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)

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)

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)

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal (...) class='Hi'>models provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

Fundamental knowledge endures deep conceptual crisis manifested in total crisis of understanding, crisis of interpretation and representation, loss of certainty, troubles with physics, crisis of methodology. Crisis of understanding in fundamental science generates deep crisis of understanding in global society. What way should we choose for overcoming total crisis of understanding in fundamental science? It should be the way of metaphysical construction of new comprehensive model of ideality on the basis of the "modified ontology". Result of quarter-century wanderings: sum of (...) ideas, concepts and eidoses, new understanding of space, time, consciousness. (shrink)

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)

Recent methodological debates regarding the place of feasibility considerations in normative political theory are hindered for want of a rigorous model of the feasibility frontier. To address this shortfall, I present an analysis of feasibility that generalizes the economic concept of a production possibility frontier and then develop a rigorous model of the feasibility frontier using the familiar possible worlds technology. I then show that this model has significant methodological implications for political philosophy. On the Target View, a political (...) class='Hi'>ideal presents a long-term goal for morally progressive reform efforts and, thus, serves as an important reference point for our specification of normative political principles. I use the model to show that we can- not reasonably expect that adopting political ideals as long-term reform objectives will guide us toward the realization of morally optimal feasible states of affairs. I conclude by proposing that political philosophers turn their attention to the analysis of actual social failures rather than political ideals. (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)

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)

This paper is a review of Suarez, M. (ed.) Fictions in Science (Routledge).

The debate on ideal theory focuses mainly on whether it can provide a long-term target and a metric for assessing the justice of different institutional arrangements in non-ideal theory. Both critics and defenders of ideal theory typically overlook the role it plays in a model of fairness that can restrict the range of permissible arrangements under non-ideal conditions. In this paper, I explain ideal theory’s fairness role and its part in ensuring an institutional structure that (...) benefits everyone in a society. Critics of ideal theory therefore cannot reject it as simply useless. But I consider how they can question the attractiveness of an ideal-theory-based fairness constraint on non-ideal theory. (shrink)

Domain extension in mathematics occurs whenever a given mathematical domain is augmented so as to include new elements. Manders argues that the advantages of important cases of domain extension are captured by the model-theoretic notions of existential closure and model completion. In the specific case of domain extension via ideal elements, I argue, Manders’s proposed explanation does not suffice. I then develop and formalize a different approach to domain extension based on Dedekind’s Habilitationsrede, to which Manders’s account is compared. (...) I conclude with an examination of three possible stances towards extensions via ideal elements. (shrink)

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)

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 focus in the literature on scientific explanation has shifted in recent years towards model-based approaches. In recent work, Alisa Bokulich has argued that idealization has a central role to play in explanation. Bokulich claims that certain highly-idealized, structural models can be explanatory, even though they are not considered explanatory by causal, mechanistic, or covering law accounts of explanation. This paper focuses on Bokulich’s account in order to make the more general claim that there are problems with maintaining that (...) a structural criterion can capture the way that highly-idealized models explain. This paper examines Bokulich’s claim that the structural model explanation of quantum wavefunction scarring, featuring semiclassical mechanics, is deeper than the explanation provided by the local quantum model. The challenge for Bokulich is to show that the semiclassical model answers a wider range of w-questions, as this is her method of assessing structural information. I look at two reasonable approaches employing w-questions, and I argue that neither approach is ultimately satisfactory. Because structural similarity has preferences for more fundamental models, I argue that the local quantum model provides explanations that at least as deep as the semiclassical ones. The criterion either wrongly identifies all models as explanatory, or prefers models from fundamental theory. Either way, it cannot capture the way that highly-idealized models explain. (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)

I argue that ML models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.

In a recent special issue dedicated to Dani Rodrik’s (2015) influential monograph Economics Rules, Grüne-Yanoff and Marchionni (2018) raise a potentially damning problem for Rodrik’s suggestion that progress in economics should be understood and measured laterally, by a continuous expansion of new models. They argue that this could lead to an “embarrassment of riches”, i.e. the rapid expansion of our model library to such an extent that we become unable to choose between the available models, and thus needs (...) to be solved to make ‘model pluralism’ viable. Drawing on Veit’s (2019a) ‘model pluralism’ account, this paper argues that model pluralism as a thesis about the relationship between science and nature undermines the very idea of a general model selection framework for policy making. (shrink)

Rawlsian ideal theory is meant to perform various roles in non-ideal theory. In this paper, I distinguish between three roles, and I consider the extent to which we can expect ideal theory to perform them. It is meant to serve as a target to guide non-ideal theorising in the long-term. It is also supposed to provide a way of comparing different injustices to tell us which is worst and therefore in most urgent need of a remedy. (...) Finally, ideal theory is the basis for a model of fairness that restricts the set of morally permissible measures in non-ideal theory. I show how the first two roles—the target and urgency roles—are less plausible than the fairness role. (shrink)

In a possible world framework, an agent can be said to know a proposition just in case the proposition is true at all worlds that are epistemically possible for the agent. Roughly, a world is epistemically possible for an agent just in case the world is not ruled out by anything the agent knows. If a proposition is true at some epistemically possible world for an agent, the proposition is epistemically possible for the agent. If a proposition is true at (...) all epistemically possible worlds for an agent, the proposition is epistemically necessary for the agent, and as such, the agent knows the proposition. -/- This framework presupposes an underlying space of worlds that we can call epistemic space. Traditionally, worlds in epistemic space are identified with possible worlds, where possible worlds are the kinds of entities that at least verify all logical truths. If so, given that epistemic space consists solely of possible worlds, it follows that any world that may remain epistemically possible for an agent verifies all logical truths. As a result, all logical truths are epistemically necessary for any agent, and the corresponding framework only allows us to model logically omniscient agents. This is a well-known consequence of the standard possible world framework, and it is generally taken to imply that the framework cannot be used to model non-ideal agents that fall short of logical omniscience. -/- A familiar attempt to model non-ideal agents within a broadly world involving framework centers around the use of impossible worlds where the truths of logic can be false. As we shall see, if we admit impossible worlds where “anything goes” in epistemic space, it is easy to avoid logical omniscience. If any logical falsehood is true at some impossible world, then any logical falsehood may remain epistemically possible for some agent. As a result, we can use an impossible world involving framework to model extremely non-ideal agents that do not know any logical truths. -/- A much harder, and considerably less investigated challenge is to ensure that the resulting epistemic space can also be used to model moderately ideal agents that are not logically omniscient but nevertheless logically competent. Intuitively, while such agents may fail to rule out impossible worlds that verify complex logical falsehoods, they are nevertheless able to rule out impossible worlds that verify obvious logical falsehoods. To model such agents, we need a construction of a non-trivial epistemic space that partly consists of impossible worlds where not "anything goes". This involves imposing substantive constraints on impossible worlds to eliminate from epistemic space, say, trivially impossible worlds that verify obvious logical falsehoods. -/- The central aim of this dissertation is to investigate the nature of such non-trivially impossible worlds and the corresponding epistemic spaces. To flag my conclusions, I argue that successful constructions of epistemic spaces that can safely navigate between the Charybdis of logical omniscience and the Scylla of of “anything goes” are hard, if not impossible to find. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of (...) computational explanation, I argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

According to Popper's rationality principle, agents act in the most adequate way according to the objective situation. I propose a new interpretation of the rationality principle as consisting of an idealization and two abstractions. Based on this new interpretation, I critically discuss the privileged status that Popper ascribes to it as an integral part of all social scientific models. I argue that as an idealization, the rationality principle may play an important role in the social sciences, but it also (...) has inherent limitations that inhibit it from having the privileged status that Popper ascribes to it in all cases. (shrink)

Models are the coin of the realm in current philosophy of science, as they are in science itself, having replaced laws and theories as the primary strategy. Logical Positivism tried to erase the older neo-Kantian distinction between ideal constructions and reality. It returns in the case of models. Nowak’s concept of idealization pro- vided an alternative account of this issue. It construed model application as concretizations of hypotheses which improve by accounting for exceptions. This appears to account (...) for physical law. But it raises the problem of uniqueness: is the result unique, as physical law should be? Neo-Kantianism failed this test. Its solutions were circular justifications for claims of uniqueness. Nowak inherited the problem without resolving it. (shrink)

In common treatments of deontic logic, the obligatory is what is true in all deontically ideal possible worlds. In this article, I offer a new semantics for Standard Deontic Logic with Leibnizian intensions rather than possible worlds. Even though the new semantics furnishes models that resemble Venn diagrams, the semantics captures the strong soundness and completeness of Standard Deontic Logic. Since, unlike possible worlds, many Leibnizian intensions are not maximally consistent entities, we can amend the semantics to invalidate (...) the inference rule which ensures that all tautologies are obligatory. I sketch this amended semantics to show how it invalidates the rule in a new way. (shrink)

Inferentialists about scientific representation hold that an apparatus’s representing a target system consists in the apparatus allowing “surrogative inferences” about the target. I argue that a serious problem for inferentialism arises from the fact that many scientific theories and models contain internal inconsistencies. Inferentialism, left unamended, implies that inconsistent scientific models have unlimited representational power, since an inconsistency permits any conclusion to be inferred. I consider a number of ways that inferentialists can respond to this challenge before suggesting (...) my own solution. I develop an analogy to exploitable glitches in a game. Even though inconsistent representational apparatuses may in some sense allow for contradictions to be generated within them, doing so violates the intended function of the apparatus’s parts and hence violates representational “gameplay.”. (shrink)

According to Steven Nadler's novel interpretation of Spinoza's much discussed ‘free man’, the free man is not an unattainable ideal. On this reading, the free man represents an ideal condition not because he is passionless, as has often been claimed, but because even though he experiences passions, he ‘never lets those passions determine his actions’. In this paper, I argue that Nadler's interpretation is incorrect in taking the model of the free man to be an attainable ideal (...) within our reach. Furthermore, I show that Spinoza's moral philosophy has room for another ideal yet attainable condition, which is represented by the wise man. On my reading, becoming a wise man consists not in surmounting human bondage, but in understanding ourselves as finite expressions of God's power and, thereby, coming to terms with the ineliminability of bondage for us due to our very human or modal condition in the Spinozistic universe. (shrink)

The value-free ideal for science holds that values should not influence the core features of scientific reasoning. We defend the difference-to-inference model of value-permeation, which holds that value-permeation in science is problematic when values make a difference to the inferences made about a hypothesis. This view of value-permeation is superior to existing views, and it suggests a corresponding maxim—namely, that scientists should strive to eliminate differences to inference. This maxim is the basis of a novel value-free ideal for (...) science. -/- . (shrink)

I introduce a new formulation of rule consequentialism, defended as an improvement on traditional formulations. My new formulation cleanly avoids what Parfit calls “ideal world” objections. I suggest that those objections arise because traditional formulations incorporate counterfactual comparisons about how things could go differently. My new formulation eliminates those counterfactual comparisons. Part of the interest of the new formulation is as a model of how to reformulate structurally similar views, including various kinds of contractualism.

Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue (...) that proper evaluation ofmodels does not depend on whether they target real biological agents or not; instead, the standards of evaluation depend on the use of models rather than on the reality of their targets. I discuss how models are validated depending on their use and argue that all-encompassing benchmarks for models may be well beyond reach. (shrink)

G. W. Leibniz developed a new model for rational decision-making which is suited to complicated decisions, where goods do not rule each other out, but compete with each other. In such cases the deliberator has to consider all of the goods and pick the ones that contribute most to the desired goal which in Leibniz’s system is ultimately the advancement of universal perfection. The inclinations to particular goods can be seen as vectors leading to different directions much like forces in (...) Leibniz’s dynamics. The vectorial model of rational decision-making is related to Leibniz’s work with metaphysical physics and the calculus of variations and is a heuristic tool which helps in finding reasonable combinations – in ideal cases optimums - between competing goods. By applying the model, the decision-maker can map and compare outcomes of combinations of goods in question and practice a kind of pseudo-mechanical arithmetic of reasons. A central feature of the model is the possibility to employ geometrical figures to help the conceptualization. In this paper I present the vectorial model, examine its applications in practical cases from political theory, jurisprudence and ethics Leibniz presented, and compare the model to recent theories of acting under uncertainty, such as bounded rationality and optimizing under constraints. (shrink)

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can be seriously (...) considered as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections. (shrink)

The focus in the literature on scientific explanation has shifted in recent years towards modelbased approaches. The idea that there are simple and true laws of nature has met with objections from philosophers such as Nancy Cartwright (1983) and Paul Teller (2001), and this has made a strictly Hempelian D-N style explanation largely irrelevant to the explanatory practices of science (Hempel & Oppenheim, 1948). Much of science does not involve subsuming particular events under laws of nature. It is increasingly recognized (...) that science across the disciplines is to some degree a patchwork of scientific models, with different methods, strategies, and with varying degrees of successful prediction and explanation. And so accounts of scientific explanation have reflected this change of perspective and model-based approaches have flourished in the explanation literature (Batterman, 2002b; Bokulich, 2008; Craver, 2006; Woodward, 2003). (shrink)

Knill, Kersten, & Mamassian (Chapter 6) provide an interesting discussion of how the Bayesian formulation can be used to help investigate human vision. In their view, computational theories can be based on an ideal observer that uses Bayesian inference to make optimal use of available information. Four factors are important here: the image information used, the output structures estimated, the priors assumed (i.e., knowledge about the structure of the world), and the likelihood function used (i.e., knowledge about the projection (...) of the world onto the sensors). Knill & Kersten argue that such a framework not only helps analyze a perceptual task, but can also help investigators to define it. Two examples are provided (the interpretation of surface contour and the perception of moving shadows) to show how this approach can be used in practice. As the authors admit, most (if not all) perceptual processes are ill-suited to a "strong" Bayesian approach based on a single consistent model of the world. Instead, they argue for a "weak" variant that assumes Bayesian inference to be carried out in modules of more limited scope. But how weak is "weak"? Are such approaches suitable for only a few relatively low-level tasks, or can they be applied more generally? Could a weak Bayesian approach, for example, explain how we would recognize the return of Elvis Presley? The formal modelling of human perception To help get a fix on things, it is useful to examine the fate of an earlier attempt to formalize human perception: the application of information theory. It was once hoped that this theory—a close cousin of the Bayesian formulation—would provide a way to uncover information-handling laws that were largely independent of physical implementation. In this approach, the human nervous system was assumed to have.. (shrink)

It is sometimes argued that ideal theories in political philosophy are a form of ideology. This article examines arguments building on the work of Charles Mills and Raymond Geuss for the claim that ideal theories are cognitively distorting belief systems that have the effect of stabilizing unjust social arrangements. I argue that Mills and Geuss neither succeed in establishing that the content of ideal theories is necessarily cognitively defective in the way characteristic for ideologies, nor can they (...) make plausible which mechanisms ensure the alleged negative effects of the widespread acceptance of ideal theorizing. This does not mean that all hope is lost for the ideology objection, however. By turning to a second Marxian model of ideology, I argue that the ideological character of ideal theories is not so much a matter of their content, but rather of their form. Ideal theories falsely present the normative concepts that they use as semantically practice-independent and thereby block potential challenges from subordinate groups to dominant ideologies. It is therefore not the normative content of ideal theories which proves to be objectionable, but the particular role their concepts play in wider political discourse. (shrink)

It has often been argued that charitable donations are not a sufficient response to global poverty; individuals need to address structural injustice. Proponents of the Effective Altruism (EA) movement have raised two main problems with this focus on structural injustice. In this paper, we respond to these concerns. The first problem raised by EA proponents is that focusing on structural injustice absolves individuals of any responsibility other than political ones. In response, we argue that discharging this duty requires more commitment (...) than EA defenders think, and we do so by framing individual responsibility in global structural injustice through the lens of Robin Zheng’s Role-Ideal Model (RIM). The second response given by EA proponents is that a focus on structural injustice does not provide concrete ways for any given individual to discharge such duties. To address this worry, we argue that RIM can be complemented with the Rawlsian account of moral maturation. This new framework makes it clear how individuals can form the right concept of justice and become responsible citizens who act in accordance with RIM. (shrink)

This article explains Kant’s claim that sciences must take, at least as their ideal, the form of a ‘system’. I argue that Kant’s notion of systematicity can be understood against the background of de Jong & Betti’s Classical Model of Science (2010) and the writings of Georg Friedrich Meier and Johann Heinrich Lambert. According to my interpretation, Meier, Lambert, and Kant accepted an axiomatic idea of science, articulated by the Classical Model, which elucidates their conceptions of systematicity. I show (...) that Kant’s critique of the mathematical method is compatible with his adherence to this axiomatic conception of science. I further show that systematicity furthers traditionally accepted logical ideals of scientific knowledge, which explains why Meier and Kant think that sciences must be ‘systematic’. (shrink)

The terms “model” and “model-building” have been used to characterize the field of formal philosophy, to evaluate philosophy’s and philosophical logic’s progress and to define philosophical logic itself. A model is an idealization, in the sense of being a deliberate simplification of something relatively complex in which several important aspects are left aside, but also in the sense of being a view too perfect or excellent, not found in reality, of this thing. Paraconsistent logic is a branch of philosophical logic. (...) It is however not clear how paraconsistent logic can be seen as model-building. What exactly is modeled? In this paper I adopt the perspective of looking at a particular instance of paraconsistent logic—paranormal modal logic—which might be seen as a model of a specific kind of agent: inductive agents. After ntroducing what I call the highlevel and low-level models of inductive agents, I analyze the extent to which the above-mentioned idealizing features of model-building appear in paranormal modal logic and how they affect its philosophical significance. (shrink)

Recent philosophy of science has witnessed a shift in focus, in that significantly more consideration is given to how scientists employ models. Attending to the role of models in scientific practice leads to new questions about the representational roles of models, the purpose of idealizations, why multiple models are used for the same phenomenon, and many more besides. In this paper, I suggest that these themes resonate with central topics in feminist epistemology, in particular prominent versions (...) of feminist empiricism, and that model-based science and feminist epistemology each has crucial resources to offer the other's project. (shrink)

In this paper, we present an expansion of one of the well-known classical inventory management models, which is the static model of inventory management without a deficit and for a single substance, based on the neutrosophic logic, where we provide through this study a basis for dealing with all data, whether specific or undefined in the field of inventory management, as it provides safe environment to manage inventory without running into deficit , and give us an approximate ideal (...) volume of inventory. Since the ideal size is affected by the rate of demand for inventory, we present in this paper a study of the rate of demand for inventory when it is not precisely refined, and we find that the indeterminate values of the demand rate cannot be ignored, because they actually affect determining the ideal size of inventory and calculating its costs, thus affecting on the efficiency of the facility and achieve great profits for it. (shrink)

Many have expected that understanding the evolution of norms should, in some way, bear on our first-order normative outlook: How norms evolve should shape which norms we accept. But recent philosophy has not done much to shore up this expectation. Most existing discussions of evolution and norms either jump headlong into the is/ought gap or else target meta-ethical issues, such as the objectivity of norms. My aim in this paper is to sketch a different way in which evolutionary considerations can (...) feed into normative thinking—focusing on stability. I will discuss two forms of argument that utilize information about social stability drawn from evolutionary models, and employs it to assess claims in political philosophy. One such argument treats stability as feature of social states that may be taken into account alongside other features. The other uses stability as a constraint on the realization of social ideals, via a version of the ought-implies-can maxim. These forms of argument are not new; indeed they have a history going back at least to early modern philosophy. But their marriage with evolutionary information is relatively recent, has a significantly novel character, and has received little attention in recent moral and political philosophy. (shrink)

-/- This essay discusses the relation between ideal theory and two forms of political moralism identified by Bernard Williams, structural and enactment views. It argues that ideal theory, at least in the sense Rawls used that term, only makes sense for structural forms of moralism. These theories see their task as describing the constraints that properly apply to political agents and institutions. As a result, they are primarily concerned with norms that govern action. In contrast, many critiques of (...) ideal theory are structured and motivated by their commitment to an enactment model of political theorizing. This instead sees political agents and institutions as instruments for producing or promoting better states of affairs. Enactment models treat the evaluations that rank different states of affairs as justificatorily basic, rather than norms governing action on which structural models focus. This reveals an important feature of debates about ideal theory. Whether ideal theory is capable of appropriately guiding action will depend on what the criteria for appropriately guiding action are, about which different theorists have importantly different views. For example, some popular strategies for defending ideal theory fail, while it may be much less clear that some alternatives to ideal theory can provide action guidance than their advocates claim. (shrink)

This paper argues that specific phobia is an ideal kind of psychiatric disorder because it bears the marks of a mature medical diagnosis and is amenable to causal explanation. A new and ambitious program of ‘causal revolution’ has recently emerged in psychiatry that hopes to refurnish our taxonomies by discovering the underlying biological and psychological causes that create and maintain mental illness. I show that the sort of causal story envisioned by the program is a mechanistic property cluster (MPC) (...) structure, which involves a causal mechanism that explains the co-occurrence of a disorder’s signs and symptoms (Kendler, Zachar & Craver, 2011). I then build a model of fear in humans and sketch a novel account of specific phobia as a configuration of the fear system in thrall to deregulated network dynamics such as hysteresis, tipping points, and feedback loops. Specific phobia has an MPC structure. I close by reflecting on whether we can reasonably expect other mental disorders to fit an MPC mold, and thus lend themselves to future causal validation. This paper shows that specific phobia holds a unique place in our picture of mental disorder that has so far been missed. It is an ideal kind of psychopathology. (shrink)

This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and (...) these relations are coordinated by a model commentary. Surrogate models are often about single mechanisms. They are distinguishable from substitute models, which are examined without any concern about their connections with the target. Models as credible worlds are surrogate models that are believed to provide access to their targets on account of their credibility (of which a few senses are distinguished). (shrink)

This article joins a growing body of scholarship on political parties in arguing that democratic party politics should be concerned with avoiding the problem of factionalism. But existing responses to this problem have relied too heavily on conceptual distinctions between parties and factions. The dangers of factionalism cannot, in practice, be separated from the celebrated benefits of party politics. Both are rooted in the same aspects of social psychology. Consequently, I argue that we should understand the ideal of non-factionalism (...) as the management of a set of chronic and ineradicable concerns about how group conflict can undermine the specific forms of political unity appropriate for a liberal democracy. This ideal is negative: focused on avoiding pathological forms of party politics rather than conforming to a particular positive model. It is also plural, encompassing several related normative concerns. And it is systemic - assessed at the level of party systems. (shrink)

Peirce considered his esthetics to be one of a trio of normative sciences. Ostensibly, the sciences of logic, ethics and esthetics, would study the traditional norms of truth, goodness and beauty. Logic was normative in the sense that it studied how people ought to reason, if truth is to be the result. Similarly, ethics is the study of how we ought to conduct ourselves, if good is to happen. At the same time, Peirce seems to have difficulty fitting the study (...) of the beautiful into this sort of normative framework. As Peirce says, esthetics was handicapped by its definition as a theory of beauty. Instead, Peirce sees esthetics as bound up with a study of ends. Peirce argues that ends are essential to normative behavior, understood as the deliberate pursuit of a purpose, the latter serving to direct and correct the behavior. Ends may be ideals, ideas, standards, or goals. In this regard, the study of esthetics is bound up with what ought to be pursued. The key problem of the normative sciences is to discover which ends ought to be pursued. In Peirce’s formative accounts, he seems to waver between assigning this task to ethics at times and esthetics otherwise. In some places, he argues that ethics defines the ends to be pursued and studies the summum bonum. On the other hand, esthetics is claimed to be the study of ideals, that which is objectively admirable, and the conditions of attractive and repulsive ideas. To that extent, Peirce claims in a number of places that the ethically good rests on a notion of the esthetically good, the morally good being a species of the esthetically good. Although Peirce notes his neglect of the study of esthetics, he does provide a preliminary study of ends, which prove to be instructive in getting a sense of what he counts as admirable to pursue. I argue that besides this positive esthetics, which identifies ends and the conditions which makes them worthy of pursuit, he also suggests a negative esthetics, one that is does not work backwards from the ideal to the means to achieve it, but sees ends as something that evolves forward as human endeavor attempts to correct those pursuits that have led to error and misdirection. Peirce’s negative esthetics is modelled after the way in which he accounts for the ability of science to self-correct away from errant hypotheses to ones less subject to error, converging toward the truth. In the same way, the pursuit of ends is a matter of converging toward the right end through self-correction. (shrink)

From Plato to the present day, political theorists have used models of idealistic societies to think about politics. How can these idealistic models inform our thinking about political life in our non-ideal world? Not, as many political theorists have hoped, by providing normative guidance -- by showing us how things should be or where we should go. Even still, we can use these models to interpret the concepts we depend on to explain and evaluate political behavior (...) and institutions, thereby sharpening our explanations of how things work and our estimations of how to make things better. Seeing idealistic models in this light reveals a promising route to a form of political inquiry that integrates our explanatory aims and our normative aspirations. -/- (Updated: October 2024. The book is in production for publication by Oxford University Press, to appear in the Philosophy, Politics, and Economics series. I have uploaded the table of contents, chapter 1, and the bibliography.). (shrink)

The underrepresentation of women, people of color, and especially women of color—and the corresponding overrepresentation of white men—is more pronounced in philosophy than in many of the sciences. I suggest that part of the explanation for this lies in the role played by the idealized rational self, a concept that is relatively influential in philosophy but rarely employed in the sciences. The idealized rational self models the mind as consistent, unified, rationally transcendent, and introspectively transparent. I hypothesize that acceptance (...) of the idealized rational self leads philosophers to underestimate the influence of implicit bias on their own judgments and prevents them from enacting the reforms necessary to minimize the effects of implicit bias on institutional decision-making procedures. I consider recent experiments in social psychology that suggest that an increased sense of one’s own objectivity leads to greater reliance on bias in hiring scenarios, and I hypothesize how these results might be applied to philosophers’ evaluative judgments. I discuss ways that the idealized rational self is susceptible to broader critiques of ideal theory, and I consider some of the ways that the picture functions as a tool of active ignorance and color-evasive racism. (shrink)

With the fast pace of AI development, the problem of preventing its global catastrophic risks arises. However, no satisfactory solution has been found. From several possibilities, the confinement of AI in a box is considered as a low-quality possible solution for AI safety. However, some treacherous AIs can be stopped by effective confinement if it is used as an additional measure. Here, we proposed an idealized model of the best possible confinement by aggregating all known ideas in the field of (...) AI boxing. We model the confinement based on the principles used in the safety engineering of nuclear power plants. We show that AI confinement should be implemented in several levels of defense. These levels include 1) AI design in fail-safe manner 2) limiting its capabilities, preventing self-improving and circuit breakers on treacherous turn 3) isolation from the outside world and, lastly, as the final hope 4) outside measures oriented on stopping AI in the wild. We demonstrate that the substantial number (more than 50 ideas listed in the article) of mutually independent measures could provide a relatively high probability of the containment of a human-level AI but may be not sufficient to preserve runaway of superintelligent AI. Thus, these measures will work only if they are used to prevent superintelligent AI creation, but not for containing superintelligence. We suggest that there could be a safe operation threshold, on which AI is useful, but is not able to hack containment system from the inside, the same way as a safe level of chain reaction inside nuclear power plants is maintained. However, overall, a failure of the confinement is inevitable, so we need to use the full AGI limited number of times (AI-ticks). (shrink)