According to a recent proposal, the epistemic aim of metaphysics as a discipline is to chart the different viable theories of metaphysical objects of inquiry (e.g. causation, persistence). This paper elaborates on and seeks to improve on that proposal in two related ways. First, drawing on an analogy with how-possibly explanation in science, I argue that we can usefully understand this aim of metaphysics as the charting of epistemically possible answers to metaphysical questions. Second, I argue that in order to (...) account for the epistemic goodness of this aim, one should appeal to the epistemic value it has in virtue of providing resources for non-factive understanding of the objects of metaphysical inquiry.**This paper is forthcoming in a special issue of Argumenta on the Epistemology of Metaphysics**. (shrink)

Among the many functions of models, explanation is central to the functioning and aims of science. However, the discussions surrounding modeling and explanation in philosophy have largely remained separate from each other. This chapter seeks to bridge the gap by focusing on the puzzle of model-based explanation, asking how different philosophical accounts answer the following question: if idealizations and fictions introduce falsehoods into models, how can idealized and fictional models provide true explanations? The chapter provides a selective and critical overview (...) of the available strategies for solving this puzzle, mainly focusing on idealized models and how they explain. (shrink)

In the context of astrophysical modeling at the solar system scale, we investigate the modalities implied by taking into account different levels of detail at which phenomena can be considered. In particular, by framing the analysis in terms of the how-possibly/how-actually distinction, we address the debated question as to whether the degree of plausibility is tightly linked to the degree of detail. On the grounds of concrete examples, we argue that, also in the astrophysical context examined, this is not necessarily (...) the case. (shrink)

Some machine learning models, in particular deep neural networks (DNNs), are not very well understood; nevertheless, they are frequently used in science. Does this lack of understanding pose a problem for using DNNs to understand empirical phenomena? Emily Sullivan has recently argued that understanding with DNNs is not limited by our lack of understanding of DNNs themselves. In the present paper, we will argue, _contra_ Sullivan, that our current lack of understanding of DNNs does limit our ability to understand with (...) DNNs. Sullivan’s claim hinges on which notion of understanding is at play. If we employ a weak notion of understanding, then her claim is tenable, but rather weak. If, however, we employ a strong notion of understanding, particularly explanatory understanding, then her claim is not tenable. (shrink)

Philosophers of science have recently taken care to highlight different modeling practices where scientific models primarily contribute modal information, in the form of for example possibility claims, how-possibly explanations, or counterfactual conditionals. While examples abound, comparatively little attention is being paid to the question of under what conditions, and in virtue of what, models can perform this epistemic function. In this paper, we firstly delineate modal modeling from other modeling practices, and secondly reviewattempts to spell out and explain the epistemic (...) success conditions of modal modeling. The aim is to more clearly expose the respective justificatory strategies of these accounts, and secondly, to identify lacunae where further work is needed. (shrink)

Several philosophers of science claim that scientific toy models afford knowledge of possibility, but answers to the question of why toy models can be expected to competently play this role are scarce. The main line of reply is that toy models support possibility claims insofar as they are credible. I raise a challenge for this credibility-thesis, drawing on a familiar problem for imagination-based modal epistemologies, and argue that it remains unanswered in the current literature. The credibility-thesis has a long way (...) to go if it is to account for the epistemic merits of toy models. (shrink)

Science is replete with falsehoods that epistemically facilitate understanding by virtue of being the very falsehoods they are. In view of this puzzling fact, some have relaxed the truth requirement on understanding. I offer a factive view of understanding that fully accommodates the puzzling fact in four steps: (i) I argue that the question how these falsehoods are related to the phenomenon to be understood and the question how they figure into the content of understanding it are independent. (ii) I (...) argue that the falsehoods do not figure into the understanding’s content by being elements of its periphery or core. (iii) Drawing lessons from case studies, I argue that the falsehoods merely enable understanding. When working with such falsehoods, only the truths we extract from them are elements of the content of our understanding. (iv) I argue that the extraction view is compatible with the thesis that falsehoods can have an epistemic value by virtue of being the very falsehoods they are. (shrink)

One can find in the literature two sets of views concerning the relationship between understanding and explanation: that one understands only if 1) one has knowledge of causes and 2) that knowledge is provided by an explanation. Taken together, these tenets characterize what I call the narrow knowledge account of understanding. While the first tenet has recently come under severe attack, the second has been more resistant to change. I argue that we have good reasons to reject it on the (...) basis of theoretical models that provide how-possibly explanations. These models, while they do not explain in the strict sense, afford understanding. In response, I propose an alternative epistemology of understanding, broad KAU, that takes cases of theoretical modelling into account. (shrink)

Two recent contributions have discussed, and disagreed, over whether so-called toy models that attempt to represent dispositions have the power to explain. In this paper, I argue that neither of these positions is completely correct. Toy models may accurately represent, satisfy the veridicality condition, yet fail to provide how-actually explanations. This is because some dispositions remain unmanifested. Instead, the models provide how-possibly explanations; they _possibly_ explain.

The practice of scientific modelling often resorts to hypothetical, false, idealised, targetless, partial, generalised, and other types of modelling that appear to have at least partially non-actual targets. In this paper, I will argue that we can avoid a commitment to non-actual targets by sketching a framework where models are understood as having networks of possibilities as their targets. This raises a further question: what are the truthmakers for the modal claims that we can derive from models? I propose that (...) we can find truthmakers for the modal claims derived from models in actuality, even in the case of supposedly non-actual targets. I then put this framework to use by examining a case study concerning the modelling of superheavy elements. (shrink)

Agent-based modelling has become a well-established method in social epistemology and philosophy of science but the question of what kind of explanations these models provide remains largely open. This paper is dedicated to this issue. It starts by distinguishing between real-world phenomena, real-world possibilities, and logical possibilities as different kinds of targets which agent-based models can represent. I argue that models representing the former two kinds provide how-actually explanations or causal how-possibly explanations. In contrast, models that represent logical possibilities provide (...) epistemically opaque how-possibly explanations. While highly idealised ABMs in the form in which they are initially proposed typically fall into the last category, the epistemic opaqueness of explanations they provide can be reduced by validation procedures. To this purpose, an examination of results of simulations in terms of classes of models can be particularly helpful. I illustrate this point by discussing a class of ABMs of scientific interaction and the claim that a high degree of interaction can impede scientific inquiry. (shrink)

Philosophy Compass, Volume 17, Issue 7, July 2022.

Recent epistemology of modality has seen a growing trend towards metaphysics-first approaches. Contrastingly, this paper offers a more philosophically modest account of justifying modal claims, focusing on the practices of scientific modal inferences. Two ways of making such inferences are identified and analyzed: actualist-manipulationist modality and relative modality. In AM, what is observed to be or not to be the case in actuality or under manipulations, allows us to make modal inferences. AM-based inferences are fallible, but the same holds for (...) practically all empirical inquiry. In RM, modal inferences are evaluated relative to what is kept fixed in a system, like a theory or a model. RM-based inferences are more certain but framework-dependent. While elements from both AM and RM can be found in some existing accounts of modality, it is worth highlighting them in their own right and isolating their features for closer scrutiny. This helps to establish their relevant epistemologies that are free from some strong philosophical assumptions often attached to them in the literature. We close by showing how combining these two routes amounts to a view that accounts for a rich variety of modal inferences in science. (shrink)

This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of models must target sets of (...) models, their multiplicity of functions within science, and their scientific context and history and for almost any aspect x of phenomenon y, scientists require multiple models to achieve scientific goal z. (shrink)

This paper examines lessons obtained by means of simulations in the form of agent-based models about the norms that are to guide disagreeing scientists. I focus on two types of epistemic and methodological norms: norms that guide one’s attitude towards one’s own theory, and norms that guide one’s attitude towards the opponent’s theory. Concerning I look into ABMs that have been designed to examine the context of peer disagreement. Here I challenge the conclusion that the given ABMs provide a support (...) for the so-called Steadfast Norm, according to which one is epistemically justified in remaining steadfast in their beliefs in face of disagreeing peers. I argue that the proposed models at best provide evidence for a weaker norm, which concerns methodological steadfastness. Concerning I look into ABMs aimed at examining epistemic effects of scientific interaction. Here I argue that the models provide diverging suggestions and that the link between each ABM and the type of represented inquiry is still missing. Moreover, I examine alternative strategies of arguing in favor of the benefits of scientific interaction, relevant for contemporary discussions on scientific pluralism. (shrink)

In this paper I examine the epistemic function of agent-based models of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni’s classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the last category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, but by (...) providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman’s ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and an argumentation-based ABM, which provides insights into potential mechanisms underlying the efficiency of scientific inquiry. (shrink)

The results of econometric modeling are fragile in the sense that minor changes in estimation techniques or sample can lead to statistical models that support inconsistent causal hypotheses. The fragility of econometric results undermines making conclusive inferences from the empirical literature. I argue that the program of evidential pluralism, which originated in the context of medicine and encapsulates to the normative reading of the Russo-Williamson Thesis that causal claims need the support of both difference-making and mechanistic evidence, offers a ground (...) for resolving empirical disagreements. I analyze a recent econometric controversy regarding the tax elasticity of cigarette consumption and smoking intensity. Both studies apply plausible estimation techniques but report inconsistent results. I show that mechanistic evidence allows for discriminating econometric models representing genuine causal relations from accidental dependencies in data. Furthermore, I discuss the differences between biological and social mechanisms and mechanistic evidence across the disciplines. I show that economists mainly rely on mathematical models to represent possible mechanisms. Still, claiming the actuality of the represented mechanisms requires establishing that crucial assumptions of these models are descriptively adequate. I exemplify my approach to assessing the quality of mechanistic evidence in economics with an analysis of two models of rational addiction. (shrink)

Empiricist modal epistemologies can be attractive, but are often limited in the range of modal knowledge they manage to secure. In this paper, I argue that one such account – similarity-based modal empiricism – can be extended to also cover justification of many scientifically interesting possibility claims. Drawing on recent work on modelling in the philosophy of science, I suggest that scientific modelling is usefully seen as the creation and investigation of relevantly similar epistemic counterparts of real target systems. On (...) the basis of experiential knowledge of what is actually the case with the models, one can draw justified conclusions about what is de re possible for the target systems. (shrink)

In Artificial Intelligence, Artificial Neural Networks are very accurate models in tasks such as classification and regression in the study of natural phenomena, but they are considered “black boxes” because they do not allow direct explanation of what they address. This paper reviews the possibility of scientific explanation from these models and concludes that other efforts are required to understand their inner workings. This poses challenges to access scientific explanation through their use, since the nature of Artificial Neural Networks makes (...) it difficult at first instance the scientific understanding that can be extracted from them. (shrink)

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)

The description of how individual models in families of models are related to each other is crucial for the general philosophical understanding of model-based scientific practice. We focus on the Capital Asset Pricing Models (CAPM) family, a cornerstone in financial economics, to provide a descriptive analysis of model relations within a family. We introduce the concepts of theoretical and empirical complementarity to characterise model relations. Our complementarity analysis of model relations has two types of payoff. Specifically regarding the CAPM, our (...) analysis reveals why this model family, which has been empirically contested, has yet remained popular and important: the different models that have been added over time have made new empirically and theoretically complementary contributions to the model family. More generally, our analysis reveals the dynamic character of model-based scientific practice. Characterising relations between models as theoretically and/or empirically complementary yields three hitherto underappreciated lessons: (i) actual modelling purposes are not always primarily epistemic, (ii) a model's individual import is relative, not absolute, and (iii) there is an important interplay between theory and data models. Faithfully characterising scientific modelling in this way facilitates subsequent analysis of models' epistemic import. (shrink)

Philosophers of science typically focus on the epistemic performance of scientific models when evaluating them. Analysing the effects that models may have on the world has typically been the purview of sociologists of science. We argue that the reactive (or “performative”) effects of models should also figure in model evaluations by philosophers of science. We provide a detailed analysis of how models in financial economics created the impetus for the growing importance of the phenomenon of “passive investing” in financial markets. (...) Considering this case motivates the position that we call contextualism about model evaluation, or _model contextualism_ for short. Model contextualism encompasses standard analyses of the epistemic performance of the model, but also includes their reactive aspects. It entails identifying the _epistemic and contextual import of the model_, the ways in which a model can engender change in the world (which we call the _channels of transmission_), and the _interactions between the epistemic and reactive import of a model_. (shrink)

Idealization is ubiquitous in human cognition, and so is the inclination to be puzzled by it: what to make of ideal gas, infinitely large populations, homo economicus, perfectly just society, known to violate matters of fact? This is apparent in social science theorizing, recent philosophy of science analyzing scientific modeling, and the debate over ideal and non-ideal theory in political philosophy. I will offer a set of concepts and principles to improve transparency about the precise contents of idealizations and their (...) distinct functions. (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)