There has been a significant amount of uncertainty and controversy over the prospects for general knowledge in ecology. Environmental decision makers have begun to despair of ecology's capacity to provide anything more than case by case guidance for the shaping of environmental policy. Ecologists themselves have become suspicious of the pursuit of the kind of genuine nomothetic knowledge that appears to be the hallmark of other scientific domains. Finally, philosophers of biology have contributed to this retreat from generality by suggesting (...) that there really are no laws in biology. This paper addresses these issues by providing a framework for thinking about general knowledge claims in ecology. It introduces a philosophical taxonomy that classifies generalizations into three broad categories – phenomenological, causal and theoretical. It then turns to the difficult problem of laws, arguing that, while there are probably no laws as that term has been understood in philosophy of science, it doesn't follow that everything in ecology is equally contingent. A mechanism for recognizing degrees of contingency in ecological generalizations is developed. The paper concludes by examining the implications of the analysis for the controversies noted at the outset. (shrink)

Beatty, Brandon, and Sober agree that biological generalizations, when contingent, do not qualify as laws. Their conclusion follows from a normative definition of law inherited from the Logical Empiricists. I suggest two additional approaches: paradigmatic and pragmatic. Only the pragmatic represents varying kinds and degrees of contingency and exposes the multiple relationships found among scientific generalizations. It emphasizes the function of laws in grounding expectation and promotes the evaluation of generalizations along continua of ontological and representational parameters. Stability of conditions (...) and strength of determination in nature govern projectibility. Accuracy, ontological level, simplicity, and manageability provide additional measures of usefulness. (shrink)

The official model of explanation proposed by the logical empiricists, the covering law model, is subject to familiar objections. The goal of the present paper is to explore an unofficial view of explanation which logical empiricists have sometimes suggested, the view of explanation as unification. I try to show that this view can be developed so as to provide insight into major episodes in the history of science, and that it can overcome some of the most serious difficulties besetting the (...) covering law model. (shrink)

To explain the phenomena in the world of our experience, to answer the question “why?” rather than only the question “what?”, is one of the foremost objectives of all rational inquiry; and especially, scientific research in its various branches strives to go beyond a mere description of its subject matter by providing an explanation of the phenomena it investigates. While there is rather general agreement about this chief objective of science, there exists considerable difference of opinion as to the function (...) and the essential characteristics of scientific explanation. In the present essay, an attempt will be made to shed some light on these issues by means of an elementary survey of the basic pattern of scientific explanation and a subsequent more rigorous analysis of the concept of law and of the logical structure of explanatory arguments. (shrink)

Experimental “localism” stresses the importance of context‐specific knowledge, and the limitations of universal theories in science. I illustrate Latour's radical approach to localism and show that it has some unpalatable consequences, in particular the suggestion that problems of external validity (or how to generalize experimental results to nonlaboratory circumstances) cannot be solved. In the last part of the paper I try to sketch a solution to the problem of external validity by extending Mayo's error‐probabilistic approach.

In his 1966 paper “The Strategy of model-building in Population Biology”, Richard Levins argues that no single model in population biology can be maximally realistic, precise and general at the same time. This is because these desirable model properties trade-off against one another. Recently, philosophers have developed Levins’ claims, arguing that trade-offs between these desiderata are generated by practical limitations on scientists, or due to formal aspects of models and how they represent the world. However this project is not complete. (...) The trade-offs discussed by Levins had a noticeable effect on modelling in population biology, but not on other sciences. This raises questions regarding why such a difference holds. I claim that in order to explain this finding, we must pay due attention to the properties of the systems, or targets modelled by the different branches of science.Keywords: Trade-offs; Population biology; Scientific models; Generality; Precision. (shrink)

Invasion biology is a relatively young discipline which is important, interesting and currently in turmoil. Biological invaders can threaten native ecosystems and global biodiversity; they can incur massive economic costs and even introduce diseases. Invasion biologists generally agree that being able to predict when and where an invasion will occur is essential for progress in their field. However, successful predictions of this type remain elusive. This has caused a rift, as some researchers are pessimistic and believe that invasion biology has (...) no future, whereas others are more optimistic and believe that the key to successful prediction is the creation of a general, unified theoretical framework which encompasses all invasion events. Although I agree that there is a future for invasion biology, extensive synthesis is not the way to better predictions. I argue that the causes of invasion phenomena are exceedingly complex and heterogeneous, hence it is impossible to make generalizations over particular events without sacrificing causal detail. However, this causal detail is just what is needed for the specific predictions which the scientists wish to produce. Instead, I show that a limited type of synthesis is a more useful tool for generating successful predictions. An important implication of my view is that it points to a more pluralistic approach to invasion biology, where generalization and prediction are treated as important yet distinct research goals. (shrink)

Critiques of case studies as an epistemic genre usually focus on the domain of justification and hinge on comparisons with statistics and laboratory experiments. In this domain, case studies can be defended by the notion of “infirming”: they use many different bits of evidence, each of which may independently “infirm” the account. Yet their efficacy may be more powerful in the domain of discovery, in which these same different bits of evi- dence must be fully integrated to create an explanatory (...) account with internal validity. (shrink)

Ecologist Richard Levins argues population biologists must trade‐off the generality, realism, and precision of their models since biological systems are complex and our limitations are severe. Steven Orzack and Elliott Sober argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins's thesis that there is a necessary trade‐off between generality, precision, and realism in mathematical models in biology is false. I argue that Orzack and Sober's arguments fail since (...) Levins's thesis concerns the pragmatic features of model building not just the formal properties of models. (shrink)

Ecologist Richard Levins argues population biologists must trade-off the generality, realism, and precision of their models since biological systems are complex and our limitations are severe. Steven Orzack and Elliott Sober argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins's thesis that there is a necessary trade-off between generality, precision, and realism in mathematical models in biology is false. I argue that Orzack and Sober's arguments fail since (...) Levins's thesis concerns the pragmatic features of model building not just the formal properties of models. (shrink)