The first U.S. nominee for the Nobel Prize, Jacques Loeb was trained in experimental physiology in Germany, joined the biology faculty of the new University of Chicago in 1892, later taught at the University of California at Berkeley and then moved to the Rockefeller Institute. Loeb's career provides the vehicle, in this book, for an examination of the foundations of biotechnology.

Models are of central importance in many scientific contexts. The centrality of models such as inflationary models in cosmology, general-circulation models of the global climate, the double-helix model of DNA, evolutionary models in biology, agent-based models in the social sciences, and general-equilibrium models of markets in their respective domains is a case in point (the Other Internet Resources section at the end of this entry contains links to online resources that discuss these models). Scientists spend significant amounts of time building, (...) testing, comparing, and revising models, and much journal space is dedicated to interpreting and discussing the implications of models. In short, models are one of the principal instruments of modern science. (shrink)

Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss (...) what consequences those tradeoffs have for theoretical practice. (shrink)

Theorizing in ecology and evolution often proceeds via the construction of multiple idealized models. To determine whether a theoretical result actually depends on core features of the models and is not an artifact of simplifying assumptions, theorists have developed the technique of robustness analysis, the examination of multiple models looking for common predictions. A striking example of robustness analysis in ecology is the discovery of the Volterra Principle, which describes the effect of general biocides in predator-prey systems. This paper details (...) the discovery of the Volterra Principle and the demonstration of its robustness. It considers the classical ecology literature on robustness and introduces two individual-based models of predation, which are used to further analyze the Volterra Principle. The paper also introduces a distinction between parameter robustness, structural robustness, and representational robustness, and demonstrates that the Volterra Principle exhibits all three kinds of robustness. *Received September 2006; revised May 2007. ‡Earlier versions of this paper were presented at the Australasian Association of Philosophy, the London School of Economics, and the University of Bristol. The authors wish to thank those audiences as well as Patrick Forber, Ken Waters, Deena Skolnick Weisberg, Uri Wilensky, and Bill Wimsatt for many helpful comments. Special thanks to Giacomo Sillari for his assistance in translating Volterra's original paper and his insightful thoughts about Volterra's aims and methods. Some of the research in this paper was supported by NSF grant SES-0620887 to MW. †To contact the authors, please write to: Michael Weisberg, Department of Philosophy, University of Pennsylvania, 433 Logan Hall, Philadelphia, PA 19104; e-mail: [email protected]; Kenneth Reisman, Pluribo, Inc., 100 Park Avenue, Suite 1600, New York, NY 10017; e-mail: [email protected]. (shrink)

Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.

This paper is an interpretation and defense of Richard Levins’ “The Strategy of Model Building in Population Biology,” which has been extremely influential among biologists since its publication 40 years ago. In this article, Levins confronted some of the deepest philosophical issues surrounding modeling and theory construction. By way of interpretation, I discuss each of Levins’ major philosophical themes: the problem of complexity, the brute-force approach, the existence and consequence of tradeoffs, and robustness analysis. I argue that Levins’ article is (...) concerned, at its core, with justifying the use of multiple, idealized models in population biology. (shrink)

In 1966, Richard Levins argued that there are different strategies in model building in population biology. In this paper, I reply to Orzack and Sober’s (1993) critiques of Levins, and argue that his views on modeling strategies apply also in the context of evolutionary genetics. In particular, I argue that there are different ways in which models are used to ask and answer questions about the dynamics of evolutionary change, prospectively and retrospectively, in classical versus molecular evolutionary genetics. Further, I (...) argue that robustness analysis is a tool for, if not confirmation, then something near enough, in this discipline. (shrink)

Ever since the early decades of this century, there have emerged a number of competing schools of ecology that have attempted to weave the concepts underlying natural resource management and natural-historical traditions into a formal theoretical framework. It was widely believed that the discovery of the fundamental mechanisms underlying ecological phenomena would allow ecologists to articulate mathematically rigorous statements whose validity was not predicated on contingent factors. The formulation of such statements would elevate ecology to the standing of a rigorous (...) scientific discipline on a par with physics. However, there was no agreement as to the fundamental units of ecology. Systems ecologists sought to identify the fundamental organization that tied the physical and biological components of ecosystems into an irreducible unit: the ecosystem was their fundamental unit. Population ecologists sought, instead, to identify the biological mechanisms regulating the abundance and distribution of plant and animal species: to these ecologists, the individual organism was the fundamental unit of ecology, and the physical environment was nothing more than a stage upon which the play of individuals in perennial competition took place. As Joel Hagen has pointed out, the two schools were thus dividied by fundamentally different and irreconcilable assumptions about the nature of ecosystems.Notwithstanding these divisive efforts to elevate the image of ecology, the discipline remained in the shadows of American academia until the mid-1960s, when systems ecologists succeeded in projecting ecology onto the national scene. They did so by seeking closer involvement with practical problems: they argued before Congress that their approach to the theoretical problems of ecology was uniquely suited to the solution of the impending “environmental crisis.” With the establishment of the International Biological Program, they succeeded in attracting unprecedented levels of funding for systems ecology research. Theoretical population ecologists, on the other hand, found themselves consigned to the outer regions of this new institutional landscape. The systems ecologists' successful capture of the limelight and the purse brought the divisions between them and population ecologists into sharper relief — hence the hardening of the division of ecology observed by Hagen.45I have argued that the population biologist Richard Levins, prompted by these institutional developments, sought to challenge the social position of systems ecology, and to assert the intellectual priority of theoretical population ecology. He attempted to do so by articulating a nontrivial and rather carefully thought out classification of ecological models that led to the disqualification of systems analysis as a legitimate approach to the study of ecological phenomena. I have suggested that — ultimately —Levins's case against systems analysis in ecology rested on the view that an aspiration to realism and prediction was incompatible with an interest in theoretical issues, a concern that he equated with the search for generality. He sought to reinforce this argument by exploiting the fact that systems ecologists had staked their future on the provision of technical solutions to the problems of the “environmental crisis”: he associated systems ecologists' aspiration to realism and precision with a concern for practical issues, trading on the widely accepted view that practical imperatives are incompatible with the aims of scientific inquiry.46 These are plausible, but nonetheless questionable, claims which have now become an integral part of ecological knowledge. And finally, I hope to have shown how even the most abstract levels of scientific argument are shaped by political considerations, and how discussions of the conceptual development of modern ecology might benefit from a greater consideration of its historical and social dimensions.47. (shrink)

The Formal Darwinism Project is an attempt to use mathematical theory to prove the claim that fitness maximization is the outcome of evolution in nature. Grafen’s (2014, p. 12) conclusion from this project is that “….there is a very general expectation of something close to fitness maximisation, which will convert into fitness-maximisation unless there are particular kinds of circumstances—and further, that fitness is the same quantity for all genetic architectures.” Grafen’s claim appears to mean to him that natural populations are (...) expected to contain individuals whose traits are optimal, i.e., any given trait outperforms all reasonable alternatives. I describe why Grafen’s attempt can never provide a meaningful expectation as to the ubiquity of optimal traits in nature. This is so because it is based upon a misconception of the relationship between theory and empirical analysis. Even if one could use theory in the way Grafen proposes, I describe how his theory is causally incomplete. Finally, I describe how Grafen’s conceptual framework is ambiguous. The Formal Darwinism Project has been inspired by “On The Origin of Species” by Darwin. The great lesson of this book was Darwin’s demonstration of the necessary dialog between theory and data, with each influencing and being influenced by the other. Grafen’s Formal Darwinism Project, an attempt to create understanding of nature by removing data from this dialog, reflects a failure to understand Darwin’s great lesson. (shrink)

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has (...) been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation. (shrink)

Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice. The practice has also been severely criticized by Steven Orzack and Elliott Sober, who claim that it is a nonempirical form of confirmation, effective only under unusual circumstances. This paper addresses Orzack and Sober's criticisms by giving a new account of robustness analysis and (...) showing how the practice can identify robust theorems. Once the structure of robust theorems is clearly articulated, it can be shown that such theorems have a degree of confirmation, despite the lack of direct empirical evidence for their truth. (shrink)

Robustness concepts are often invoked to manage two obstacles confronting models of ecological systems: complexity and uncertainty. The intuitive idea is that any result derived from many idealized but credible models is thereby made more reliable or is better confirmed. An appropriate basis for this inference has proven elusive. Here, several representations of robustness analysis are vetted, paying particular attention to complex models of ecosystems and the global climate. The claim that robustness is itself confirmatory because robustness analysis employs a (...) Bayesian variety-of-evidence argument is criticized, but recent overwhelming pessimism about robustness may have a silver lining. (shrink)

Published research findings are sometimes refuted by subsequent evidence, says Ioannidis, with ensuing confusion and disappointment.

Charles Darwin's famous 1882 letter, in response to a gift by his friend, William Ogle of Ogle's recent translation of Aristotle's "Parts of Animals," in which Darwin remarks that his "two gods," Linnaeus and Cuvier, were "mere school-boys to old Aristotle," has been though to be only an extravagantly worded gesture of politeness. However, a close examination of this and other Darwin letters, and of references to Aristotle in Darwin's earlier work, shows that the famous letter was written several weeks (...) after a first, polite letter of thanks, and was carefully formulated and literally meant. Indeed, it reflected an authentic, and substantial, increase in Darwin's already high respect for Aristotle, as a result of a careful reading both of Ogle's Introduction and of more or less the portion of Ogle's translation which Darwin says he has read. Aristotle's promotion to the pantheon, as an examination of the basis for Darwin's admiration of Linnaeus and Cuvier suggests, was most likely the result specifically of Darwin's late discovery that the man he already knew as "one of the greatest... observers that ever lived" was also the ancient equivalent both of the great modern systematist and of the great modern advocate of comparative functional explanation. It may also have reflected some real insight on Darwin's part into the teleological aspect of Aristotle's thought, indeed more insight than Ogle himself had achieved, as a portion of their correspondence reveals. (shrink)

The multiple-models approach, which has its origins in Levins’s work, is gaining broader acceptance among philosophers. Levins asserted that there is a trade-off between modeling desiderata, which justified the multiple-models approach through two separate justificatory paths. Some attention has been paid to the trade-off thesis, culminating in a paper by Matthewson and Weisberg. However, no attention has been paid to how the trade-off is supposed to justify the multiple-models approach. I argue that a trade-off between generality and precision cannot support (...) one of Levins’s justificatory paths, and I consider what that might mean for the multiple-models approach. (shrink)

This textbook is aimed at newcomers to nonlinear dynamics and chaos, especially students taking a first course in the subject. The presentation stresses analytical methods, concrete examples and geometric intuition. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, and strange attractors.A unique feature of the book is its emphasis on applications. These include (...) mechanical vibrations, lasers, biological rhythms, superconducting circuits, insect outbreaks, chemical oscillators, genetic control systems, chaotic waterwheels, and even a technique for using chaos to send secret messages. In each case, the scientific background is explained at an elementary level and closely integrated with the mathematical theory.Richly illustrated, and with many exercises and worked examples, this book is ideal for an introductory course at the junior/senior or first-year graduate level. It is also ideal for the scientist who has not had formal instruction in nonlinear dynamics, but who now desires to begin informal study. The prerequisites are multivariable calculus and introductory physics. (shrink)

T. H. Morgan, A. H. Sturtevant, H. J. Muller and C. B. Bridges published their comprehensive treatise "The Mechanism of Mendelian Heredity" in 1915. By 1920 Morgan 's "Chromosome Theory of Heredity" was generally accepted by geneticists in the United States, and by British geneticists by 1925. By 1930 it had been incorporated into most general biology, botany, and zoology textbooks as established knowledge. In this paper, I examine the reasons why it was accepted as part of a series of (...) comparative studies of theory-acceptance in the sciences. In this context it is of interest to look at the persuasiveness of confirmed novel predictions, a factor often regarded by philosophers of science as the most important way to justify a theory. Here it turns out to play a role in the decision of some geneticists to accept the theory, but is generally less important than the CTH's ability to explain Mendelian inheritance, sex-linked inheritance, non-disjunction, and the connection between linkage groups and the number of chromosome pairs; in other words, to establish a firm connection between genetics and cytology. It is remarkable that geneticists were willing to accept the CTH as applicable to all organisms at a time when it had been confirmed only for Drosophila. The construction of maps showing the location on the chromosomes of genes for specific characters was especially convincing for non-geneticists. (shrink)

It's been 41 years since the publication of Ernst Mayr's Cause and Effect in Biology wherein Mayr most clearly develops his version of the influential distinction between ultimate and proximate causes in biology. In critically assessing Mayr's essay I uncover false statements and red-herrings about biological explanation. Nevertheless, I argue to uphold an analogue of the ultimate/proximate distinction as it refers to two different kinds of explanations, one dynamical the other statistical.

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections (...) and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence and systems from The Cognitive Science Society. (shrink)

one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...

This volume draws together Allan Gotthelf's pioneering work on Aristotle's biology. He examines Aristotle's natural teleology, the axiomatic structure of biological explanation, and the reliance on scientifically organized data in the three great works with which Aristotle laid the foundations of biological science.