Several recent accounts of modeling have focused on the modal dimension of scientific inquiry. More precisely, it has been suggested that there are specific models and modeling practices that are best understood as being geared towards possibilities, a view recently dubbed modal modeling. But modalities encompass much more than mere possibility claims. Besides possibilities, modal modeling can also be used to investigate contingencies, necessities or impossibilities. Although these modal concepts are logically connected to the notion of possibility, not all models (...) are equal in their affordances for these richer modal inferences. This paper investigates the modal extent of selected models and argues that analyzing singular model-target pairings by themselves is typically not enough to explain their modal aptness or to identify the kinds of modalities they can be used to reason about. Furthermore, it is argued that some important concepts that are not explicitly modal - like biological robustness - can be understood modally through their relational nature to a background space of possibilities. In conclusion, it is suggested that the strategy of modal modeling is contrastive, situating particular possibilities in larger modal spaces and studying the structural relations within them. (shrink)

This paper aims to develop an account of the pursuitworthiness of models based on a view of models as epistemic tools. This paper is motivated by the historical question of why, in the 1960s, when many scientists hardly found QSAR models attractive, some pharmaceutical scientists pursued Quantitative Structure–Activity Relationship (QSAR) models despite the lack of potential for theoretical development or empirical success. This paper addresses this question by focusing on how models perform their heuristic functions as epistemic tools rather than (...) as potential theories. I argue that models perform their heuristic function by “constructing” phenomena from data in the sense that they allow the model users who interact with the medium of the models to recognise the phenomena as such. The constructed phenomena assist model users in identifying which conditional hypotheses that are focused on low-level regularities concerning entities such as chemical compounds are more “testworthy,” a concept that links the costs associated with hypothesis testing with the fertility of the hypothesis. (shrink)

The aim of this article is to use a model from the origin of life studies to provide some depth and detail to our understanding of exploratory models by suggesting that some of these models should be understood as indeterminate. Models that are indeterminate are a type of exploratory model and therefore have extensive potential and can prompt new lines of research. They are distinctive in that, given the current state of scientific understanding, we cannot specify how and where the (...) model will be useful in understanding the natural world: in this case, the origin of life on Earth. The purpose of introducing indeterminacy is to emphasize the epistemic uncertainty associated with modeling, a feature of this practice that has been under emphasized in the literature in favor of attempts to understand the more specific epistemic successes afforded by models. (shrink)

Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation (e.g., reduction and emergence). In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory multiscale modeling, scientists use multiscale (...) techniques to select information that is relevant to explain a particular type of behavior of the target system. In descriptive multiscale modeling scientists use multiscale techniques to explore lower-scale features which could be explanatorily relevant to many different types of behavior, and to determine which features of a target system an upper-scale data pattern could refer to. Using multiscale models from data-driven neuroscience as a case study, I argue that descriptive multiscale models have an exploratory function because they are a sources of potential explanations and serve as tools to reassess our conception of the target system. (shrink)

This paper examines two parallel discussions of scientific modeling which have invoked experimentation in addressing the role of models in scientific inquiry. One side discusses the experimental character of models, whereas the other focuses on their exploratory uses. Although both relate modeling to experimentation, they do so differently. The former has considered the similarities and differences between models and experiments, addressing, in particular, the epistemic value of materiality. By contrast, the focus on exploratory modeling has highlighted the various kinds of (...) exploratory functions of models in the early stages of inquiry. These two perspectives on modeling are discussed through a case study in the field of synthetic biology. The research practice in question explores biological control by making use of an ensemble of different epistemic means: mathematical models and simulations, synthetic genetic circuits and intracellular measuring devices, and finally electronic circuits. We argue that the study of exploratory modeling should trace the ways different epistemic means, in different materialities, are being combined over time. Finally, the epistemic status of such novel investigative objects as synthetic genetic circuits is evaluated, with the conclusion that they can function as both experiments and models. (shrink)

The standard view in current philosophy of creativity says that being creative has two requirements: being novel and being valuable. The standard view on creativity has recently become an object of critical scrutiny. Hills and Bird have specifically proposed to remove the value requirement from the definition, as it is not clear that creative objects are necessarily valuable or creative people necessarily praiseworthy. In this paper, I argue against Hills and Bird, since eliminating the element of value from the explanation (...) of creativity hinders the understanding of the role that creative products play in actual epistemic practices, which are fundamentally normative. More specifically, I argue that the terms ‘creativity’ and ‘creative’ function as thick epistemic concepts when employed by competent epistemic agents in practice, that is, these concepts have both a descriptive and an evaluative content that cannot be disentangled from one another. Accordingly, I suggest that philosophers should prefer thick accounts over thin accounts of creativity. A thick account of creativity is one that endorses the standard view at its basis, but further develops it in two ways: by stressing the entanglement of the value and novelty requirements; by permitting to encompass a range of domain-specific characterizations of such entanglement for different epistemic situations. In order to take the first steps in the development of such a thick account of creativity, I look at the domain of scientific practices as a case in point, and try to spell out what the thickness of creative instances typically entails here. Namely, I identify the worthy novelty of creative models and methods with their potential to clarify a tradition, with fruitfulness, and with the fulfilment of exploratory aims. (shrink)

Researchers often pursue proof of concept research, but criteria for evaluating such research remain poorly specified. This article proposes a general framework for proof of concept research that k...

This paper explores the epistemic status of models and simulations between theory, on the one hand, and observations, on the other. In particular, I will argue that the interpretation of an essentially invariant astrophysical model structure can change substantially over time. I will illustrate this claim using as an example the first 20 years (1985–2004) of development of the Paris-Durham shock code—a numerical model of slow interstellar shock waves (i.e. a disturbance of the medium that travel faster than the local (...) speed of sound). I will show that the model’s interpretation and, in particular, its underlying representational ideal—the modeler’s (often implicit) goal governing the development and the use of the model—changed notably during this period. Whereas the code was originally used in a purely exploratory fashion, it was later taken to represent and encompass the target phenomenon as completely as possible. It is noteworthy that during this transition the model’s change of epistemic status was never explicitly acknowledged or in any way articulated. However, the impetus for the change can, I claim, be found in the role that observational data came to play in the later publications. (shrink)

This paper explores the epistemic status of models and simulations between theory, on the one hand, and observations, on the other. In particular, I will argue that the interpretation of an essentially invariant astrophysical model structure can change substantially over time. I will illustrate this claim using as an example the first 20 years (1985–2004) of development of the Paris-Durham shock code—a numerical model of slow interstellar shock waves (i.e. a disturbance of the medium that travel faster than the local (...) speed of sound). I will show that the model’s interpretation and, in particular, its underlying representational ideal—the modeler’s (often implicit) goal governing the development and the use of the model—changed notably during this period. Whereas the code was originally used in a purely exploratory fashion, it was later taken to represent and encompass the target phenomenon as completely as possible. It is noteworthy that during this transition the model’s change of epistemic status was never explicitly acknowledged or in any way articulated. However, the impetus for the change can, I claim, be found in the role that observational data came to play in the later publications. (shrink)

In this essay, I provide an overview of the debate on infinite and essential idealizations in physics. I will first present two ostensible examples: phase transitions and the Aharonov– Bohm effect. Then, I will describe the literature on the topic as a debate between two positions: Essentialists claim that idealizations are essential or indispensable for scientific accounts of certain physical phenomena, while dispensabilists maintain that idealizations are dispensable from mature scientific theory. I will also identify some attempts at finding a (...) middle ground between the essentialists and dispensabilists camps. Finally, I will raise questions for future research on essential and infinite idealizations via the notion of exploration. (shrink)