second edition - will be accessible to philosophy students with a limited formal background. It is equally suitable for mathematics students taking a first course in mathematical logic.

I will sketch, but not argue for here, a hypothesis about its origins and structure. What philosophers think of as folk psychology has dual origins. One is a genuine "intuitive psychology." This is an evolved predictive tool seen also in some nonhuman animals and very young children. It is "peripheral" in what it recognizes and describes. Primarily, it recognizes seeing and acting (including trying) as activities of others. This is common element in how human and non-human animals deal with each other. The full folk psychology that we are familiar with in philosophical discussion also includes a model of psychological interactions within the periphery. This model is based perhaps on discourse, the exchange of sentences in public – roughly as Wilfrid Sellars suggested many years ago. This part of folk psychology, unlike the first, might be expected to show some cultural variation. The resulting framework might be fine for describing certain kinds of human thought, but it is problematic when applied more generally.

"Additive variance in fitness" is an important concept in the formal apparatus of population genetics. Wimsatt and Lloyd have argued that this concept can also be used to decide the "unit of selection" in an evolutionary process. The paper argues that the proposed criteria of Wimsatt and Lloyd are ambiguous, and several interpretations of their views are presented. It is argued that none of these interpretations provide acceptable criteria for deciding units of selection. The reason is that additive variance in fitness can be both a cause of evolution, but also a byproduct of selection at another level.

Sometimes themes can be found in common across very different systems in which change occurs. Imre Lakatos developed a theory of change in science, and one involving entities visible at different levels. There are theories defended at a particular time, and there are also research programs, larger units that bundle together a sequence of related theories and within which many scientists may work. Research programs are competing higher-level units within a scientific field. Scientific change involves change within research programs, and change in the ensemble of research programs present at a time, where some will be growing, some shrinking, some progressing, some degenerating. These are also themes in biological evolution. Recent biology has often found itself dealing with the relation between change at a level of "collectives" – such as organisms like us – and change at a lower level – the level of cells, genes, and other evolving parts. This work is continuous with an older discussion, one that arose when biological evolution was no more than a vague speculation, round the beginning of the 19th century. What is the living individual? What is the basic unit of life or living organization? Questions like this were pursued by Goethe, by Erasmus Darwin, the grandfather of Charles, and many others. Initially it was plants, especially, that were seen to raise these problems, and then newly described marine animals with strange life cycles. The discussion was influenced by the rise of the cell theory in the early 19th century, but some writers looked for individuals well below the level of the cell

A model of “ephemeral” population structure is presented that applies not only to biological systems in which discrete groups form but also to networks without group boundaries. The evolution of altruistic behaviors is discussed. Nonrandom interaction and nonlinear fitness structures are modeled; together, these factors can produce stable polymorphisms of altruistic and selfish types, as well as bistability. Empirical applications of the model may be found in microbes, marine invertebrates, annual plants, and other organisms.

Is Popper's philosophy alive or dead? If we make a judgment based on recent discussion in academic philosophy of science, he definitely seems to be fading. Popper is still seen as an important historical figure, a key part of the grand drama of 20th century thinking about science. He is associated with an outlook, a mindset, and a general picture of scientific work. His name has bequeathed us an adjective, "Popperian," that is well established. But the adjective is used for very general ideas that, according to most current philosophers, Popper did not develop convincingly. His detailed account is often seen as attractive on first impression, but full of holes that become bigger rather than smaller as discussion continues. The picture and the name remain, which is more than 1 most philosophers can hope for. But the name attaches more and more to a set of instincts and intuitions, less and less to views that are seeing ongoing philosophical development. Inside science itself, Popper's standing is quite different. He continues to be just about the only philosopher who can seize the imagination and command the loyalty of successful professional scientists. And he is popular within science not only for purposes of general commentary and public relations. Popper's philosophy is a resource drawn on by scientists in their internal debates about scientific matters. This has been especially marked in some parts of biology (Hull 1999). From the point of view of philosophers, this affection on the part of scientists may have an unflattering explanation. Popper offers a rather heroic view of the scientific character, featuring an appealing combination of creativity and hard-headedness. It is no surprise that scientists like to be described this way. It is no surprise that they prefer this picture to the one often (though inaccurately) associated with Hempel and Carnap – the scientist as a sort of logic-driven pattern-recognition machine. The same applies to the picture associated with Kuhn, who presents the normal scientist as a narrow-minded, indoctrinated member of a peculiar collective enterprise, an enterprise devoted to solving tiny puzzles most of the time but prone to occasional fits of crisis and chaos..