Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: (...) Can the fictions and falsehoods inherent in the modeling practice do real explanatory work? Do some highly abstract and mathematical models exhibit a noncausal form of scientific explanation? How can one distinguish an exploratory "how-possibly" model explanation from a genuine "how-actually" model explanation? Do modelers face tradeoffs such that a model that is optimized for yielding explanatory insight, for example, might fail to be the most predictively accurate, and vice versa? This chapter explores the various answers that have been given to these questions. (shrink)

Kaplan and Craver claim that all explanations in neuroscience appeal to mechanisms. They extend this view to the use of mathematical models in neuroscience and propose a constraint such models must meet in order to be explanatory. I analyze a mathematical model used to provide explanations in dynamical systems neuroscience and indicate how this explanation cannot be accommodated by the mechanist framework. I argue that this explanation is well characterized by Batterman’s account of minimal model explanations and that (...) it demonstrates how relationships between explanatory models in neuroscience and the systems they represent is more complex than has been appreciated. (shrink)

Stem cell biology and systems biology are two prominent new approaches to studying cell development. In stem cell biology, the predominant method is experimental manipulation of concrete cells and tissues. Systems biology, in contrast, emphasizes mathematical modeling of cellular systems. For scientists and philosophers interested in development, an important question arises: how should the two approaches relate? This essay proposes an answer, using the model of Waddington’s landscape to triangulate between stem cell and systems approaches. This simple abstract model represents (...) development as an undulating surface of hills and valleys. Originally constructed by C. H. Waddington to visually explicate an integrated theory of genetics, development and evolution, the landscape model can play an updated unificatory role. I examine this model’s structure, representational assumptions, and uses in all three contexts, and argue that explanations of cell development require both mathematical models and concrete experiments. On this view, the two approaches are interdependent, with mathematical models playing a crucial but circumscribed role in explanations of cell development. (shrink)

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model (...) produces understanding that is related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding. (shrink)

During the 1930s and 1940s, American physical organic chemists employed electronic theories of reaction mechanisms to construct models offering explanations of organic reactions. But two molecular rearrangements presented enormous challenges to model construction. The Claisen and Cope rearrangements were predominantly inaccessible to experimental investigation and they confounded explanation in theoretical terms. Drawing on the idea that models can be autonomous agents in the production of scientific knowledge, I argue that one group of models in particular were functionally autonomous from (...) the Hughes–Ingold theory. Cope and Hardy’s models of the Claisen and Cope rearrangements were resources for the exploration of the Hughes–Ingold theory that otherwise lacked explanatory power. By generating ‘how-possibly’ explanations, these models explained how these rearrangements could happen rather than why they did happen. Furthermore, although these models were apparently closely connected to theory in terms of their construction, I argue that partial autonomy issued in extra-logical factors concerning the attitudes of American chemists to the Hughes–Ingold theory. And in the absence of a complete theoretical hegemony, a degree of consensus was reached concerning modelling the Claisen rearrangement mechanism.Keywords: Models; Explanation; Physical organic chemistry; Rearrangements. (shrink)

The signaling theory of religion has many claimed virtues, but these are not necessarily all realizable at the same time. Modeling choices involve trade-offs, and the available options here have not traditionally been well understood. This paper offers an overview of signaling theory relevant to the signaling theory of religion, arguing for a narrow, “core” reading of it. I outline a broad taxonomy of the choices on offer for signaling models, and examples of how previous and potential approaches to modeling (...) religious signaling meet or fail to meet the initial promise of the theory. A pluralist approach to religious signaling seems possible, but this would require a high level of detail and specificity with respect to both formal models and target systems. (shrink)

In biology a great variety of models can be distinguished: there is a gradation scale from the more realistic to the more idealistic ones. The place of a model on this scale depends on the role of the fundamental ideas, apriorisms and empirisms, which inspire the direction of thought. The relation between reality, models and explanatory theory is worked out. The interplay between model and ideas makes it understandable why in biology several kinds of explanation are possible.

Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) they (...) propose to answer the three questions cannot answer them, at least not by itself. Second, I argue that answers to the three questions are unnecessary to account for the explanatoriness of their minimal models. Finally, I argue that a common features account, what I call the ‘generalized ontic conception of explanation’, can capture the explanatoriness of minimal models. (shrink)

It is widely recognized that scientific theories are often associated with strictly inconsistent models, but there is little agreement concerning the epistemic consequences. Some argue that model inconsistency supports a strong perspectivism, according to which claims serving as interpretations of models are inevitably and irreducibly perspectival. Others argue that in at least some cases, inconsistent models can be unified as approximations to a theory with which they are associated, thus undermining this kind of perspectivism. I examine the arguments for perspectivism, (...) and contend that its strong form is defeasible in principle, not merely in special cases. The argument rests on the plausibility of scientific knowledge concerning non-perspectival, dispositional facts about modelled systems. This forms the basis of a novel suggestion regarding how to understand the knowledge these models afford, in terms of a contrastive theory of what-questions. (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)

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work (...) in computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology. (shrink)

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficultiesn in the traditional account.

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account. 1. Introduction2. Causal models: a review2.1Causal models2.2Syntax and semantics3. The definition of cause4. Examples5. A more refined definition6. DiscussionAAppendix: Some Technical IssuesA.1The active causal processA.2A closer look at AC2(b)A.3Causality with infinitely many variablesA.4Causality in nonrecursive (...) models. (shrink)

Jonathan Walkan challenges cognitive science's dominant model of mental representation and proposes a novel, well-devised alternative. The traditional view in the cognitive sciences uses a linguistic model of mental representation. That logic-based model of cognition informs and constrains both the classical tradition of artificial intelligence and modeling in the connectionist tradition. It falls short, however, when confronted by the frame problem---the lack of a principled way to determine which features of a representation must be updated when new information becomes available. (...) So far, proposed alternatives, including the imagistic model, have not resolved the problem. Waskan proposes the Intrinsic Cognitive Models hypothesis, according to which representational states can be concpetualized as the cognitive equivalent of scale models.Waskan argues further that the proposal that humans harbor and manipulate cognitive counterparts to scale models offers the only viable explanation for what most clearly differentiates humans from other creatures: the capacity to engage in truth-preserving manipulation of representations. The ICM hypothesis, he claims, can be distinguished from sentence-based accounts of truth preservation in a way that is fully compatible with what is known about the brain. (shrink)

We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from (...) the literature. 1. Introduction2. Causal models and the definition of actual causality: a review2.1Causal models2.2Syntax and semantics2.3The definition of cause3. Explanation: the basic definition4. Partial explanations and explanatory power5. The general definition6. DiscussionAppendix: the formal definition of causality. (shrink)

We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic examples from (...) the literature. (shrink)

We propose new definitions of explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent’s initial uncertainty. We show that the definition handles well a number of problematic examples from the (...) literature. (shrink)

Good explanations are not only true or probably true, but are also relevant to a causal question. Current models of causal explanation either only address the question of the truth of an explanation, or do not distinguish the probability of an explanation from its relevance. The tasks of scenario construction and conversational explanation are distinguished, which in turn shows how scenarios can interact with conversational principles to determine the truth and relevance of explanations. The proposed model (...) distinguishes causal discounting from causal backgrounding , and makes predictions concerning the differential effects of contextual information about alternative explanations on: (a) the kind of mental models constructed; (b) belief revision about probable cause; and (c) the perceived quality of a focal explanation. Four experiments are reported that test these predictions. The significance of the notion of explanatory relevance for research on causal explanation is then discussed. (shrink)

Mathematical models provide explanations of limited power of specific aspects of phenomena. One way of articulating their limits here, without denying their essential powers, is in terms of contrastive explanation.

SynopsisThe response to physics and chemistry which characterized mid-nineteenth century physiology took two major directions. One, found most prominently among the German physiologists, developed explanatory models which had as their fundamental assumption the ultimate reducibility of all biological phenomena to the laws of physics and chemistry. The other, characteristic of the French school of physiology, recognized that physics and chemistry provided potent analytical tools for the exploration of physiological activities, but assumed in the construction of explanatory models that the organism (...) involved special levels of organization and that there must, in consequence, be special biological laws.The roots of this argument about concept formation in physiology are explored in the works of Theodor Schwann, Johannes Müller, François Magendie and Claude Bernard among others. (shrink)

Scribal copying is investigated as a test case for the memetic and epidemiological models for explaining the distribution of cultural items. We may hypothesize that the incidence of errors could be low enough to allow two conditions for neo-Darwinian explanation to be fulfilled: first, that there be a rather reliable mechanism for heredity, and second that occasional mutations might produce a version more likely to survive and be propagated than the exemplar. Scriptorial conventions are reviewed. Textual criticism is investigated. (...) Finally, some attention is given to the psychology of language perception and production. In conclusion, it is argued that the memetic model for cultural transmission is not generally fecund while the epidemiological model is. (shrink)

In investigating their models, economists do not appear to engage much in the activities many philosophers take to be essential to scientific understanding of the world, activities such as testing hypotheses and establishing laws. How, then, can economic models explain anything about the real world? Borrowing from William Dray, an explanation of what something really is, as opposed to an explanation of why something happens, is the subsumption of the explanandum under a suitable concept. One way economic models (...) explain real-world phenomena is by providing explanations-what of such phenomena. In this way, economic models can afford explanations of the world without necessarily involving the activities philosophers take to be integral to scientific understanding of the world, for explanations-what do not necessarily involve the activities in question. (shrink)

: Models that fail to satisfy the Markov condition are unstable in the sense that changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. This sort of error arises exactly from the failure of non-Markovian models to track the set of causal relations upon which the values of response variables depend. The result has implications for discussions of the level of selection: under certain plausible conditions the (...) models of selection presented in such debates will not satisfy the Markov condition when fit to data from real populations. Since this is true both for group and individual level models, models of neither sort correctly represent the causal structure generating, nor correctly explain, the phenomena of interest. (shrink)

Read argues that modeling cultural idea systems serves to make explicit the cultural rules through which "cultural idea systems" frame behaviors that are culturally meaningful. Because cultural rules are typically "invisible" to us, one of the anthropologists' tasks is to elicit these rules, make them explicit and then use them to build explanations for patterns in cultural phenomena. The main example of Read's approach to cultural idea systems is the formal modeling of kinship terminologies. I reconstruct Read's modeling strategy as (...) comprising the following steps:From the way in which culture-bearers compute kin relations a data model is construed that makes explicit the cultural theory embedded in a kinship .. (shrink)

A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system. Furthermore, in many contexts, it is in (...) virtue of their independence of causes that optimality models are able to provide a better explanation than competing causal models. Consequently, our account of explanation and modeling must expand beyond the causal approach. (shrink)

Andrés Rivadulla argues for an instrumentalist approach to the use of theoretical models in the physical sciences, which, on his view, have not to be conceived of as intended representations of the phenomena, but just as useful tools for explaining and predicting those phenomena. He analyses two examples of theoretical models employed for those aims. The first one is the supernova model, intended mainly as explanatory. The second one is the atomic central field shell model, where the postulated internal structure (...) for atoms—their electronic configuration- made the prediction and discovery of new noble gases possible. He concludes that theoretical models can be regarded as artifacts designed with the specific purpose of facilitating calculations. (shrink)

The widespread and long-lived failings of academic economics are due to an over-reliance on largely inappropriate mathematical methods of analysis. This is an assessment I have long maintained. Many heterodox economists, however, appear to hold instead that the central problem is a form of political-economic ideology. Specifically, it is widely contended in heterodox circles that the discipline goes astray just because so many economists are committed to a portrayal of the market economy as a smoothly or efficiently functioning system or (...) some such, a portrayal that, whether sincerely held or otherwise, is inconsistent with the workings of social reality. Here I critically examine the contention that a form of political-economic ideology of this sort is the primary problem and assess its explanatory power. I conclude that the contention does not fare very well. I do not, though, deny that ideology of some sort has a major impact on the output of the modern economics academy. However it is of a different nature to the form typically discussed, and works in somewhat indirect and complex ways. Having raised the question of the impact of ideology I take the opportunity to explore its play in the economics academy more generally. (shrink)

We develop an account of laboratory models, which have been central to the group selection controversy. We compare arguments for group selection in nature with Darwin's arguments for natural selection to argue that laboratory models provide important grounds for causal claims about selection. Biologists get information about causes and cause-effect relationships in the laboratory because of the special role their own causal agency plays there. They can also get information about patterns of effects and antecedent conditions in nature. But to (...) argue that some cause is actually responsible in nature, they require an inference from knowledge of causes in the laboratory context and of effects in the natural context. This process, cause detection, forms the core of an analogical argument for group selection. We discuss the differing roles of mathematical and laboratory models in constructing selective explanations at the group level and apply our discussion to the units of selection controversy to distinguish between the related problems of cause determination and evaluation of evidence. Because laboratory models are at the intersection of the two problems, their study is crucial for framing a coherent theory of explanation for evolutionary biology. (shrink)

I examine the warrants we have in light of the empirical successes of a kind of model I call ‘ hybrid models ’, a kind that includes climate models among its members. I argue that these warrants ’ strengths depend on inferential virtues that are not just explanatory virtues, contrary to what would be the case if inference to the best explanation provided the warrants. I also argue that the warrants in question, unlike those IBE provides, guide inferences only (...) to model implications about which there is real uncertainty. My conclusion provides criteria of adequacy for epistemologies of climate and other hybrid models. (shrink)

Knauff and Gazzo Castañeda (2022) object to using the term “new paradigm” to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term “paradigm” may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of (...) mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved. (shrink)

We mobilise contemporary philosophy of science to further clarify observations on economic modelling made by Gilboa et al. (2023). We adopt a normative stance towards these modelling practices to identify the extent to which they are epistemically justified. Our message is simple: many of the distinctions proposed by Gilboa et al. (2023) are useful, but without the proper qualifications, too much flexibility in choosing the right interpretation risks downplaying the crucial role that empirical evidence should play in any (...) modelling endeavour. (shrink)

Quantum mechanics portrays the universe as involving non-local influences that are difficult to reconcile with relativity theory. By postulating backward causation, retro-causal interpretations of quantum mechanics could circumvent these influences and accordingly reconcile quantum mechanics with relativity. The postulation of backward causation poses various challenges for the retro-causal interpretations of quantum mechanics and for the existing conceptual frameworks for analyzing counterfactual dependence, causation and causal explanation. In this chapter, we analyze the nature of time, causation and explanation in (...) a local, deterministic retro-causal interpretation of quantum mechanics that is inspired by Bohmian mechanics. This interpretation of quantum mechanics offers a deterministic, local ‘hidden-variables’ model of the Einstein-Podolsky-Rosen experiment that poses a new challenge for Reichenbach’s principle of the common cause. In this model, the common cause – the state of particles at the emission from the source – screens off the correlation between its effects – the distant measurement outcomes – but nevertheless fails to explain the correlation between the effects. (shrink)

In Talking about, Unnsteinsson defends an intentionalist theory of reference by arguing that confused referential intentions degrade reference. Central to this project is a “belief model” of both identity confusion and unconfused thought. By appealing to a well‐known argument from Campbell, I argue that this belief model falls short, because it fails to explain the inferential behavior it promises to explain. Campbell's argument has been central in the contemporary literature on Frege's puzzle, but Unnsteinsson's account of confusion provides an opportunity (...) for more clarity about how the argument is best interpreted, and what it shows. (shrink)

Chakravartty puts forward a view of scientific knowledge that conceives of properties attributed to objects by scientific models as dispositions. Those dispositions refer to the capacity of an object to behave differently in different circumstances. This pluralism of behaviour is intended to show that perspectivalism does not exclude the possibility of non-perspectival knowledge. To support this claim, he offers an analogy between conflicting models and contrastive explanations. I examine the strength of the purported analogy between conflicting models and contrastive explanations. (...) Then, I analyse this dispositionalist account of properties in order to assess whether ontological pluralism can meet the challenge that conflicting models pose to the scientific realism. To conclude, I contend that to accomplish this goal, a more detailed account of dispositional properties should be provided, as well as a theory of explanatory relevance. (shrink)

We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to target (...) a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use-cases in ML and AI in a comparative manner. Specifically, we envision that user-studies will help determine human understandability of explanations generated using these two frameworks. (shrink)

The automatic prediction of court case judgments using Deep Learning and Natural Language Processing is challenged by the variety of norms and regulations, the inherent complexity of the forensic language, and the length of legal judgments. Although state-of-the-art transformer-based architectures and Large Language Models (LLMs) are pre-trained on large-scale datasets, the underlying model reasoning is not transparent to the legal expert. This paper jointly addresses court judgment prediction and explanation by not only predicting the judgment but also providing legal (...) experts with sentence-based explanations. To boost the performance of both tasks we leverage a legal named entity recognition step, which automatically annotates documents with meaningful domain-specific entity tags and masks the corresponding fine-grained descriptions. In such a way, transformer-based architectures and Large Language Models can attend to in-domain entity-related information in the inference process while neglecting irrelevant details. Furthermore, the explainer can boost the relevance of entity-enriched sentences while limiting the diffusion of potentially sensitive information. We also explore the use of in-context learning and lightweight fine-tuning to tailor LLMs to the legal language style and the downstream prediction and explanation tasks. The results obtained on a benchmark dataset from the Indian judicial system show the superior performance of entity-aware approaches to both judgment prediction and explanation. (shrink)

The neobehaviorist Clark L. Hull and his disciple Kenneth Spence shared in common many views on the nature of science and the role of theories in psychology. However, a telling exchange in their correspondence of the early 1940s reveals a disagreement over the nature of intervening variables in behavior theory. Spence urged Hull to abandon his interpretations of intervening variables in terms of physiological models in favor of positivistic, purely mathematical interpretations that conflicted with Hull's mechanistic explanatory aims and ontological (...) commitment to materialism. This dispute is set against the background of similar disputes in physics, and the origins of Hull's and Spence's divergent views on theoretical explanation are described. (shrink)

McCullough et al.'s target article is a psychological version of the reputation models pioneered by biologist Robert Trivers (1971) and economist Robert Frank (1988). The authors, like Trivers and Frank, offer an implausible explanation of the fact that revenge is common even when there are no possible reputational effects. I sketch a more plausible model based on recent research.

The periodic table is one of the best-known systems of classification in science. Because of the information it contains, it raises explanation-seeking questions. Quantum mechanical models of the behaviour of electrons may be seen as providing explanations in response to these questions. In this paper we first address the question ‘Do quantum mechanical models of atoms provide legitimate explanations?’ Because our answer is positive, our next question is ‘Are the explanations provided by quantum mechanical models of atoms mechanistic explanations?’. (...) This question is motivated by the fact that in many scientific disciplines, mechanistic explanations are abundant. Because our answer to the second question is negative, our last question is ‘What kind of explanation do quantum mechanical models of atom provide?’ By addressing these questions, we shed light on the nature of an important type of chemical explanation. (shrink)

Scientists appeal to models when explaining phenomena. Such explanations are often dubbed model explanations or model-based explanations. But what are the precise conditions for ME? Are ME special explanations? In our paper, we first rebut two definitions of ME and specify a more promising one. Based on this analysis, we single out a related conception that is concerned with explanations that are induced from working with a model. We call them ‘model-induced explanations’. Second, we study three paradigmatic cases of alleged (...) ME. We argue that all of them are MIE, upon closer examination. Third, we argue that this undermines the building consensus that model explanations are special explanations that, e.g., challenge the factivity of explanation. Instead, it suggests that what is special about models in science is the epistemology behind how models induce explanations. (shrink)