The perturbative treatment of realistic quantum field theories, such as quantum electrodynamics, requires the use of mathematical idealizations in the approximation series for scattering amplitudes. Such mathematical idealizations are necessary to derive empirically relevant models from the theory. Mathematical idealizations can be either controlled or uncontrolled, depending on whether current scientific knowledge can explain whether the effects of the idealization are negligible or not. Drawing upon negative mathematical results in asymptotic analysis (failure of Borel summability) and renormalization group (...) theory (failure of asymptotic safety), we argue that the mathematical idealizations applied in perturbative quantum electrodynamics should be understood as uncontrolled. This, in turn, leads to the problematic conclusion that such theories do not have theoretical models in the natural understanding of this term. The existence of unquestionable empirically successful theories without theoretical models has significant implications both for our understanding of the theory-model relationship in physics and the concept of empirical adequacy. (shrink)

In The Explanatory Dispensability of Idealizations, Sam Baron suggests a possible strategy enabling the indispensability argument to break the symmetry between mathematical claims and idealization assumptions in scientific models. Baron’s distinction between mathematical and non-mathematical idealization, I claim, is in need of a more compelling criterion, because in scientific models idealization assumptions are expressed through mathematical claims. In this paper I argue that this mutual dependence of idealization and mathematics cannot be read in terms (...) of symmetry and that Baron’s non-causal notion of mathematical difference-making is not effective in justifying any symmetry-breaking between mathematics and idealization. The function of making a difference that Baron attributes to mathematics cannot be referred to physical facts, but to the features of quantities, such as step lengths or time intervals taken into account in the models. It appears, indeed, that it does not follow from Baron’s argument that idealizations do not help to carry the explanatory load at least for two reasons: mathematics is not independent of idealizations in modelling and idealizations help mathematics to carry the explanatory load of a model in different degrees. (shrink)

Abstract Three main concepts of model in science are distinguished: (1) semantical model of a theory; (2) real model of another real thing; (3) mathematical model of a real thing. The last concept is the most important for the empirical sciences. The mathematical model is not identical with a theory: it is an ideal object which is directly described by the theory. We have here an intermediate level between reality and theory.

Is there a unique way to de-idealize models? If not, how might the possible ways of reducing the distortion between models and reality differ from each other? Based on an empirical case study conducted in financial markets, this paper discusses how a popular valuation model (the Discounted Cash Flow model) idealizes reality and how the market participants de-idealize it in concrete market situations. In contrast to Cartwright's view that economic models are generally over-constrained, this paper suggests that (...) valuation models are under-constrained. This serves as the reason why the relaxation of simplifying assumptions and concretization do not work as methods of de-idealization. The paper finds that financial market participants de-idealize models using commentary that takes the form of judgment. As a conclusion, a hypothesis is formulated that proposes that the more underdetermined the model is the bigger role narrative and other pragmatic elements play in the process of model application. (shrink)

Scientific models typically contain idealizations, or assumptions that are known not to be true. Philosophers have long questioned the nature of idealizations: Are they heuristic tools that will be abandoned? Or rather fictional representations of reality? And how can we reconcile them with realism about knowledge of nature? Immanuel Kant developed an account of scientific investigation that can inspire a new approach to the contemporary debate. Kant argued that scientific investigation is possible only if guided by ideal assumptions—what he (...) calls “regulative ideas”. These ideas are not true of objects of nature, and yet they are not heuristic tools or fictional represen- tations. They are necessary rules governing the construction and assessment of scientific explanations. In this paper, I suggest that some idealizations can be interpreted as having necessary regulative value and as being compatible with scientific realism. I first analyze the puzzle of the nature of idealization and present the main approaches to this topic in the literature. Second, I reconsider the puzzle vis-a-vis a restricted, Kantian definition of idealization and a novel characterization of the relation between idealization and truth. Finally, I discuss in detail an example of idealization (the Hardy-Weinberg equilibrium) along the suggested Kantian lines. (shrink)

Many accounts of scientific modelling assume that models can be decomposed into the contributions made by their accurate and inaccurate parts. These accounts then argue that the inaccurate parts of the model can be justified by distorting only what is irrelevant. In this paper, I argue that this decompositional strategy requires three assumptions that are not typically met by our best scientific models. In response, I propose an alternative view in which idealized models are characterized as holistically (...) distorted representations that are justified by allowing for the application of various modelling techniques. (shrink)

Ecologists attempt to understand the diversity of life with mathematical models. Often, mathematical models contain simplifying idealizations designed to cope with the blooming, buzzing confusion of the natural world. This strategy frequently issues in models whose predictions are inaccurate. Critics of theoretical ecology argue that only predictively accurate models are successful and contribute to the applied work of conservation biologists. Hence, they think that much of the mathematical work of ecologists is poor science. Against this view, (...) I argue that model building is successful even when models are predictively inaccurate for at least three reasons: models allow scientists to explore the possible behaviors of ecological systems; models give scientists simplified means by which they can investigate more complex systems by determining how the more complex system deviates from the simpler model; and models give scientists conceptual frameworks through which they can conduct experiments and fieldwork. Critics often mistake the purposes of model building, and once we recognize this, we can see their complaints are unjustified. Even though models in ecology are not always accurate in their assumptions and predictions, they still contribute to successful science. (shrink)

The book "Idealization XIV: Models in Science" offers a detailed ontological, epistemological and historical account of the role of models in scientific practice. The volume contains contributions of different international scholars who developed many aspects of the use of idealizations and models both in the natural and the social sciences. This volume is particularly relevant because it offers original contributions concerning one of the main topic in philosophy of science: the role of models in such (...) branches of the sciences and the humanities like comparative historical sociology, economics, history, linguistics and political philosophy. -/- Contributors are: Giacomo Borbone, Krzysztof Brzechczyn, Mieszko Ciesielski, Adam Czerniak, Xavier de Donato Rodríguez, José L. Falguera, Adolfo García de la Sienra, Lidia Godek, Igor Hanzel, Łukasz Hardt, Krzysztof Kiedrowski, Barbara Konat, Zenonas Norkus, Piotr Przybysz, Piotr Szwochert. (shrink)

Phylogenetic models traditionally represent the history of life as having a strictly-branching tree structure. However, it is becoming increasingly clear that the history of life is often not strictly-branching; lateral gene transfer, endosymbiosis, and hybridization, for example, can all produce lateral branching events. There is thus motivation to allow phylogenetic models to have a reticulate structure. One proposal involves the reconciliation of genealogical discordance. Briefly, this method uses patterns of disagreement – discordance – between trees of different genes (...) to add lateral branching events to phylogenetic trees of taxa, and to estimate the most likely cause of these events. I use this practice to argue for: (1) a need for expanded accounts of multiple-models idealization, (2) a distinction between automatic and manual de-idealization, and (3) recognition that idealization may serve the meso-level aims of science in a different way than hitherto acknowledged. (shrink)

This paper erects a framework for analyzing some idealized models as (what I call) inferentially veridical representations. It adopts a version of the semantic view of theories that focuses on properties, and mobilizes conceptual resources associated with properties and the way that properties are related in various ways. The outcome is an elaboration of some aspects of the analysis of Jones (2005).

Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., (...) class='Hi'>models, are constructed with the help of specific representational means and used in various ways, for example, for the purposes of scientific reasoning, theory construction and design of experiments and other artefacts. Furthermore, in this activity of modelling the model construction is intertwined with the construction of new phenomena, theoretical principles and new scientific concepts. We will illustrate these claims by studying the construction of the ideal heat engine by Sadi Carnot. (shrink)

The ArgumentIdealizations and approximations are an indispensable tool for the scientist. This paper argues that idealizations and approximations are equally indispensable for the philosopher of science. In particular, it is shown that the deductive model of scientific theories is an idealization in precisely the same sense that frictionless motion is an idealization in mechanics. By its very nature, an idealization cannot be criticized as not being absolutely true to the facts, for it need not be. Thus, the (...) usual type of criticism levelled against the deductive model is shown to be irrelevant. The main step in the argument consists in using a logic of approximation to clarify the notions of approximate implication and approximate entailment, which are usually rejected as hopelessly vague. More generally, the conception underlying the paper is that the desiderata of science should be formulated in such a way that complying with them could be a matter of degree, and that pragmatic considerations could be taken into account. This conception is motivated by the wish to introduce some quantitative concepts in the philosophy of science, thereby bringing the philosopher's perspective closer to that of the scientist. (shrink)

The social epistemology of science has adopted agent-based computer simulations as one of its core methods for investigating the dynamics of scientific inquiry. The epistemic status of these highly idealized models is currently under active debate in which they are often associated either with predictive or the argumentative functions. These two functions roughly correspond to interpreting simulations as virtual experiments or formalized thought experiments, respectively. This paper advances the argumentative account of modeling by proposing that models serve as (...) a means to (re)conceptualize the macro-level dynamics of complex social epistemic interactions. I apply results from the epistemology of scientific modeling and the psychology of mental simulation to the ongoing debate in the social epistemology of science. Instead of considering simulation models as predictive devices, I view them as artifacts that exemplify abstract hypothetical properties of complex social epistemic processes in order to advance scientific understanding, hypothesis formation, and communication. Models need not be accurate representations to serve these purposes. They should be regarded as pragmatic cognitive tools that engender rather than replace intuitions in philosophical reasoning and argumentation. Furthermore, I aim to explain why the community tends to converge around few model templates: Since models have the potential to transform our intuitive comprehension of the subject of inquiry, successful models may literally capture the imagination of the modeling community. (shrink)

In this paper a model is presented for the growth of knowledge in a dynamic scientific system. A system which is in some respects an idealization of a Lakatosian research program. The kinematics of the system is described in terms of two probabilistic variables, one of which is related to the evolution of its theoretical component and the other--to the growth of the empirical component. It is shown that when the empirical growth is faster than the theoretical growth the (...) posterior probability of the theoretical component increases. Thus, empirical progressiveness of a research program, as explicated in this model, is accompanied by an increase in the degree of confirmation. In such a case the system grows in a Popperian-like spirit, while learning from experience in a Bayesian manner. (shrink)

Starting with a measurable cardinal κ of the Mitchell order κ++ we construct a model with a precipitous ideal on ℵ1 but without normal precipitous ideals. This answers a question by T. Jech and K. Prikry. In the constructed model there are no Q-point precipitous filters on ℵ1, i. e. those isomorphic to extensions of Cubℵ1.

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For (...) illustrative purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

This paper calls into question one fundamental claim at the basis of an alleged puzzle for veritistic accounts of the value of idealized models: the claim that idealized models cannot be veridical representations of the world. Catherine Elgin has argued that the value of idealized models can only be explained if we construe them as exemplars, which do not represent the world. I argue that Elgin’s proposal is problematic and cannot accommodate central cases of idealization. Nevertheless, (...) there is value in Elgin’s proposal. I rescue her notion of selectiveness in exemplification and incorporate it within a revised account of representation. I provide independent motivation in favour of a form of veritism, which motivates a revision of Elgin’s case against it. I argue that an account based on selective representation has the resources to rescue veritism from the problems raised by Elgin. In the proposal I advance, idealized models are literally veridical representations of the world. (shrink)

The equations of state for the ideal classical gas are generalized with two characteristic constants known as the state indices. A simple and complete representation is developed for the super-ideal gas, with explicit results which are general enough to cover a wide range of equilibrium systems and states. Evidence and justification are provided in terms of examples and results of statistical physics. The new model should prove useful for treating problems involving physical behavior and properties which might otherwise call for (...) specialized or advanced methods of statistical mechanics. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For (...) illustrative purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

A successful vision system must solve the problem of deriving geometrical information about three-dimensional objects from two-dimensional photometric input. The human visual system solves this problem with remarkable efficiency, and one challenge in vision research is to understand howneural representations of objects are formed and what visual information is used to form these representations. Ideal observer analysis has demonstrated the advantages of studying vision from the perspective of explicit generative models and a specified visual task, which divides the causes (...) of image variations into the separate categories of signal and noise. Classification image techniques estimate the visual information used in a task from the properties of “noise” images that interact most strongly with the task. Both ideal observer analysis and classification image techniques rely on the assumption of a generative model. We show here how the ability of the classification image approach to understand how an observer uses visual information can be improved by matching the type and dimensionality of the model to that of the neural representation or internal template being studied. Because image variation in real world object tasks can arise from both geometrical shape and photometric (illumination or material) changes, a realistic image generation process should model geometry as well as intensity. A simple example is used to demonstrate what we refer to as a “classification object” approach to studying three-dimensional object representations. (shrink)

A defense of "Scientific Realism" based on the role that idealizations play in the cognitive sciences. Written by an up and coming philosopher or mind and cognitive scientist"-- Provided by publisher.

There has recently been a great interest in models in the natural sciences. Models are used mainly for their representational functions: they help to concretize certain relationships between parameters in studying physical systems. For instance, we might be interested in representing how the planets orbit around the sun—a scale model of the solar system is an ideal tool for achieving this end. We are free to leave out one or two planets or ignore the moons which many of (...) the planets have. Alternatively, we might be interested in studying the relationship between two particular parameters—how one may be dependent on the other. Then we construct a functional model and determine the functional relationship between them. For instance, the orbital period of a planet is functionally dependent on the average distance of the planet from the sun.1 Models are rather widespread in the social sciences, especially in economics where functional models are used to study relationships between, say, supply and demand. Economics, however, also uses a different kind of model which is also used in the natural sciences: the hypothetical or as if model. Economists employ as if constructions when they assume economic agents to be perfectly rational beings who always seek to maximize their utilities. It is common knowledge that economic agents are not perfectly rational and that the assumptions of perfect rationality and optimal information are at best idealizations which do not, strictly speaking, correspond to the economic reality of how economic agents behave in the market-place. (shrink)

In the methodology of theory construction, the concept of "intuitions" is commonly assigned a central role. This is especially true of philosophical and social scientific theories or rational human agency. An equally important trait of such accounts is the theorist's employment of "ideal models" or rational agency. It is frequently supposed that the concept of intuitions and the concept of ideal models link in such a way as to give rise to a coherent and load-bearing notion of "objective (...) normativity." This paper shows, with reference to a wide range of contemporary theories, that the employment of ideal models is otiose, and that the supposedly related concept of objective normativity is groundless. (shrink)

This work motivates the Combined Model for Combining Ideals, which Larry Temkin introduces in “sketch” form in Rethinking the Good, and goes on to begin filling in the details of the sketch. It argues that the Combined Model for Combining Ideals is most plausible when there are upper and lower caps on the extent to which an ideal can add to or subtract from the overall goodness of an outcome, but the caps for different values can and should differ.

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between (...) class='Hi'>models and targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

There is much debate on how social values should influence scientific research. However, the question of practical applicability of philosophers’ normative proposals has received less attention. Here, we test the attainability of Matthew J. Brown’s (2020) Moral Imagination ideal (MI ideal), which aims to help scientists to make warranted value-judgements through reflecting on goals, options, values, and stakeholders of research. Here, the tools of the MI ideal are applied to a climate modelling setting, where researchers are developing aerosol-cloud interaction (ACI) (...) parametrizations in an Earth System Model with the broader goal of improving climate sensitivity estimation. After the identification of minor obstacles to applying the MI ideal, we propose two ways to increase its applicability. First, its tools should be accompanied with more concrete guidance for identifying how social values enter more technical decisions in scientific research. Second, since research projects can have multiple goals, examining the alignment between broader societal aims of research and more technical goals should be part of the tools of the MI ideal. (shrink)

In The Explanatory Dispensability of Idealizations, Sam Baron suggests a possible strategy enabling the indispensability argument to break the symmetry between mathematical claims and idealization assumptions in scientific models. Baron’s distinction between mathematical and non-mathematical idealization, I claim, is in need of a more compelling criterion, because in scientific models idealization assumptions are expressed through mathematical claims. In this paper I argue that this mutual dependence of idealization and mathematics cannot be read in terms (...) of symmetry and that Baron’s non-causal notion of mathematical difference-making is not effective in justifying any symmetry-breaking between mathematics and idealization. The function of making a difference that Baron attributes to mathematics cannot be referred to physical facts, but to the features of quantities, such as step lengths or time intervals taken into account in the models. It appears, indeed, that it does not follow from Baron’s argument that idealizations do not help to carry the explanatory load at least for two reasons: mathematics is not independent of idealizations in modelling and idealizations help mathematics to carry the explanatory load of a model in different degrees. (shrink)

More general form of -neutrosophic ideal is introduced, and their properties are investigated. Relations between -neutrosophic ideal and )-neutrosophic ideal are discussed. Characterizations of )-neutrosophic ideal are discussed, and conditions for a neutrosophic set to be an )-neutrosophic ideal are displayed.

Both questions as abstract objects and the speech acts, here called requests, by which we ask them play an enormous role in all argumentative practices. Nonetheless, there is hardly a proper systematic treatment of questions and requests in current argumentation theories. This paper is a first attempt at providing such a systematic treatment. This is achieved by following the ideal model of a critical discussion as elaborated over the years by the Amsterdam school of pragma-dialectics. After introducing the distinction between (...) questions and requests, the paper sets forth and illustrates the norms of questioning which implicitly underlie the four stages of a critical discussion in the standard order: confrontation, opening, argumentation, and conclusion. Among other things, it is shown that crucial insights of pragma-dialectics are illuminated by the questioning perspective, in particular the distinction between single and multiple differences of opinion, the duties of the antagonist and protagonist roles, the precise character of the argumentation stage, and the different ways in which a disagreement can be resolved. (shrink)

In this paper we examine the epistemic value of highly idealized agent-based models of social aspects of scientific inquiry. On the one hand, we argue that taking the results of such simulations as informative of actual scientific inquiry is unwarranted, at least for the class of models proposed in recent literature. Moreover, we argue that a weaker approach, which takes these models as providing only “how-possibly” explanations, does not help to improve their epistemic value. On the other (...) hand, we suggest that if ABMs of science underwent two types of robustness analysis, they could indeed have a clear epistemic function, namely by providing evidence for philosophical and historical hypotheses. In this sense, ABMs can obtain evidential and explanatory properties and thus be a useful tool for integrated history and philosophy of science. We illustrate our point with an example of a model—building on the work by Kevin Zollman—which we apply to a concrete historical case study. (shrink)

In empirical modeling, mathematics has an important utility in transforming descriptive representations of target system into calculation devices, thus creating useful scientific models. The transformation may be considered as the action of tools. In this paper, I assume that model idealizations could be such tools. I then examine whether these idealizations have characteristic properties of tools, i.e., whether they are being adapted to the objects to which they are applied, and whether they are to some extent generic.

Idealizations are ubiquitous in physics. They are distortions or falsities that enter into theories, laws, models, and scientific representations. Various questions suggest themselves: What are idealizations? Why do we appeal to idealizations and how do we justify them? Are idealizations essential to physics and, if so, in what sense and for which purpose? How can idealizations provide genuine understanding? If our motivation for believing in the existence of unobservable entities like electrons and quarks is that they are indispensable to (...) our best theories, should we also believe in the existence of indispensable idealizations? This Element will tackle such questions and offer an opinionated and selective introduction to philosophical issues concerning idealizations in physics. Topics to be covered include the concept of and reasons for introducing idealization, abstraction, and approximation, possible taxonomy and justification, and application to issues of mathematical Platonism, scientific realism, and scientific understanding. (shrink)

Applying first-order logic to derive the consequences of laws that are only approximately true of empirical phenomena involves idealization of a kind that is akin to applying arithmetic to calculate the area of a rectangular surface from approximate measures of the lengths of its sides. Errors in the data, in the exactness of the lengths in one case and in the exactness of the laws in the other, are in some measure transmitted to the consequences deduced from them, and (...) the aim of a theory of idealization is to describe this process. The present paper makes a start on this in the case of applied first-order logic, and relates it to Plato's picture of a world or model of 'appearances' in which laws are only approximately true, but which to some extent resembles an ideal world or model in which they are exactly true. (shrink)

Embryonic stem cells are derived from the preimplantation embryo and can differentiate into virtually any other cell type, which is governed by lineage specific transcriptions factors binding to cis regulatory elements to mediate changes in gene expression. The reliance on transcriptional regulation to maintain pluripotency makes ESCs a valuable model to study the role of distal CREs such as enhancers in modulating gene expression to affect cell fate decisions. This review will highlight recent advance on transcriptional enhancers, focusing on studies (...) performed in ESCs. In addition, we argue that the Nanog locus, which encodes for an ESC-critical TF, is particularly informative because it contains multiple co-regulated genes and enhancers in close proximity to one another. The unique landscape at Nanog permits the study of ongoing questions including whether multiple enhancers function additively versus synergistically, determinants of gene specificity, and cell-to-cell variability in gene expression. Nanog encodes a transcription factor required to maintain ESC pluripotency. The Nanog locus is an ideal model to study enhancers because it contains multiple highly potent super-enhancers within a small region. Nanog and three super-enhancers are in close physical proximity but only two SEs are required for Nanog expression. (shrink)

Explainable AI (xAI) methods are important for establishing trust in using black-box models. However, recent criticism has mounted against current xAI methods that they disagree, are necessarily false, and can be manipulated, which has started to undermine the deployment of black-box models. Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations ‘must be wrong’. However, strict fidelity to the truth is historically not a desideratum (...) in science. Idealizations – the intentional distortions introduced to scientific theories and models – are commonplace in the natural sciences and are seen as a successful scientific tool. Thus, it is not falsehood qua falsehood that is the issue. In this paper, I outline the need for xAI research to engage in idealization evaluation. Drawing on the use of idealizations in the natural sciences and philosophy of science, I introduce a novel framework for evaluating whether xAI methods engage in successful idealizations or deceptive explanations (SIDEs). SIDEs evaluates whether the limitations of xAI methods, and the distortions that they introduce, can be part of a successful idealization or are indeed deceptive distortions as critics suggest. I discuss the role that existing research can play in idealization evaluation and where innovation is necessary. Through a qualitative analysis we find that leading feature importance methods and counterfactual explanations are subject to idealization failure and suggest remedies for ameliorating idealization failure. (shrink)

The precocious induction in vivo and in culture of insect and amphibian metamorphosis by exogenous ecdysteroids and thyroid hormones, and its retardation or inhibition by juvenile hormone and prolactin, respectively, has allowed the analysis of such diverse processes of post‐embryonic development as morphogenesis, tissue remodelling, functional reorganization, and programmed cell death. Metamorphosis in vertebrates also shares many similarities with mammalian development in the late foetal and perinatal period. This review describes the regulation of expression of some of the ‘adult’ gene (...) products during metamorphosis in invertebrates and vertebrates. Recent studies on metamorphosis have revealed the important role played by auto‐induction of hormone receptor genes, based on which a model will be presented to explain the activation of ‘downstream’ genes which give rise to the adult phenotype. It will also be argued that metamorphosis is an ideal model for analyzing some of the major mechanisms governing post‐embryonic development. (shrink)

Every model leaves out or distorts some factors that are causally connected to its target phenomenon -- the phenomenon that it seeks to predict or explain. If we want to make predictions, and we want to base decisions on those predictions, what is it safe to omit or to simplify, and what ought a causal model to describe fully and correctly? A schematic answer: the factors that matter are those that make a difference to the target phenomenon. There are several (...) ways to understand differencemaking. This paper advances a view as to which is the most relevant to the forecaster and the decision-maker. It turns out that the right notion of differencemaking for thinking about idealization in prediction is also the right notion for thinking about idealization in explanation; this suggests a carefully circumscribed version of Hempel's famous thesis that there is a symmetry between explanation and prediction. (shrink)

Idealization is a necessity. Stripping away levels of complexity makes questions tractable, focuses our attention, and lets us develop comprehensible, testable models. Applying such models, however, requires care and attention to how the idealizations incorporated into their development affect their predictions. In epistemology, we tend to focus on idealizations concerning individual agents' capacities, such as memory, mathematical ability, and so on, when addressing this concern. By contrast, this dissertation focuses on social idealizations, particularly those pertaining to salient (...) social categories like race, sex, and gender. In Chapter II, Privilege and Superiority, we begin with standpoint epistemology, one of the earliest efforts to grapple with the ways that social structures affect our epistemic lives. I argue that, if we interpret standpoint epistemologists' claims as hypotheses about the ways that our social positions affect access to evidence, we can fruitfully employ recent developments in evidence logic to study the consequences. I lay the groundwork for this project, developing a model based on neighborhood semantics for modal logic. Adapting this framework to standpoint epistemology helps to clarify the meaning of terms like "epistemic privilege" and "superior knowledge" and to elucidate the differences between various accounts of standpoint epistemology. I also address a longstanding criticism of these views: Longino's (1990) bias paradox, which suggests that there is no objective position from which to judge the goodness of a particular standpoint. Chapter III, Evidence in a Non-Ideal World, turns to the broader social context, looking at how ideology affects the availability of evidence. Throughout the chapter, I take the formation, justification, and maintenance of racist, sexist, and otherwise oppressive beliefs as a central case. I argue that these beliefs are, at least sometimes, formed as a result of evidential distortion, a structural feature of our epistemic contexts that skews readily available evidence in favor of dominant ideologies. Because they are formed this way, such beliefs will appear justified on prominent accounts of justification, both internalist and externalist. As a result, epistemic norms that fail to account for such non-ideal conditions will deliver verdicts that are not only counter-intuitive, but also morally unpalatable. This, I argue, reveals a kind of structural epistemic injustice, especially where oppressive ideology is involved and suggests the need for epistemic norms that are sensitive to agents' social contexts. Much of the discussion in Chapters II and III depends on social categories like race and gender, arguing that they have a distinctive influence on our epistemic lives. In Chapter IV, I Know You Are, But What Am I?, my co-author and I focus on social categories themselves, distinguishing between self-identity, social identity, and social role. We self-identify as gay or straight, men or women, couch-potatoes or gym rats. Sometimes, these identities affect our social roles---the way we are perceived and treated by others---and sometimes they do not. This relationship between our internal identities and our preferred public perceptions begs for explanation. On our account, this relationship is captured by what we refer to as 'social identity'---roughly, internal identities made available to others. We argue that this account of social identity plays an illuminating role in structural explanation of discrimination and individual behavior, dissolves puzzles surrounding the phenomenon of 'passing', and explains certain moral and political obligations toward individuals. (shrink)

It's standard in epistemology to approach questions about knowledge and rational belief using idealized, simplified models. But while the practice of constructing idealized models in epistemology is old, metaepistemological reflection on that practice is not. Greco argues that the fact that epistemologists build idealized models isn't merely a metaepistemological observation that can leave first-order epistemological debates untouched. Rather, once we view epistemology through the lens of idealization and model-building, the landscape looks quite different. Constructing idealized (...) class='Hi'>models is likely the best epistemologists can do. Once one starts using epistemological categories like belief, knowledge, and confidence, the realm of idealization and model-building is entered. We can object to a model of knowledge by pointing to a better model, but in the absence of a better model, the fact that a framework for epistemologizing theorizing involves simplifications, approximations, and other inaccuracies-the fact of its status as an idealized model-is not in itself objectionable. Once we accept that theorizing in epistemological terms is inescapably idealized, a number of intriguing possibilities open up. Greco defends a package of epistemological views that might otherwise have looked indefensibly dismissive of our cognitive limitations-a package according to which we know a wide variety of facts with certainty, including what our evidence is, what we know and don't know, and what follows from our knowledge. (shrink)

Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim (...) of this paper is to precisely articulate and to defend this claim. In particular, we will distinguish between autonomous and embedded toy models, and, then, argue that important examples of autonomous toy models are sometimes best interpreted to provide how-possibly understanding, while embedded toy models yield how-actually understanding, if certain conditions are satisfied. (shrink)

In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space (...) and their effect on the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models. (shrink)

How can a model that stops short of representing the whole truth about the causal production of a phenomenon help us to understand the phenomenon? I answer this question from the perspective of what I call the simple view of understanding, on which to understand a phenomenon is to grasp a correct explanation of the phenomenon. Idealizations, I have argued in previous work, flag factors that are casually relevant but explanatorily irrelevant to the phenomena to be explained. Though useful to (...) the would-be understander, such flagging is only a first step. Are there any further and more advanced ways that idealized models aid understanding? Yes, I propose: the manipulation of idealized models can provide considerable insight into the reasons that some causal factors are difference-makers and others are not, which helps the understander to grasp the nature of explanatory connections and so to better grasp the explanation itself. (shrink)

The book reveals different dimensions of modeling in the historical sciences. Papers collected in the first part (Ontology of the Historical Process) consider different models of historical reality and discuss their status. The second part (Modeling in the Methodology of History) presents various forms of idealization in historiographic research. The papers in the third part (Modeling in the Research Practice) present various models of past reality (e.g. of Poland, Central Europe and the general history of the feudal (...) system) put forward by historians. Other papers consider the status of scientific laws and historical generalizations. The volume will be of interest to those who study analytical philosophy of history, methodology of history and social sciences, social philosophy as well as theory and history of historiography. (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)

Causal relations among components and activities are intentionally misrepresented in mechanistic explanations found routinely across the life sciences. Since several mechanists explicitly advocate accurately representing factors that make a difference to the outcome, these idealizations conflict with the stated rationale for mechanistic explanation. We argue that these idealizations signal an overlooked feature of reasoning in molecular and cell biology—mechanistic explanations do not occur in isolation—and suggest that explanatory practices within the mechanistic tradition share commonalities with model-based approaches prevalent in population (...) biology. (shrink)