Massimo Pigliucci

Recent debates between proponents of the modern evolutionary synthesis (the standard model in evolutionary biology) and those of a possible extended synthesis are a good example of the fascinating tangle among empirical, theoretical, and conceptual or philosophical matters that is the practice of evolutionary biology. In this essay, we briefly discuss two case studies from this debate, highlighting the relevance of philosophical thinking to evolutionary biologists in the hope of spurring further constructive cross-pollination between the two fields.

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.

Philosophical inquiries into morality are as old as philosophy, but it may turn out that morality itself is much, much older than that. At least, that is the main thesis of prima- tologist Frans De Waal, who in this short book based on his Tanner Lectures at Princeton, elaborates on what biologists have been hinting at since Darwin’s (1871) book The Descent of Man and Hamilton’s (1963) studies on the evolution of altruism: morality is yet another allegedly human characteristic that turns out to be built over evolutionary time by natural

Discussions about the biological bases 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 philosophical unpacking of his conceptual analysis, drawing on a dual professional background as an evolutionary biologist and a philosopher of science.

Ever since Socrates, philosophers have been in the business of asking ques- tions of the type “What is X?” The point has not always been to actually find out what X is, but rather to explore how we think about X, to bring up to the surface wrong ways of thinking about it, and hopefully in the process to achieve an increasingly better understanding of the matter at hand. In the early part of the twentieth century one of the most ambitious philosophers of sci- ence, Karl Popper, asked that very question in the specific case in which X = science. Popper termed this the “demarcation problem,” the quest for what distinguishes science from nonscience and pseudoscience (and, presumably, also the latter two from each other).

Twenty years have passed since Gould and Lewontin published their critique of ‘the adaptationist program’ – the tendency of some evolutionary biologists to assume, rather than demonstrate, the operation of natural selection. After the ‘Spandrels paper’, evolutionists were more careful about producing just-so stories based on selection, and paid more attention to a panoply of other processes. Then came reactions against the excesses of the anti-adaptationist movement, which ranged from a complete dismissal of Gould and Lewontin’s contribution to a positive call to overcome the problems. We now have an excellent opportunity for finally affirming a more balanced and pluralistic approach to the study of evolutionary biology.

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