Massimo Pigliucci

Lewis et al. (2011) attempted to restore the reputation of Samuel George Morton, a 19th century physician who reported on the skull sizes of different folk-races. Whereas Gould (1978) claimed that Morton’s conclusions were invalid because they reflected unconscious bias, Lewis et al. alleged that Morton’s findings were, in fact, supported, and Gould’s analysis biased. We take strong exception to Lewis et al.’s thesis that Morton was “right.” We maintain that Gould was right to reject Morton’s analysis as inappropriate and misleading, but wrong to believe that a more appropriate analysis was available. Lewis et al. fail to recognize that there is, given the dataset available, no appropriate way to answer any of the plausibly interesting questions about the “populations” in question (which in many cases are not populations in any biologically meaningful sense). We challenge the premise shared by both Gould and Lewis et al. that Morton’s confused data can be used to draw any meaningful conclusions. This, we argue, reveals the importance of properly focusing on the questions asked, rather than more narrowly on the data gathered.

Science and philosophy have a very long history, dating back at least to the 16th and 17th centuries, when the first scientist-philosophers, such as Bacon, Galilei, and Newton, were beginning the process of turning natural philosophy into science. Contemporary relationships between the two fields are still to some extent marked by the distrust that maintains the divide between the so-called “two cultures.” An increasing number of philosophers, however, are making conceptual contributions to sciences ranging from quantum mechanics to evolutionary biology, and a few scientists are conducting research relevant to classically philosophical fields of inquiry, such as consciousness and moral decision-making. This article will introduce readers to the borderlands between science and philosophy, beginning with a brief description of what philosophy of science is about, and including a discussion of how the two disciplines can fruitfully interact not only at the level of scholarship, but also when it comes to controversies surrounding public understanding of science.

The relationship between ethics and science has been discussed within the framework of continuity versus discontinuity theories, each of which can take several forms. Continuity theorists claim that ethics is a science or at least that it has deep similarities with the modus operandi of science. Discontinuity theorists reject such equivalency, while at the same time many of them claim that ethics does deal with objective truths and universalizable statements, just not in the same sense as science does. I propose here a third view of quasi-continuity (or, equivalently, quasi-discontinuity) that integrates ethics and science as equal partners toward the uncovering of new knowledge. In this third way, a program envisioned by William James but made practicable only by contemporary scientific advancement, science can and must inform ethics at a deep level, and ethical theory— while going beyond science—cannot do without it. In particular, I identify four areas of ethics-science collaboration: neurobiological research into the basis of moral judgment, comparative anthropol- ogy, comparative evolutionary biology of primates, and game-theo- retical modeling. I provide examples within each of these fields to show how they link to ethical theories (including prescriptive work) and questions. The essay concludes with a brief discussion of the light that a scientifically informed ethics can shed on some classical problems in moral theory, such as the relationships between rational- ity and selfishness, egoism and altruism, as well as the concept of social contract. A joint research program involving both philosophers and scientists is called for if we wish to move ethical theory into the twenty-first century.

Public discussions of science are often marred by two pernicious phenomena: a widespread rejection of scientific findings (e.g., the reality of anthropogenic climate change, the conclusion that vaccines do not cause autism, or the validity of evolutionary theory), coupled with an equally common acceptance of pseudoscientific notions (e.g., homeopathy, psychic readings, telepathy, tall tales about alien abductions, and so forth). The typical reaction by scientists and science educators is to decry the sorry state of science literacy among the general public, and to call for more science education as the answer to both problems. But the empirical evidence concerning the relationship between science literacy, rejection of science and acceptance of pseudoscience is mixed at best. In this chapter I argue that—while certainly important—efforts at increasing public knowledge of science (science education) need to be complemented by attention to common logical fallacies (philosophy), cognitive biases and dissonance (psychology), and the role of ideological commitments (sociology). Even this complex, multi-disciplinary approach to science education will likely only yield measurable results in the very long term. Meanwhile science remains, as Carl Sagan famously put it, a candle in the dark, delicate and in need of much nurturing.

This paper outlines a critique of the use of the genetic variance–covariance matrix (G), one of the central concepts in the modern study of natural selection and evolution. Specifically, I argue that for both conceptual and empirical reasons, studies of G cannot be used to elucidate so-called constraints on natural selection, nor can they be employed to detect or to measure past selection in natural populations – contrary to what assumed by most practicing biologists. I suggest that the search for a general solution to the difficult problem of identifying causal structures given observed correlation’s has led evolutionary quantitative geneticists to substitute statistical modeling for the more difficult, but much more valuable, job of teasing apart the many possible causes underlying the action of natural selection. Hence, the entire evolutionary quantitative genetics research program may be in need of a fundamental reconsideration of its goals and how they correspond to the array of mathematical and experimental techniques normally employed by its practitioners

Philosophers of science have given up on the quest for a silver bullet to put an end to all pseudoscience, as such a neat formal criterion to separate good science from its contenders has proven elusive. In the literature on critical thinking and in some philosophical quarters, however, this search for silver bullets lives on in the taxonomies of fallacies. The attractive idea is to have a handy list of abstract definitions or argumentation schemes, on the basis of which one can identify bad or invalid types of reasoning, abstracting away from the specific content and dialectical context. Such shortcuts for debunking arguments are tempting, but alas, the promise is hardly if ever fulfilled. Different strands of research on the pragmatics of argumentation, probabilistic reasoning and ecological rationality have shown that almost every known type of fallacy is a close neighbor to sound inferences or acceptable moves in a debate. Nonetheless, the kernel idea of a fallacy as an erroneous type of argument is still retained by most authors. We outline a destructive dilemma we refer to as the Fallacy Fork: on the one hand, if fallacies are construed as demonstrably invalid form of reasoning, then they have very limited applicability in real life. On the other hand, if our definitions of fallacies are sophisticated enough to capture real-life complexities, they can no longer be held up as an effective tool for discriminating good and bad forms of reasoning. As we bring our schematic “fallacies” in touch with reality, we seem to lose grip on normative questions. Even approaches that do not rely on argumentation schemes to identify fallacies fail to escape the Fallacy Fork, and run up against their own version of it

Evolutionary biology is a field currently animated by much discussion concerning its conceptual foundations. On the one hand, we have supporters of a classical view of evolutionary theory, whose backbone is provided by population genetics and the so-called Modern Synthesis (MS). On the other hand, a number of researchers are calling for an Extended Synthe- sis (ES) that takes seriously both the limitations of the MS (such as its inability to incorporate developmental biology) and recent empirical and theoretical research on issues such as evolvability, modularity, and self-organization. In this article, I engage in an in-depth commentary of an influential paper by population geneticist Michael Lynch, which I take to be the best defense of the MS-population genetics position published so far. I show why I think that Lynch’s arguments are wanting and propose a modification of evolutionary theory that retains but greatly expands on population genetics.

Discussions about the biological bases (or lack thereof) of the concept of race in the human species seem to be never ending. One of the latest rounds is represented by a paper by Neven Sesardic, which attempts to build a strong scientific case for the existence of human races, based on genetic, morphometric and behavioral characteristics, as well as on a thorough critique of opposing positions. In this paper I show that Sesardic’s critique falls far short of the goal, and that his positive case is exceedingly thin. I do this through a combination of analysis of the actual scientific findings invoked by Sesardic and of some philo- sophical unpacking of his conceptual analysis, drawing on a dual professional background as an evolu- tionary biologist and a philosopher of science.

The term “scientism” is used in a variety of ways with both negative and positive connotations. I suggest that some of these uses are inappropriate, as they aim simply at dismissing without argument an approach that a particular author does not like. However, there are legitimate negative uses of the term, which I explore by way of an analogy with the term “pseudoscience.” I discuss these issues by way of a recent specific example provided by a controversy in the field of bioethics concerning the value, or lack thereof, of homeopathy. I then frame the debate about scientism within the broader context of C.P. Snow’s famous essay on the “two cultures.”

Phenotypic plasticity integrates the insights of ecological genetics, developmental biology, and evolutionary theory. Plasticity research asks foundational questions about how living organisms are capable of variation in their genetic makeup and in their responses to environmental factors. For instance, how do novel adaptive phenotypes originate? How do organisms detect and respond to stressful environments? What is the balance between genetic or natural constraints (such as gravity) and natural selection? The author begins by defining phenotypic plasticity and detailing its history, including important experiments and methods of statistical and graphical analysis. He then provides extended examples of the molecular basis of plasticity, the plasticity of development, the ecology of plastic responses, and the role of costs and constraints in the evolution of plasticity. A brief epilogue looks at how plasticity studies shed light on the nature/nurture debate in the popular media.

In a now classic paper published in 1991, Alberch introduced the concept of genotype–phenotype (G!P) mapping to provide a framework for a more sophisticated discussion of the integration between genetics and developmental biology that was then available. The advent of evo-devo first and of the genomic era later would seem to have superseded talk of transitions in phenotypic space and the like, central to Alberch’s approach. On the contrary, this paper shows that recent empirical and theoretical advances have only sharpened the need for a different conceptual treat- ment of how phenotypes are produced. Old-fashioned metaphors like genetic blueprint and genetic programme are not only woefully inadequate but positively misleading about the nature of G!P, and are being replaced by an algorithmic approach emerging from the study of a variety of actual G!P maps. These include RNA folding, protein function and the study of evolvable soft- ware. Some generalities are emerging from these disparate fields of analysis, and I suggest that the concept of ‘developmental encoding’ (as opposed to the classical one of genetic encoding) provides a promising computational–theoretical underpinning to coherently integrate ideas on evolvability, modularity and robustness and foster a fruitful framing of the G!P mapping problem.

The idea of phenotypic novelty appears throughout the evolutionary literature. Novelties have been defined so broadly as to make the term meaningless and so narrowly as to apply only to a limited number of spectacular structures. Here I examine some of the available definitions of phenotypic novelty and argue that the modern synthesis is ill equipped at explaining novelties. I then discuss three frameworks that may help biologists get a better insight of how novelties arise during evolution but warn that these frameworks should be considered in addition to, and not as potential substitutes of, the modern synthesis. †To contact the author, please write to: Departments of Ecology and Evolution and Philosophy, Stony Brook University, Stony Brook, NY 11794; e‐mail: [email protected].

Stoicism is an ancient Greco-Roman practical philosophy focused on the ethics of everyday living. It is a eudaemonistic (i.e., emphasizing one’s flourishing) approach to life, as well as a type of virtue ethics (i.e., concerned with the practice of virtues as central to one’s existence). This paper summarizes the basic tenets of Stoicism and discusses how it tackles the issues of death and suicide. It presents a number of exercises that modern Stoics practice in order to prepare for death (one’s own, or those of relatives and friends). It argues that modern Stoicism is a viable personal philosophy.

Phenotypic Evolution explicitly recognizes organisms as complex genetic-epigenetic systems developing in response to changing internal and external environments. As a key to a better understanding of how phenotypes evolve, the authors have developed a framework that centers on the concept of the Developmental Reaction Norm. This encompasses their views: (1) that organisms are better considered as integrated units than as disconnected parts (allometry and phenotypic integration); (2) that an understanding of ontogeny is vital for evaluating evolution of adult forms (ontogenetic trajectories, epigenetics, and constraints); and (3) that environmental heterogeneity is ubiquitous and must be acknowledged for its pervasive role in phenotypic expression.

The “demarcation problem,” the issue of how to separate science from pseu- doscience, has been around since fall 1919—at least according to Karl Pop- per’s (1957) recollection of when he first started thinking about it. In Popper’s mind, the demarcation problem was intimately linked with one of the most vexing issues in philosophy of science, David Hume’s problem of induction (Vickers 2010) and, in particular, Hume’s contention that induction cannot be logically justified by appealing to the fact that “it works,” as that in itself is an inductive argument, thereby potentially plunging the philosopher straight into the abyss of a viciously circular argument.

Are science and religion compatible when it comes to understanding cosmology (the origin of the universe), biology (the origin of life and of the human species), ethics, and the human mind (minds, brains, souls, and free will)? Do science and religion occupy non-overlapping magisteria? Is Intelligent Design a scientific theory? How do the various faith traditions view the relationship between science and religion? What, if any, are the limits of scientific explanation? What are the most important open questions, problems, or challenges confronting the relationship between science and religion, and what are the prospects for progress? These and other questions are explored in Science and Religion: 5 Questions--a collection of thirty-three interviews based on 5 questions presented to some of the world's most influential and prominent philosophers, scientists, theologians, apologists, and atheists. Contributions by Simon Blackburn, Susan Blackmore, Sean Carroll, William Lane Craig, William Dembski, Daniel C. Dennett, George F.R. Ellis, Owen Flanagan, Owen Gingerich, Rebecca Newberger Goldstein, John F. Haught, Muzaffar Iqbal, Lawrence Krauss, Colin McGinn, Alister McGrath, Mary Midgley, Seyyed Hossein Nasr, Timothy O'Connor, Massimo Pigliucci, John Polkinghorne, James Randi, Alex Rosenberg, Michael Ruse, Robert John Russell, John Searle, Michael Shermer, Victor J. Stenger, Robert Thurman, Michael Tooley, Charles Townes, Peter van Inwagen, Keith Ward, Rabbi David Wolpe.

The concept of burden of proof is used in a wide range of discourses, from philosophy to law, science, skepticism, and even in everyday reasoning. This paper provides an analysis of the proper deployment of burden of proof, focusing in particular on skeptical discussions of pseudoscience and the paranormal, where burden of proof assignments are most poignant and relatively clear-cut. We argue that burden of proof is often misapplied or used as a mere rhetorical gambit, with little appreciation of the underlying principles. The paper elaborates on an important distinction between evidential and prudential varieties of burdens of proof, which is cashed out in terms of Bayesian probabilities and error management theory. Finally, we explore the relationship between burden of proof and several (alleged) informal logical fallacies. This allows us to get a firmer grip on the concept and its applications in different domains, and also to clear up some confusions with regard to when exactly some fallacies (ad hominem, ad ignorantiam, and petitio principii) may or may not occur

‘‘Theoretical biology’’ is a surprisingly heter- ogeneous field, partly because it encompasses ‘‘doing the- ory’’ across disciplines as diverse as molecular biology, systematics, ecology, and evolutionary biology. Moreover, it is done in a stunning variety of different ways, using anything from formal analytical models to computer sim- ulations, from graphic representations to verbal arguments. In this essay I survey a number of aspects of what it means to do theoretical biology, and how they compare with the allegedly much more restricted sense of theory in the physical sciences. I also tackle a recent trend toward the presentation of all-encompassing theories in the biological sciences, from general theories of ecology to a recent attempt to provide a conceptual framework for the entire set of biological disciplines. Finally, I discuss the roles played by philosophers of science in criticizing and shap- ing biological theorizing.

In the six decades since the publication of Julian Huxley's Evolution: The Modern Synthesis, spectacular empirical advances in the biological sciences have been accompanied by equally significant developments within the core theoretical framework of the discipline. As a result, evolutionary theory today includes concepts and even entire new fields that were not part of the foundational structure of the Modern Synthesis. In this volume, sixteen leading evolutionary biologists and philosophers of science survey the conceptual changes that have emerged since Huxley's landmark publication, not only in such traditional domains of evolutionary biology as quantitative genetics and paleontology but also in such new fields of research as genomics and EvoDevo. Most of the contributors to Evolution—The Extended Synthesis accept many of the tenets of the classical framework but want to relax some of its assumptions and introduce significant conceptual augmentations of the basic Modern Synthesis structure—just as the architects of the Modern Synthesis themselves expanded and modulated previous versions of Darwinism. This continuing revision of a theoretical edifice the foundations of which were laid in the middle of the nineteenth century—the reexamination of old ideas, proposals of new ones, and the synthesis of the most suitable—shows us how science works, and how scientists have painstakingly built a solid set of explanations for what Darwin called the "grandeur" of life.

What makes beliefs thrive? In this paper, we model the dissemination of bona fide science versus pseudoscience, making use of Dan Sperber's epidemiological model of representations. Drawing on cognitive research on the roots of irrational beliefs and the institutional arrangement of science, we explain the dissemination of beliefs in terms of their salience to human cognition and their ability to adapt to specific cultural ecologies. By contrasting the cultural development of science and pseudoscience along a number of dimensions, we gain a better understanding of their underlying epistemic differences. Pseudoscience can achieve widespread acceptance by tapping into evolved cognitive mechanisms, thus sacrificing intellectual integrity for intuitive appeal. Science, by contrast, defies those deeply held intuitions precisely because it is institutionally arranged to track objective patterns in the world, and the world does not care much about our intuitions. In light of these differences, we discuss the degree of openness or resilience to conceptual change (evidence and reason), and the divergent ways in which science and pseudoscience can achieve cultural “success”.