Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come (...) in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)

I define a concept of causal probability and apply it to questions about the role of probability in evolutionary processes. Causal probability is defined in terms of manipulation of patterns in empirical outcomes by manipulating properties that realize objective probabilities. The concept of causal probability allows us see how probabilities characterized by different interpretations of probability can share a similar causal character, and does so in such way as to allow new inferences about relationships between probabilities realized in different chance (...) setups. I clarify relations between probabilities and properties defined in terms of them, and argue that certain widespread uses of computer simulations in evolutionary biology show that many probabilities relevant to evolutionary outcomes are causal probabilities. This supports the claim that higher-level properties such as biological fitness and processes such as natural selection are causal properties and processes, contrary to what some authors have argued. (shrink)

Scientific discourse is rife with passages that appear to be ordinary descriptions of systems of interest in a particular discipline. Equally, the pages of textbooks and journals are filled with discussions of the properties and the behavior of those systems. Students of mechanics investigate at length the dynamical properties of a system consisting of two or three spinning spheres with homogenous mass distributions gravitationally interacting only with each other. Population biologists study the evolution of one species procreating at a constant (...) rate in an isolated ecosystem. And when studying the exchange of goods, economists consider a situation in which there are only two goods, two perfectly rational agents, no restrictions on available information, no transaction costs, no money, and dealings are done immediately. Their surface structure notwithstanding, no competent scientist would mistake descriptions of such systems as descriptions of an actual system: we know very well that there are no such systems. These descriptions are descriptions of a model-system, and scientists use model-systems to represent parts or aspects of the world they are interested in. Following common practice, I refer to those parts or aspects as target-systems. What are we to make of this? Is discourse about such models merely a picturesque and ultimately dispensable façon de parler? This was the view of some early twentieth century philosophers. Duhem (1906) famously guarded against confusing model building with scientific theorizing and argued that model building has no real place in science, beyond a minor heuristic role. The aim of science was, instead, to construct theories, with theories understood as classificatory or representative structures systematically presented and formulated in precise symbolic.. (shrink)

Since the beginning of the 20th century, philosophers of science have asked, "what kind of thing is a scientific theory?" The logical positivists answered: a scientific theory is a mathematical theory, plus an empirical interpretation of that theory. Moreover, they assumed that a mathematical theory is specified by a set of axioms in a formal language. Later 20th century philosophers questioned this account, arguing instead that a scientific theory need not include a mathematical component; or that the mathematical component need (...) not be specified by a set of axioms in a formal language. We survey various accounts of scientific theories entertained in the 20th century -- removing some misconceptions, and clearing a path for future research. (shrink)

The modern history of verisimilitude can be divided into three periods. The first began in 1960, when Karl Popper proposed his qualitative definition of what it is for one theory to be more truthlike than another theory, and lasted until 1974, when David Miller and Pavel Trichý published their refutation of Popper's definition. The second period started immediately with the attempt to explicate truthlikeness by means of relations of similarity or resemblance between states of affairs (or their linguistic representations); the (...) work within this similarity approach was summarized in the books of Graham Oddie [1986] and Ilkka Niiniluoto [1987]. During the subsequent third period, studies in verisimilitude have been actively continued, and interesting results and applications have been achieved, but not many dramatic novelties. While it is now obsolete to claim that truthlikeness with reasonable properties cannot be defined at all, there is still a lot of controversy about the best and least arbitrary approach to doing this. (shrink)

A review of the chemical education research literature suggests that the term constructivism is used in two ways: experience-based constructivism and discipline-based constructivism. These two perspectives are examined as an epistemology in relation to the teaching and learning of the concept of idealization in chemistry. It is claimed that experience-based constructivism is powerless to inform the origin of such concepts in chemistry and while discipline-based constructivism can admit such theoretical concepts as idealization it does not offer any unique perspectives that (...) cannot be obtained from other models. Chemical education researchers do not consistently appeal to constructivism as an epistemology or as a teaching/learning perspective and it is shown that, while it draws attention to worthwhile teaching/learning strategies, it cannot be considered as foundational to chemical education research and tends to be used more as an educational label than as an undergirding theory. (shrink)

A review of the chemical education research literature suggests that the term constructivism is used in two ways: experience-based constructivism and discipline-based constructivism. These two perspectives are examined as an epistemology in relation to the teaching and learning of the concept of idealization in chemistry. It is claimed that experience-based constructivism is powerless to inform the origin of such concepts in chemistry and while discipline-based constructivism can admit such theoretical concepts as idealization it does not offer any unique perspectives that (...) cannot be obtained from other models. Chemical education researchers do not consistently appeal to constructivism as an epistemology or as a teaching/learning perspective and it is shown that, while it draws attention to worthwhile teaching/learning strategies, it cannot be considered as foundational to chemical education research and tends to be used more as an educational label than as an undergirding theory. (shrink)

Among theory theorists, it is commonly thought that folk psychological theory is tacitly known. However, folk psychological knowledge has none of the central features of tacit knowledge. But if it is ordinary knowledge, why is it that we have difficulties expressing anything but a handful of folk psychological generalisations? The reason is that our knowledge is of theoretical models and hypotheses, not of universal generalisations. Adopting this alternative view of (scientific) theories, we come to see that, given time and reflection, (...) we can say what we know. (shrink)

You are asked to call out the letters on a chart during an eyeexamination: you see and then read out the letters ‘U’, ‘R’, and ‘X’. Commonsense says that your perceptual experiences causally control your calling out the letters. Or suppose you are playing a game of chess intent on winning: you plan your strategy and move your chess pieces accordingly. Again, commonsense says that your intentions and plans causally control your moving the chess pieces. These causal judgements are as (...) plain and evident as any can be. (shrink)

It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...) such relevant possibilities may affect one’s confidence in necessity or impossibility hypotheses. Thus, one can learn from minimal economic models. (shrink)

The 1994 US spectrum auction is now a paradigmatic case of the successful use of microeconomic theory for policy-making. We use a detailed analysis of it to review standard accounts in philosophy of science of how idealized models are connected to messy reality. We show that in order to understand what made the design of the spectrum auction successful, a new such account is required, and we present it here. Of especial interest is the light this sheds on the issue (...) of progress in economics. In particular, it enables us to get clear on exactly what has been progressing, and on exactly what theory has – and has not – contributed to that. This in turn has important implications for just what it is about economic theory that we should value. (shrink)

This paper provides a conceptual analysis of the notion of interests as it is used in the social studies of science. After describing the theoretical background behind the Strong Program's adoption of the concept of interest, the paper outlines a reconstruction of the everyday notion of interest and argues that this same notion is used also by the sociologists of scientific knowledge. However, there are a couple of important differences between the everyday use of this notion and the way in (...) which it used by the sociologists. The sociologists do not use the term in evaluative context and they do not regard interests as purely non-epistemic factors. Finally, it is argued that most of the usual critiques of interest explanations, by both philosophers and fellow sociologists, are misguided. (shrink)

Regarding the dichotomy between applied science and pure science, there are two apparently paradoxical facts. First, they are distinguishable. Second, the outcomes of pure sciences (e.g. scientific theories and models) are applicable to producing the outcomes of applied sciences (e.g. technological artefacts) and vice versa. Addressing the functional roles of applied and pure science, i.e. to produce design representation and science representation, respectively, I propose a new characterisation of the dichotomy that explains these two facts.

n this paper, I examine the charge that Gopnik and Meltzoff’s ‘Child as Scientist’ program, outlined and defended in their 1997 book Words, Thoughts and Theories is vitiated by a form of ‘cognitive individualism’ about science. Although this charge has often been leveled at Gopnik and Meltzoff’s work, it has rarely been developed in any detail. -/- I suggest that we should distinguish between two forms of cognitive individualism which I refer to as ‘ontic’ and ‘epistemic’ cognitive individualism (OCI and (...) ECI respectively). I then argue - contra Ronald Giere – that Gopnik and Meltzoff’s commitment to OCI is relatively unproblematic, since it is an easily detachable part of their view. By contrast, and despite their explicit discussion of the issue, their commitment to ECI is much more problematic. (shrink)

Though many agree that we need to account for the role that social factors play in inquiry, developing a viable social epistemology has proved to be difficult. According to Longino, it is the processes that make inquiry possible that are aptly described as "social," for they require a number of people to sustain them. These processes, she claims, not only facilitate inquiry, but also ensure that the results of inquiry are more than mere subjective opinions, and thus deserve to be (...) called "knowledge." In this paper, I (a) explain Longino's epistemology, and (b) defend it against charges that have recently been raised by Kitcher, Schmitt, and Solomon. Longino rightly recognizes that not all social factors have the same (adverse) affect on inquiry. She also recommends that we distinguish knowledge from mere opinion by reference to a social standard. (shrink)

I take another look at the history of science and offer some fresh insights into why the history of science is filled with discarded theories. I argue that the history of science is just as we should expect it to be, given the following two facts about science: theories are always only partial representations of the world, and almost inevitably scientists will be led to investigate phenomena that the accepted theory is not fit to account for. Together these facts suggest (...) that most scientific theories are apt to be discarded sometime, superseded by new theories that better serve scientists’ new research interests. Consequently, it is reasonable to expect that many of the theories we currently accept, despite their many impressive successes, will be discarded sometime in the future. But I also argue that discarded theories are not always aptly characterized as a sign of failure or as a sign of some sort of shortcoming with science. Theories are discarded because scientists are making advances in their pursuit of knowledge. Thus, discarded theories are often a sign of the good health of science. Scientists are responding to their changing research interests. (shrink)

It is widely accepted by formal and informal logicians alike that a formal logic which, by the lights of English, gets the connectives wrong, nevertheless conspires to get entailment right—right that is, modulo English. There is a vexing problem occasioned by this semantic alienation of formal logic. It is next to impossible for formal logic to meet the expectations of realism. What, then, of informal logic?

This paper explores the idea that laws express relationships between properties or universals as defended in Michael Tooley's recent book Causation: A Realist Approach. I suggest that the most plausible version of realism will take a different form than that advocated by Tooley. According to this alternative, laws are grounded in facts about the capacities and powers of particular systems, rather than facts about relations between universals. The notion of lawfulness is linked to the notion of invariance, rather than to (...) the metaphysical notion of a necessary connection. (shrink)

The practice of model-building is very common in analytic philosophical theology. Yet many other theologians worry that any attempt to model God must be hubristic and idolatrous. A better understanding of scientific modeling can set the stage for a more fruitful engagement between analytic theologians and their critics. I first present an account of scientific modeling that draws on recent work in the philosophy of science. I then apply that account to a prominent analytic model of the trinity, Michael Rea (...) and Jeffrey Brower’s “material constitution model.” I argue that modeling – whether scientific or theological – need not be understood as a hubristic enterprise. A model does not always try to grasp its target at all, let alone grasp it fully and completely. Even theologians who are committed to a strong doctrine of divine mystery can therefore find value in analytic modeling. (shrink)

This paper describes an alternative to the common view that explanation in the special sciences involves subsumption under laws. According to this alternative, whether or not a generalization can be used to explain has to do with whether it is invariant rather than with whether it is lawful. A generalization is invariant if it is stable or robust in the sense that it would continue to hold under a relevant if it is stable or robust in the sense that it (...) would continue to hold under a relevant class of changes. Unlike lawfulness, invariance comes in degrees and has other features that are well suited to capture the characteristics of explanatory generalizations in the special sciences. For example, a generalization can be invariant even if it has exceptions or holds only over a limited spatio-temporal interval. The notion of invariance can be used to resolve a number of dilemmas that arise in standard treatments of explanatory generalizations in the special sciences. (shrink)

This paper explores some connections between competing conceptions of scientific laws on the one hand, and a problem in the foundations of statistical mechanics on the other. I examine two proposals for understanding the time asymmetry of thermodynamic phenomenal: David Albert's recent proposal and a proposal that I outline based on Hans Reichenbach's “branch systems”. I sketch an argument against the former, and mount a defense of the latter by showing how to accommodate statistical mechanics to recent developments in the (...) philosophy of scientific laws. (shrink)

The rich and ongoing debate about constructivism in chemistry education includes questions about the relationship, for better or worse, between applications of the theory in pedagogy and in epistemology. This paper presents an examination of the potential to use connections of epistemological and pedagogical constructivism to one another. It examines connections linked to the content, processes, and premises of science with a goal of prompting further research in these areas.

Many standard philosophical accounts of scientific practice fail to distinguish between modeling and other types of theory construction. This failure is unfortunate because there are important contrasts among the goals, procedures, and representations employed by modelers and other kinds of theorists. We can see some of these differences intuitively when we reflect on the methods of theorists such as Vito Volterra and Linus Pauling on the one hand, and Charles Darwin and Dimitri Mendeleev on the other. Much of Volterra's and (...) Pauling's work involved modeling; much of Darwin's and Mendeleev's did not. In order to capture this distinction, I consider two examples of theory construction in detail: Volterra's treatment of post-WWI fishery dynamics and Mendeleev's construction of the periodic system. I argue that modeling can be distinguished from other forms of theorizing by the procedures modelers use to represent and to study real-world phenomena: indirect representation and analysis. This differentiation between modelers and non-modelers is one component of the larger project of understanding the practice of modeling, its distinctive features, and the strategies of abstraction and idealization it employs. (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)

Roald Hoffmann and other theorists claim that we ought to use highly idealized chemical models (“qualitative models”) in order to increase our understanding of chemical phenomena, even though other models are available which make more highly accurate predictions. I assess this norm by examining one of the tradeoffs faced by model builders and model users—the tradeoff between precision and generality. After arguing that this tradeoff obtains in many cases, I discuss how the existence of this tradeoff can help us defend (...) Hoffmann's norm for modelling. (shrink)

Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...) the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations. (shrink)

Although most philosophical accounts about model/world relations focus on structural mappings such as isomorphism, similarity has long been discussed as an alternative account. Despite its attractions, proponents of the similarity view have not provided detailed accounts of what it means that a model is similar to a real-world target system. This article gives the outlines of such an account, drawing on the work of Amos Tversky.

Because of its complexity, contemporary scientific research is almost always tackled by groups of scientists, each of which works in a different part of a given research domain. We believe that understanding scientific progress thus requires understanding this division of cognitive labor. To this end, we present a novel agent-based model of scientific research in which scientists divide their labor to explore an unknown epistemic landscape. Scientists aim to climb uphill in this landscape, where elevation represents the significance of the (...) results discovered by employing a research approach. We consider three different search strategies scientists can adopt for exploring the landscape. In the first, scientists work alone and do not let the discoveries of the community as a whole influence their actions. This is compared with two social research strategies, which we call the follower and maverick strategies. Followers are biased towards what others have already discovered, and we find that pure populations of these scientists do less well than scientists acting independently. However, pure populations of mavericks, who try to avoid research approaches that have already been taken, vastly outperform both of the other strategies. Finally, we show that in mixed populations, mavericks stimulate followers to greater levels of epistemic production, making polymorphic populations of mavericks and followers ideal in many research domains. (shrink)

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 .. (shrink)

I argue that the best interpretation of the general theory of relativity has need of a causal entity, and causal structure that is not reducible to light cone structure. I suggest that this causal interpretation of GTR helps defeat a key premise in one of the most popular arguments for causal reductionism, viz., the argument from physics.

Many explanations in physics rely on idealized models of physical systems. These explanations fail to satisfy the conditions of standard normative accounts of explanation. Recently, some philosophers have claimed that idealizations can be used to underwrite explanation nonetheless, but only when they are what have variously been called representational, Galilean, controllable or harmless idealizations. This paper argues that such a half-measure is untenable and that idealizations not of this sort can have explanatory capacities.

The goal of this paper is to show how scientific explanation functions in the context of idealized models. It argues that the aspect of explanation most urgently requiring investigation is the nature of the connection between global theories and explanatory local models. This aspect is neglected in traditional accounts of explanation. The paper examines causal, minimal model, and structural accounts of model-based explanation. It argues that they too fail to offer an account of the connection with global theory that can (...) justify the explanatory power of an idealized local model, and consequently these accounts are unable effectively to distinguish explanatory from non-explanatory models. On the account proposed here, scientific explanation requires theoretical integration between the local model described in the explanation and a global theory with independent explanatory power. (shrink)

The goal of this paper is to show how scientific explanation functions in the context of idealized models. It argues that the aspect of explanation most urgently requiring investigation is the nature of the connection between global theories and explanatory local models. This aspect is neglected in traditional accounts of explanation. The paper examines causal, minimal model, and structural accounts of model-based explanation. It argues that they too fail to offer an account of the connection with global theory that can (...) justify the explanatory power of an idealized local model, and consequently these accounts are unable effectively to distinguish explanatory from non-explanatory models. On the account proposed here, scientific explanation requires theoretical integration between the local model described in the explanation and a global theory with independent explanatory power. (shrink)

Leading philosophical accounts presume that Thomas H. Morgan’s transmission theory can be understood independently of experimental practices. Experimentation is taken to be relevant to confirming, rather than interpreting, the transmission theory. But the construction of Morgan’s theory went hand in hand with the reconstruction of the chief experimental object, the model organism Drosophila melanogaster . This raises an important question: when a theory is constructed to account for phenomena in carefully controlled laboratory settings, what knowledge, if any, indicates the theory’s (...) relevance to phenomena outside highly controlled settings? The answer, I argue, is found within the procedural knowledge embedded within laboratory practice. †To contact the author, please write to: Minnesota Center for Philosophy of Science, University of Minnesota, Department of Philosophy, 831 Heller Hall, 271 19th Ave., University of Minnesota, Minneapolis, MN 55455‐0310; e‐mail: [email protected]. (shrink)

Former discussions of biological generalizations have focused on the question of whether there are universal laws of biology. These discussions typically analyzed generalizations out of their investigative and explanatory contexts and concluded that whatever biological generalizations are, they are not universal laws. The aim of this paper is to explain what biological generalizations are by shifting attention towards the contexts in which they are drawn. I argue that within the context of any particular biological explanation or investigation, biologists employ two (...) types of generations. One type identifies causal regularities exhibited by particular kinds of biological entities. The other type identifies how these entities are distributed in the biological world. (shrink)

Many philosophers of science follow Hempel in embracing both substantive and methodological anti-psychologism regarding the study of explanation. The former thesis denies that explanations are constituted by psychological events, and the latter denies that psychological research can contribute much to the philosophical investigation of the nature of explanation. Substantive anti-psychologism is commonly defended by citing cases, such as hyper-complex descriptions or vast computer simulations, which are reputedly generally agreed to constitute explanations but which defy human comprehension and, as a result, (...) fail to engender any relevant psychological events. It is commonly held that the truth of the substantive thesis would lend support to the methodological thesis. However, the standard argument for the substantive thesis presumes that philosophers’ own judgments about the aforementioned cases issues from mastery of the lay or scientific norms regarding the use of ‘explanation.’ Here we challenge this presumption with a series of experiments indicating that both lay and scientific populations require of explanations that they actually render their targets intelligible. This research not only undermines a standard line of argument for substantive anti-psychologism, it demonstrates the utility of psychological research methods for answering meta-questions about the norms regarding the use of ‘explanation.’. (shrink)

The importance of the comparative notion of versimilitude, or truthlikeness, for a realist conception of knowledge follows from two modest ‘realist’ assumptions, namely, that the aim of an enquiry, as an enquiry, is the truth of some matter; and that one false theory may realize this aim better than another. However, there seem to be two ways in which one (false) theory can realize this aim better than another. One (false) theory can be closer to the truth than another either (...) by being preponderantly more accurate in its predictions or by providing more comprehensive information about the system (or class of systems) at issue. This paper presents a model-theoretic approach to the analysis of the comprehensiveness-related component of the comparative notion of versimilitude. The machinery of the ‘semantic’ view of theories is applied to the problem of providing necessary and sufficient conditions for the truth of sentences of the form, ‘B is truth-increasing with respect to A’, where A and B are taken to be sets of structures. (shrink)

Is a policy-friendly philosophy of science possible? In order to respond this question, I consider a particular instance of contemporary philosophy of science, the semantic view of scientific theories, by placing it in the broader methodological landscape of the integration of philosophy of science into STS (Science and Technology Studies) as a component of the overall contribution of the latter to science policy. In that context, I defend a multi-disciplinary methodological integration of the special discipline composing STS against a reductionist (...) interdisciplinary unification, arguing that if STS wants to contribute to policy advising by constructing narratives of science practice feasible for science policy both in terms of descriptive completeness and intelligibility, then it must avoid the explanatory reductionism tendencies of special disciplines in interdisciplinary contexts. This would favour, at the same time, a relaxation of esoteric language. On this basis, it seems that the semantic view is one right candidate among other approaches in the philosophy of science for facilitating the integration of the methodologically different contributions to STS toward policy objectives. In fact, besides offering a more realistic and descriptively complete picture of science practice with respect to its predecessor in the philosophy of science, namely the syntactic view, the semantic view is also able to capture some aspects of science practice that elude even sociological approaches to STS, thus inviting different perspective on the same subject matter. (shrink)