Representing with imaginary models: Formats matter

Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and I emphasise the importance of what I call the “format” of a representation for the inferences agents can draw from it. Then, I show that the very same model can be presented under different formats, which do not enable scientists to perform the same inferences. Assuming that the main function of a representation is to allow one to draw predictions and explanations of the phenomena by reasoning with it, I conclude that imaginary models in abstracto are not used as representations: scientists always reason with formatted representations. Therefore, the problem of scientific representation does not lie in the relationship of imaginary entities with real systems. One should rather focus on the variety of the formats that are used in scientific practice.     

The inferences of common causes reduced to common origins

This paper advocates the reduction of the inference of common cause to that of common origins. It distinguishes and subjects to critical analysis thirteen interpretations of “the inference of common cause” whose conclusions do not follow from their assumptions. Instead, I introduce six types of inferences of common origins of information signals from their receivers to reduce, in the sense of supersede and replace, the thirteen inferences of common causes. I show how the paradigmatic examples of inferences of common cause, as well as a broader scope of inferences in the historical sciences, are better explained by inferences of origins.Inferences of origins from information rich coherences between receivers of information signals both fit more closely and explain better the range of examples that have traditionally been associated with inferences of common causes, as well as a broader scope of examples from the historical sciences. Shannon's concept of information as reduction in uncertainty, rather than physicalist concepts of information that relate it to entropy or waves, simplifies the inferences, preempts objections, and avoids the underdetermination of conclusions that challenge models of inferences of common causes.In the second part of the paper I model inferences of common origins from information preserved in their receivers. I distinguish information poor inferences that there were some common origins of receivers from the information richer inferences of ranges of possible common origins and the information transmission channels by which they transmitted signals to receivers. Lastly and most information rich, I distinguish the inference of the defining properties of common origins. The information transmission model from origins to receivers allows the reconceptualization of the concepts of "independence" as absence of intersections between information channels and "reliability" as the preservation of information from origins in receivers. Finally, I show how inferences of origins form the epistemic basis of the historical sciences.     

What do numerical (climate) models really represent?

The translation of a mathematical model into a numerical one employs various modifications in order to make the model accessible for computation. Such modifications include discretizations, approximations, heuristic assumptions, and other methods. The paper investigates the divergent styles of mathematical and numerical models in the case of a specific piece of code in a current atmospheric model. Cognizance of these modifications means that the question of the role and function of scientific models has to be reworked. Neither are numerical models pure intermediaries between theory and data, nor are they autonomous tools of inquiry. Instead, theory and data are transformed into a new symbolic form of research due to the fact that computation has become an essential requirement for every scientific practice. Therefore the question is posed: What do numerical (climate) models really represent?     

History of the Lenz–Ising model 1965–1971: the role of a simple model in understanding critical phenomena

This is the last in a series of three papers on the history of the Lenz–Ising model from 1920 to the early 1970s. In the first paper, I studied the invention of the model in the 1920s, while in the second paper, I documented a quite sudden change in the perception of the model in the early 1960s when it was realized that the Lenz–Ising model is actually relevant for the understanding of phase transitions. In this article, which is self-contained, I study how this realization affected attempts to understand critical phenomena, which can be understood as limiting cases of (first-order) phase transitions, in the epoch from circa 1965 to 1970, where these phenomena were recognized as a research field in its own right. I focus on two questions: What kinds of insight into critical phenomena was the employment of the Lenz–Ising model thought to give? And how could a crude model, which the Lenz–Ising model was thought to be, provide this understanding? I document that the model played several roles: At first, it played a role analogous to experimental data: hypotheses about real systems, in particular relations between critical exponents and what is now called the hypothesis of scaling, which was advanced by Benjamin Widom and others, were confronted with numerical results for the model, in particular the model’s so-called critical exponents. A positive result of a confrontation was seen as positive evidence for this hypothesis. The model was also used to gain insight into specific aspects of critical phenomena, for example that diverse physical systems exhibit similar behavior close to a critical point. Later, a more systematic program of understanding critical phenomena emerged that involved an explicit formulation of what it means to understand critical phenomena, namely, the elucidation of what features of the Hamiltonian of models lead to what kinds of behavior close to critical points. Attempts to accomplish this program culminated with the so-called hypothesis of universality, put forward independently by Robert B. Griffiths and Leo P. Kadanoff in 1970. They divided critical phenomena into classes with similar critical behavior. I also study the crucial role of the Lenz–Ising model in the development and justification of these ideas.     

Explanation,understanding, and unrealistic models

How can false models be explanatory? And how can they help us to understand the way the world works? Sometimes scientists have little hope of building models that approximate the world they observe. Even in such cases, I argue, the models they build can have explanatory import. The basic idea is that scientists provide causal explanations of why the regularity entailed by an abstract and idealized model fails to obtain. They do so by relaxing some of its unrealistic assumptions. This method of ‘explanation by relaxation’ captures the explanatory import of some important models in economics. I contrast this method with the accounts that Daniel Hausman and Nancy Cartwright have provided of explanation in economics. Their accounts are unsatisfactory because they require that the economic model regularities obtain, which is rarely the case. I go on to argue that counterfactual regularities play a central role in achieving ‘understanding by relaxation.’ This has a surprising implication for the relation between explanation and understanding: Achieving scientific understanding does not require the ability to explain observed regularities.     

Generative models: Human embryonic stem cells and multiple modeling relations

Model organisms are at once scientific models and concrete living things. It is widely assumed by philosophers of science that (1) model organisms function much like other kinds of models, and (2) that insofar as their scientific role is distinctive, it is in virtue of representing a wide range of biological species and providing a basis for generalizations about those targets. This paper uses the case of human embryonic stem cells (hESC) to challenge both assumptions. I first argue that hESC can be considered model organisms, analogous to classic examples such as Escherichia coli and Drosophila melanogaster. I then discuss four contrasts between the epistemic role of hESC in practice, and the assumptions about model organisms noted above. These contrasts motivate an alternative view of model organisms as a network of systems related constructively and developmentally to one another. I conclude by relating this result to other accounts of model organisms in recent philosophy of science.     

The emergence of objectivity: Fleck,Foucault, Kuhn and Hacking

The analytical notions of ‘thought style’, ‘paradigm’, ‘episteme’ and ‘style of reasoning’ are some of the most popular frameworks in the history and philosophy of science. Although their proponents, Ludwik Fleck, Thomas Kuhn, Michel Foucault, and Ian Hacking, are all part of the same philosophical tradition that closely connects history and philosophy, the extent to which they share similar assumptions and objectives is still under debate. In the first part of the paper, I shall argue that, despite the fact that these four thinkers disagree on certain assumptions, their frameworks have the same explanatory goal – to understand how objectivity is possible. I shall present this goal as a necessary element of a common project -- that of historicising Kant's a priori. In the second part of the paper, I shall make an instrumental use of the insights of these four thinkers to form a new model for studying objectivity. I shall also propose a layered diagram that allows the differences between the frameworks to be mapped, while acknowledging their similarities. This diagram will show that the frameworks of style of reasoning and episteme illuminate conditions of possibility that lie at a deeper level than those considered by thought styles and paradigms.     

How to think about analogical inferences: A reply to Norton

In this paper, I raise some worries with John D. Norton's application of his material theory of induction to the study of analogical inferences. Skeptical that these worries can be properly addressed, I propose a principle to guide the philosophical research on analogical inferences and argue for its usefulness.     

Superposition: on Cavalieri’s practice of mathematics

Bonaventura Cavalieri has been the subject of numerous scholarly publications. Recent students of Cavalieri have placed his geometry of indivisibles in the context of early modern mathematics, emphasizing the role of new geometrical objects, such as, for example, linear and plane indivisibles. In this paper, I will complement this recent trend by focusing on how Cavalieri manipulates geometrical objects. In particular, I will investigate one fundamental activity, namely, superposition of geometrical objects. In Cavalieri’s practice, superposition is a means of both manipulating geometrical objects and drawing inferences. Finally, I will suggest that an integrated approach, namely, one which strives to understand both objects and activities, can illuminate the history of mathematics.     

How to infer explanations from computer simulations

Computer simulations are involved in numerous branches of modern science, and science would not be the same without them. Yet the question of how they can explain real-world processes remains an issue of considerable debate. In this context, a range of authors have highlighted the inferences back to the world that computer simulations allow us to draw. I will first characterize the precise relation between computer and target of a simulation that allows us to draw such inferences. I then argue that in a range of scientifically interesting cases they are particular abductions and defend this claim by appeal to two case studies.     

An empirical approach to symmetry and probability

We often rely on symmetries to infer outcomes’ probabilities, as when we infer that each side of a fair coin is equally likely to come up on a given toss. Why are these inferences successful? I argue against answering this question with an a priori indifference principle. Reasons to reject such a principle are familiar, yet instructive. They point to a new, empirical explanation for the success of our probabilistic predictions. This has implications for indifference reasoning generally. I argue that a priori symmetries need never constrain our probability attributions, even for initial credences.     

A second look at the colors of the dinosaurs

In earlier work, I predicted that we would probably not be able to determine the colors of the dinosaurs. I lost this epistemic bet against science in dramatic fashion when scientists discovered that it is possible to draw inferences about dinosaur coloration based on the microstructure of fossil feathers (Vinther et al., 2008). This paper is an exercise in philosophical error analysis. I examine this episode with two questions in mind. First, does this case lend any support to epistemic optimism about historical science? Second, under what conditions is it rational to make predictions about what questions scientists will or will not be able answer? In reply to the first question, I argue that the recent work on the colors of the dinosaurs matters less to the debate about the epistemology of historical science than it might seem. In reply to the second question, I argue that it is difficult to specify a policy that would rule out the failed bet without also being too conservative.     

Does time-symmetry imply retrocausality? How the quantum world says “Maybe”?

It has often been suggested that retrocausality offers a solution to some of the puzzles of quantum mechanics: e.g., that it allows a Lorentz-invariant explanation of Bell correlations, and other manifestations of quantum nonlocality, without action-at-a-distance. Some writers have argued that time-symmetry counts in favour of such a view, in the sense that retrocausality would be a natural consequence of a truly time-symmetric theory of the quantum world. Critics object that there is complete time-symmetry in classical physics, and yet no apparent retrocausality. Why should the quantum world be any different?This note throws some new light on these matters. I call attention to a respect in which quantum mechanics is different, under some assumptions about quantum ontology. Under these assumptions, the combination of time-symmetry without retrocausality is unavailable in quantum mechanics, for reasons intimately connected with the differences between classical and quantum physics (especially the role of discreteness in the latter). Not all interpretations of quantum mechanics share these assumptions, however, and in those that do not, time-symmetry does not entail retrocausality.     

How we load our data sets with theories and why we do so purposefully

In this paper, I compare theory-laden perceptions with imputed data sets. The similarities between the two allow me to show how the phenomenon of theory-ladenness can manifest itself in statistical analyses. More importantly, elucidating the differences between them will allow me to broaden the focus of the existing literature on theory-ladenness and to introduce some much-needed nuances. The topic of statistical imputation has received no attention in philosophy of science. Yet, imputed data sets are very similar to theory-laden perceptions, and they are now an integral part of many scientific inferences. Unlike the existence of theory-laden perceptions, that of imputed data sets cannot be challenged or reduced to a manageable source of error. In fact, imputed data sets are created purposefully in order to improve the quality of our inferences. They do not undermine the possibility of scientific knowledge; on the contrary, they are epistemically desirable.     

I am knowledge. Get me out of here! On localism and the universality of science

It has become increasingly common in historiography of science to understand science and its products as inherently local. However, this orientation is faced with three problems. First, how can one explain the seeming universality of contemporary science? Second, if science is so reflective of its local conditions of production, how can it travel so effortlessly to other localities and even globally? And third, how can scientific knowledge attain validity outside its context of origin? I will argue that the notion of standardization and theories of delocalization manage to explain the ‘globality’ of science, but that localism denies ‘universality’ if it is understood non-spatially. Further, localism limits the validity of scientific knowledge unacceptably inside the laboratory walls or other boundaries of knowledge creation. This is not consistent with scientific practice. I will consider on what grounds extra-local knowledge inferences that transcend the boundaries of locality could be seen as justified.     

Thomas Reid’s Newtonian Theism: his differences with the classical arguments of Richard Bentley and William Whiston

Reid was a Newtonian and a Theist, but did he found his Theism on Newton’s physics? In opposition to commonplace assumptions about the role of Theism in Reid’s philosophy, my answer is no. Reid prefers to found his Theism on a priori reasons, rather than on physics. Reid’s understanding of physics as an empirical science stops it from contributing in any clear and efficient way to issues of natural theology. In addition, Reid is highly sceptical of our ability to discover the efficient and final causes of natural phenomena, knowledge of which is essential for natural theology. To bring out Reid’s differences with classical Newtonian Theists Richard Bentley and William Whiston, I examine their use of the law and force of general gravitation, and reconstruct what would be Reidian objections.     

Models as interpreters (with a case study from pain science)

Most philosophical accounts of scientific models assume that models represent some aspect, or some theory, of reality. They also assume that interpretation plays only a supporting role. This paper challenges both assumptions. It proposes that models can be used in science to interpret reality. (a) I distinguish these interpretative models from representational ones. They find new meanings in a target system’s behaviour, rather than fit its parts together. They are built through idealisation, abstraction and recontextualisation. (b) To show how interpretative models work, I offer a case study on the scientific controversy over foetal pain. It highlights how pain scientists use conflicting models to interpret the human foetus and its behaviour, and thereby to support opposing claims about whether the foetus can feel pain. (c) I raise a sceptical worry and a methodological challenge for interpretative models. To address the latter, I use my case study to compare how interpretative and representational models ought to be evaluated.     

Successful visual epistemic representation

In this paper, I characterize visual epistemic representations as concrete two- or three-dimensional tools for conveying information about aspects of their target systems or phenomena of interest. I outline two features of successful visual epistemic representation: that the vehicle of representation contain sufficiently accurate information about the phenomenon of interest for the user's purpose, and that it convey this information to the user in a manner that makes it readily available to her. I argue that actual epistemic representation may involve tradeoffs between these features and is successful to the extent that they are present.     

Diagrams and alien ways of thinking

The recent wave of data on exoplanets lends support to METI ventures (Messaging to Extra-Terrestrial Intelligence), insofar as the more exoplanets we find, the more likely it is that “exominds” await our messages. Yet, despite these astronomical advances, there are presently no well-confirmed tests against which to check the design of interstellar messages. In the meantime, the best we can do is distance ourselves from terracentric assumptions. There is no reason, for example, to assume that all inferential abilities are language-like. With that in mind, I argue that logical reasoning does not have to be couched in symbolic notation. In diagrammatic reasoning, inferences are underwritten, not by rules, but by transformations of self-same qualitative signs. I use the Existential Graphs of C. S. Peirce to show this. Since diagrams are less dependent on convention and might even be generalized to cover non-visual senses, I argue that METI researchers should add some form of diagrammatic representations to their repertoire. Doing so can shed light, not just on alien minds, but on the deepest structures of reasoning itself.