Massimo Pigliucci

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engineering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms.

Questions traditionally answered by philosophers are now being tackled by prominent scientists. As the cultural influence of science and technology continues to grow, what room, if any, is left for philosophy? Three philosophers—Dr. Jonathan Kaplan, Dr. Massimo Pigliucci, and Dr. Leonard Finkelman —explore issues related to the philosophy of science, including how philosophy has contributed to scientific progress, why philosophy continues to be important to science, and why there remain questions that only philosophy can answer. The panelists represent four generations of an academic lineage: Dr. Kaplan was Dr. Pigliucci's dissertation advisor, Dr. Pigliucci was Dr. Finkelman's dissertation advisor, and Evan Tracy is a student of Dr. Finkelman.

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

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.

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

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.”

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.

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.