This paper explores a new reason for preferring a model-theoretic approach to understanding the nature of scientific theories. Identifying the models in philosophers' model-theoretic accounts of theories with the concepts in cognitive scientists' accounts of categorization suggests a structure to families of models far richer than has commonly been assumed. Using classical mechanics as an example, it is argued that families of models may be “mapped” as an array with “horizontal” graded structures, multiply hierarchical “vertical” structures, and local “radial” structures. These structures promise important implications for how scientific theories are learned and used in actual scientific practice.

There has been little serious work to integrate the constructionist approach and the cognitive/evolutionary approach in the domain of race, although many researchers have paid lip service to this project. We believe that any satisfactory account of human beings’ racialist cognition has to integrate both approaches. In this paper, we propose to move toward this integration. We present an evolutionary hypothesis that rests on a distinction between three kinds of groups—kin-based groups, small scale coalitions, and ethnies. Following Gil-White (1999, 2001a, 2001b), we propose that ethnies have raised specific evolutionary challenges that were solved by an evolved cognitive system. We suggest that the concept of race is a byproduct of this mechanism. We argue that recent theories of cultural transmission are our best hope for integrating social constructionists’ and cognitive/evolutionary theorists’ insights.

The existing literature treats of several investigations with a certain bearing on the question which is roughly indicated by the title “Imperatives and Logic.” Some of those investigations, however, are entirely outside the scope of the present work.

Mally sets himself the task of developing a “Logik des Willens” constituting a parallel to the usual logic, the “Logik des Denkens". In order to emphasize its independence, the author also calls this “Logik des Willens” “Deontik”, and he conceives it as being based on the “Wesensgesetze” of will in the same way as the usual logic is conceived as being based on the “Wesensgesetze” of thought. The author develops a formalistic deductive system on the basis of 5 axioms of demands (Grundsatze des Bollen), including the axiom that there is at least one fact which is unconditionally demanded, and through a lengthy series of theorems he arrives at the sentence that what is unconditionally demanded is identical with what is actually the case: “was sein soll, ist Tatsache”. Similarly, the author develops a system of axioms for “right” volition, including the axiom that right volition is non-contradictory.

The interpretation of functional imaging experiments is complicated by the pluripotency of brain regions. As there is a many-to-one mapping between cognitive functions and their neural substrates, region-based analyses of imaging data provide only weak support for cognitive theories. Price and Friston argue that we need a ‘cognitive ontology’ that abstractly categorizes the function of regions. I argue that abstract characterizations are unlikely to be cognitively interesting. I argue instead that we should attribute functions to regions in a context-sensitive manner. I review recent meta-analyses that approach fMRI data in this light and argue that they have revisionary potential.

This article examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon, “Epistemic Landscapes and the Division of Cognitive Labor” (this journal, 2009), that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments.

Biographical studies have shown that visual mental imagery plays a significant role in the conduct of scientific research, particularly in the generation of hypotheses. But the nature of visual mental imagery and its participation in abductive inference is not systematically understood. This paper discusses examples of visual abductive reasoning by archaeologists, analyzing them according to the visual information and the process of inference employed. This work supports the conclusion that visual abduction is useful to scientists under certain conditions and that it is amenable to detailed study.

Reichenbach's well-known distinction between the context of discovery and the context of justification has recently come under attack from several quarters. In this paper I attempt to reconsider the distinction and evaluate various recent criticisms of it. These criticisms fall into two main groups: those which directly challenge Reichenbach's distinction; and those which (I argue) indirectly but no less seriously challenge that distinction by rejecting the related distinction between psychology and epistemology, and defending the “naturalizing” of epistemology. I argue that these recent criticisms fail, and that the distinction remains an important conceptual tool necessary for an adequate understanding of the way in which scientific claims purport to appropriately portray our natural environment.

Contrary to the claim that measurement standards are absolutely accurate by definition, I argue that unit definitions do not completely fix the referents of unit terms. Instead, idealized models play a crucial semantic role in coordinating the theoretical definition of a unit with its multiple concrete realizations. The accuracy of realizations is evaluated by comparing them to each other in light of their respective models. The epistemic credentials of this method are examined and illustrated through an analysis of the contemporary standardization of time. I distinguish among five senses of “measurement accuracy” and clarify how idealizations enable the assessment of accuracy in each sense.

Inductive methods can be used to estimate the accuracies of inductive methods. Call a method immodest if it estimates that it is at least as accurate as any of its rivals. It would be unreasonable to adopt any but an immodest method. Under certain assumptions, exactly one of Carnap's lambda-methods is immodest. This may seem to solve the problem of choosing among the lambda-methods; but sometimes the immodest lambda-method is λ = 0, which it would not be reasonable to adopt. We should therefore reconsider the assumptions that led to this conclusion: for instance, the measure of accuracy.

The concept of action-research has been developed during the last decade, mainly at the Research Center for Group Dynamics, University of Michigan, Ann Arbor and at the Commission for Community Interrelations of the American Jewish Congress—centers founded by the late Kurt Lewin whose original and creative mind has made many contributions to social-psychological and sociological research. I owe my acquaintance with this new approach to the Research Center, particularly to Ronald Lippitt and Alvin Zander. Yet most of the following observations are based on my research experience during the last four years. The responsibility for what I am saying is, therefore, completely my own.

Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom-up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding.

Scientific experts have confirmed that anthropogenic warming is underway, and some degree of adaptation is now unavoidable. However, the details of impacts on the scale of climate change at which humans would have to prepare for and adjust to them are still the subject of considerable research, inquiry, and debate. Planning for adaptation requires information on the scale over which human organizations and institutions have authority and capacity, yet the general circulation models lack forecasting skill at these scales, and attempts to “downscale” climate models are still in the early stages of development. Because we do not know what adaptations will be required, we cannot say whether they will be harder or easier—more expensive or less—than emissions control. Whatever improvements in regional predictive capacity may come about in the future, the lack of current predictive capacity on the relevant scale is a strong argument for why we must both control greenhouse gas emissions and prepare to adapt.

Through an examination of the actual research strategies and assumptions underlying the Human Genome Project (HGP), it is argued that the epistemic basis of the initial model organism programs is not best understood as reasoning via causal analog models (CAMs). In order to answer a series of questions about what is being modeled and what claims about the models are warranted, a descriptive epistemological method is employed that uses historical techniques to develop detailed accounts which, in turn, help to reveal forms of reasoning that are explicit, or more often implicit, in the practice of a particular field of scientific study. It is suggested that a more valid characterization of the reasoning structure at work here is a form of case-based reasoning. This conceptualization of the role of model organisms can guide our understanding and assessment of these research programs, their knowledge claims and progress, and their limitations, as well as how we educate the public about this type of biomedical research.

The key problem in the controversy over group selection is that of defining a criterion of group selection that identifies a distinct causal process that is irreducible to the causal process of individual selection. We aim to clarify this problem and to formulate an adequate model of irreducible group selection. We distinguish two types of group selection models, labeling them type I and type II models. Type I models are invoked to explain differences among groups in their respective rates of production of contained individuals. Type II models are invoked to explain differences among groups in their respective rates of production of distinct new groups. Taking Elliott Sober's model as an exemplar, we argue that although type I models have some biological importance—they force biologists to consider the role of group properties in influencing the fitness of organisms—they fail to identify a distinct group-level causal selection process. Type II models if properly framed, however, do identify a group-level causal selection process that is not reducible to individual selection. We propose such a type II model and apply it to some of the major candidates for group selection.

This paper argues for the importance of approaching medicine, as a theoretical science, through values. The normative concepts of benefit and harm are held to provide a framework for the analysis of medicine which reflects the obligations of the doctor-patient relationship, suffices to define the key concept of medical relevance, yields a general necessary condition for the basic concepts of medicine, explains the role of such nonnormative conceptions as discomfort, dysfunction, and incapacity, and avoids the mistakes of other normative approaches which hold that unhealthy conditions are disvaluable or should be treated. Neutralist analyses are criticized, especially those approaching health through proper functioning.

Some scientists are happy to follow in the footsteps of others; some like to explore novel approaches. It is tempting to think that herein lies an epistemic division of labor conducive to overall scientific progress: the latter point the way to fruitful areas of research, and the former more fully explore those areas. Weisberg and Muldoon’s model, however, suggests that it would be best if all scientists explored novel approaches. I argue that this is due to implausible modeling choices, and I present an alternative ‘epistemic landscape’ model that demonstrates the alleged benefits from division of labor, with one restriction.

Evolutionary psychology and social constructionism are widely regarded as fundamentally irreconcilable approaches to the social sciences. Focusing on the study of the emotions, we argue that this appearance is mistaken. Much of what appears to be an empirical disagreement between evolutionary psychologists and social constructionists over the universality or locality of emotional phenomena is actually generated by an implicit philosophical dispute resulting from the adoption of different theories of meaning and reference. We argue that once this philosophical dispute is recognized, it can be set aside. When this is done, it becomes clear that the two approaches to the emotions complement, rather than compete with, one another.

My paper draws on examples from molecular biology, the details of which I have developed elsewhere (Rheinberger 1992, 1993, 1995, 1997). Here, I can give only a brief outline of my argument. Reduction of complexity is a prerequisite for experimental research. To make sense of the universe of living beings, the modern biologist is bound to divide his world into fragments in which parameters can be defined, quantities measured, qualities identified. Such is the nature of any “experimental system.” Ontic complexity has to be reduced in order to make experimental research possible. The complexity of the research object, however, is epistemically retained in the rich context of an experimental landscape, where the eruption of “volcanic systems” can change the scenery dramatically as the result of particular, unprecedented findings.

This paper examines the way in which causal relations are understood in the dominant model in contemporary medicine. It argues that the causal relation is not definable in terms of the condition relation, but that in general for conditions of an occurrence to be among its causes they must answer instrumental interests in a certain way, and there are further criteria for distinguishing ‘the’ cause of a disease (i.e., its etiological agent) from other causal factors, which are based upon instrumental interests peculiar to medicine. It also argues that diseases are complex processes of which both clinical and underlying patho-physiological manifestations are proper parts (as contrasted with effects).