Michael Weisberg

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]

Much of biological and economic theorizing takes place by modeling, the indirect study of real-world phenomena by the construction and examination of models. Books and articles about biological and economic theory are often books and articles about models, many of which are highly idealized and chosen for their explanatory power and analytical convenience rather than for their fit with known data sets. Philosophers of science have recognized these facts and have developed literatures about the nature of models, modeling, idealization, as well as testing of models and explanation by models, for both biology and economics. The impetus for this special issue came from our recognition that there is remarkably little overlap between the “modeling in biology” and “modeling in economics” literatures, despite many of the same themes appearing in these literatures.

Evolutionary biology – or, more precisely, two (purported) applications of Darwin's theory of evolution by natural selection, namely, evolutionary psychology and what has been called human behavioral biology – is on the cusp of becoming the new rage among legal scholars looking for interdisciplinary insights into the law. We argue that as the actual science stands today, evolutionary biology offers nothing to help with questions about legal regulation of behavior. Only systematic misrepresentations or lack of understanding of the relevant biology, together with far-reaching analytical and philosophical confusions, have led anyone to think otherwise. Evolutionary accounts are etiological accounts of how a trait evolved. We argue that an account of causal etiology could be relevant to law if (1) the account of causal etiology is scientifically well-confirmed, and (2) there is an explanation of how the well-confirmed etiology bears on questions of development (what we call the Environmental Gap Objection). We then show that the accounts of causal etiology that might be relevant are not remotely well-confirmed by scientific standards. We argue, in particular, that (a) evolutionary psychology is not entitled to assume selectionist accounts of human behaviors, (b) the assumptions necessary for the selectionist accounts to be true are not warranted by standard criteria for theory choice, and (c) only confusions about levels of explanation of human behavior create the appearance that understanding the biology of behavior is important. We also note that no response to the Environmental Gap Objection has been proffered. In the concluding section of the article, we turn directly to the work of Owen Jones, a leading proponent of the relevance of evolutionary biology to law, and show that he does not come to terms with any of the fundamental problems identified in this article

In defending semantic externalism, philosophers of language have often assumed that there is a straightforward connection between scientific kinds and the natural kinds recognized by ordinary language users.1 For example, the claim that water is H2O assumes that the ordinary language kind water corresponds to a chemical kind, which contains all the molecules with molecular formula H2O as its members. This assumption about the coordination between ordinary language kinds and scientific kinds is important for the externalist program, because it is what allows us to discover empirically the extensions of ordinary language kind terms.

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

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

Aristotle’s On generation and corruption raises a vital question: how is mixture, or what we would now call chemical combination, possible? It also offers an outline of a solution to the problem and a set of criteria that a successful solution must meet. Understanding Aristotle’s solution and developing a viable peripatetic theory of chemical combination has been a source of controversy over the last two millennia. We describe seven criteria a peripatetic theory of mixture must satisfy: uniformity, recoverability, potentiality, equilibrium, alteration, incompleteness, and the ability to distinguish mixture from generation, corruption, juxtaposition, augmentation, and alteration. After surveying the theories of Philoponus , Avicenna , Averroes , and John M. Cooper , we argue for the merits of Richard Rufus of Cornwall’s theory. Rufus was a little known scholastic philosopher who became a Franciscan theologian in 1238, after teaching Aristotelian natural philosophy as a secular master in Paris. Lecturing on Aristotle’s De generatione et corruptione, around the year 1235, he offered his students a solution to the problem of mixture that we believe satisfies Aristotle’s seven criteria.Author Keywords: Mixture; Mixt; Chemical combination; Accidental potential; Potential; Elements; Medieval chemistry; Peripatetic chemistry; Aristotelian science; Richardus Rufus; Averroes ; Avicenna ; Philoponus ; John M. Cooper

Contemporary literature in philosophy of science has begun to emphasize the practice of modeling, which differs in important respects from other forms of representation and analysis central to standard philosophical accounts. This literature has stressed the constructed nature of models, their autonomy, and the utility of their high degrees of idealization. What this new literature about modeling lacks, however, is a comprehensive account of the models that figure in to the practice of modeling. This paper offers a new account of both concrete and mathematical models, with special emphasis on the intentions of theorists, which are necessary for evaluating the model-world relationship during the practice of modeling. Although mathematical models form the basis of most of contemporary modeling, my discussion begins with more traditional, concrete models such as the San Francisco Bay model.

Simulation and Similarity: Using Models to Understand the World is an account of modeling in contemporary science. Modeling is a form of surrogate reasoning where target systems in the natural world are studied using models, which are similar to these targets. My book develops an account of the nature of models, the practice of modeling, and the similarity relation that holds between models and their targets. I also analyze the conceptual tools that allow theorists to identify the trustworthy aspects of models. Taken as a whole, I try to account for the ways that modeling is actually practiced by theorists, while abstracting sufficiently to understand the similarities and differences among examples of concrete, mathematical, and computational modeling.I am grateful to Wendy Parker, Jay Odenbaugh, and Bill Wimsatt for their careful and interesting reading of my book, as well as their constructive criticisms. Although I naturally disagree with some of their critiques, I have learned much ..