The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what exactly they mean by robustness. In this paper, we provide a conceptual analysis of the term robustness, with the aim to develop a common language, that allows us to weave together different strands of robustness (...) research. We define robustness as the relative stability of a robustness target with respect to specific interventions on a modifier. Our account captures the various sub-types of robustness that are discussed in the research literature, including robustness to distribution shifts, prediction robustness, or the robustness of algorithmic explanations. Finally, we delineate robustness from adjacent key concepts in ML, such as extrapolation, generalization, and uncertainty, and establish it as an independent epistemic concept. (shrink)

In a recent paper, I argued that philosophical intuitions are surprisingly robust both across demographic groups and across development. Machery and Stich reply by reviewing a series of studies that do show significant differences in philosophical intuition between different demographic groups. This is a helpful point, which gets at precisely the issues that are most relevant here. However, even when one looks at those very studies, one finds truly surprising robustness. In other words, despite the presence of statistically significant (...) differences between demographic groups, a core finding coming out of those studies is that philosophical intuitions are surprisingly robust across demographic groups. (shrink)

We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt's account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is (...) usually difficult to tell which idealisations are truly harmful. (shrink)

The Cold Dark Matter model faces many controversies at small scales, as simulations fail to reproduce the observed properties of dark matter halos. Since rival DM models differ on their predic...

Featuring contributions from the world’s leading experts on the subject and based partly on several detailed case studies, this volume is the first comprehensive analysis of the scientific notion of robustness as well as of the general ...

Computer simulations are nowadays often directly involved in the generation of experimental results. Given this dependency of experiments on computer simulations, that of simulations on models, and that of the models on free parameters, how do researchers establish trust in their experimental results? Using high-energy physics (HEP) as a case study, I will identify three different types of robustness that I call conceptual, methodological, and parametric robustness, and show how they can sanction this trust. However, as I will (...) also show, simulation models in HEP themselves fail to exhibit a type of robustness I call inverse parametric robustness. This combination of robustness and failures thereof is best understood by distinguishing different epistemic capacities of simulations and different senses of trust: Trusting simulations in their capacity to facilitate credible experimental results can mean accepting them as means for generating belief in these results, while this need not imply believing the models themselves in their capacity to represent an underlying reality. (shrink)

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of (...) class='Hi'>robustness analysis has been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation. (shrink)

Robustness is a common platitude: hypotheses are better supported with evidence generated by multiple techniques that rely on different background assumptions. Robustness has been put to numerous epistemic tasks, including the demarcation of artifacts from real entities, countering the “experimenter’s regress,” and resolving evidential discordance. Despite the frequency of appeals to robustness, the notion itself has received scant critique. Arguments based on robustness can give incorrect conclusions. More worrying is that although robustness may be valuable (...) in ideal evidential circumstances (i.e., when evidence is concordant), often when a variety of evidence is available from multiple techniques, the evidence is discordant. †To contact the author, please write to: Jacob Stegenga, Department of Philosophy, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093; e‐mail: [email protected]. (shrink)

A number of philosophers of science have argued that there are important differences between robustness in modeling and experimental contexts, and—in particular—many of them have claimed that the former is non-confirmatory. In this paper, I argue for the opposite conclusion: robust hypotheses are confirmed under conditions that do not depend on the differences between and models and experiments—that is, the degree to which the robust hypothesis is confirmed depends on precisely the same factors in both situations. The positive argument (...) turns on the fact that confirmation theory doesn’t recognize a difference between different sources of evidence. Most of the paper is devoted to rebutting various objections designed to show that it should. I end by explaining why philosophers of science have (often) gone wrong on this point. (shrink)

Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential (...) of minimal models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models can confirm, but its confirmatory value depends on concrete circumstances. (shrink)

Robustness is often presented as a guideline for distinguishing the true or real from mere appearances or artifacts. Most of recent discussions of robustness have focused on the kind of derivational robustness analysis introduced by Levins, while the related but distinct idea of robustness as multiple accessibility, defended by Wimsatt, has received less attention. In this paper, I argue that the latter kind of robustness, when properly understood, can provide justification for ontological commitments. The idea (...) is that we are justified in believing that things studied by science are real insofar as we have robust evidence for them. I develop and analyze this idea in detail, and based on concrete examples show that it plays an important role in science. Finally, I demonstrate how robustness can be used to clarify the debate on scientific realism and to formulate new arguments. (shrink)

Robustness arguments hold that hypotheses are more likely to be true when they are confirmed by diverse kinds of evidence. Robustness arguments require the confirming evidence to be independent. We identify two kinds of independence appealed to in robustness arguments: ontic independence —when the multiple lines of evidence depend on different materials, assumptions, or theories—and probabilistic independence. Many assume that OI is sufficient for a robustness argument to be warranted. However, we argue that, as typically construed, (...) OI is not a sufficient independence condition for warranting robustness arguments. We show that OI evidence can collectively confirm a hypothesis to a lower degree than individual lines of evidence, contrary to the standard assumption undergirding usual robustness arguments. We employ Bayesian networks to represent the ideal empirical scenario for a robustness argument and a variety of ways in which empirical scenarios can fall short of this ideal. (shrink)

In this essay, I argue that uneliminated idealizations pose a serious problem for scientific realism. I consider one method for “de-idealizing” models—robustness analysis. However, I argue that unless idealizations are eliminated from an idealized theory and robustness analysis need not do that, scientists are not justified in believing that the theory is true. I consider one example of modeling from the biological sciences that exemplifies the problem.

When scientists seek further confirmation of their results, they often attempt to duplicate the results using diverse means. To the extent that they are successful in doing so, their results are said to be robust. This paper investigates the logic of such "robustness analysis" [RA]. The most important and challenging question an account of RA can answer is what sense of evidential diversity is involved in RAs. I argue that prevailing formal explications of such diversity are unsatisfactory. I propose (...) a unified, explanatory account of diversity in RAs. The resulting account is, I argue, truer to actual cases of RA in science; moreover, this account affords us a helpful, new foothold on the logic undergirding RAs. (shrink)

In the philosophical literature concerning scientific experimentation, the notion of robustness has been solely discussed in relation to experimental results. In this paper, I propose a novel sense of experimental robustness that applies to experimental procedures. I call the foregoing sense of robustness procedure robustness and characterize it as the capacity of an experimental procedure to maintain its intended function invariant during the experimental process despite possible variations in its inputs. I argue that PR is a (...) precondition for what I call result robustness, which refers to the traditional sense of experimental robustness, namely the existence of convergent experimental results obtained through different and independent means of detection. Furthermore, I argue, PR and RR constitute useful experimental strategies in the context of high-energy physics experiments, but these strategies are not without limitations. (shrink)

We argue that the robustness analysis of idealized models can have confirmational power. This responds to concerns recently raised in the literature, according to which the robustness analysis of models whose idealizations are not discharged is unable to confirm the causal mechanisms underlying these models, and the robustness analysis of models whose idealizations are discharged is unnecessary. In response, we make clear that, where idealizations sweep out, in a specific way, the space of possibilities— which is sometimes, (...) though not always, the case—they can be holistically discharged. In turn, this can be used to show that the robustness analysis of idealized models can have confirmational force after all. (shrink)

This paper distinguishes between causal isolation robustness analysis and independent determination robustness analysis and suggests that the triangulation of the results of different epistemic means or activities serves different functions in them. Circadian clock research is presented as a case of causal isolation robustness analysis: in this field researchers made use of the notion of robustness to isolate the assumed mechanism behind the circadian rhythm. However, in contrast to the earlier philosophical case studies on causal isolation (...) robustness analysis (Weisberg and Reisman in Philos Sci 75:106–131, 2008 ; Kuorikoski et al. in Br J Philos Sci 61:541–567, 2010 ), robustness analysis in the circadian clock research did not remain in the level of mathematical modeling, but it combined it with experimentation on model organisms and a new type of model, a synthetic model. (shrink)

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I (...) present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists’ discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. (shrink)

Multiple realization has traditionally been characterized as a thesis about the relation between kinds posited by the taxonomic systems of different sciences. In this paper, I argue that there are good reasons to move beyond this framing. I begin by showing how the traditional framing is tied to positivist models of explanation and reduction and proceed to develop an alternate framing that operates instead within causal explanatory frameworks. I draw connections between this account and the notion of functional robustness (...) in biology and neuroscience. I then examine two cases from systems neuroscience that substantiate my account and show how traditional debates fail to track important features of these cases. (shrink)

The principle-based approach is a methodology to classify and analyse argumentation semantics. In this paper we classify seven of the main alternatives for argumentation semantics using a set of new robustness principles. These principles complement Baroni and Giacomin’s original classification and deal with the behaviour of a semantics when the argumentation framework changes due to the addition or removal of an attack between two arguments. We distinguish so-called persistence principles and monotonicity principles, where the former deal with the question (...) of whether a labelling or extension of an argumentation framework under a given semantics persists after a change, and the latter with the question of whether new labellings or extensions are created after a change. We furthermore show in which sense labelling-based and extension-based semantics lead to subtly different principles and results. Our results can be used for choosing a semantics for a particular application, or to guide the search for new argumentation semantics, but they have been used also in the design of algorithms. (shrink)

The AGI alignment problem has a bimodal distribution of outcomes with most outcomes clustering around the poles of total success and existential, catastrophic failure. Consequently, attempts to solve AGI alignment should, all else equal, prefer false negatives (ignoring research programs that would have been successful) to false positives (pursuing research programs that will unexpectedly fail). Thus, we propose adopting a policy of responding to points of philosophical and practical uncertainty associated with the alignment problem by limiting and choosing necessary assumptions (...) to reduce the risk of false positives. Herein we explore in detail two relevant points of uncertainty that AGI alignment research hinges on---meta-ethical uncertainty and uncertainty about mental phenomena---and show how to reduce false positives in response to them. (shrink)

In this chapter, Odenbaugh first provides a conceptual framework for thinking about climate modeling, specifically focused on general circulation models. Second, he considers what makes models independent of one another. Third, he shows robustness analysis, which depends on models being independent of one another, can be used to remove doubts about idealizations in general climate models. Finally, he considers a dilemma for robustness analysis; namely, it leads to either an infinite regress of idealizations or a complete removal of (...) idealizations. A response to the dilemma is given defending a form of epistemic contextualism and by drawing a distinction between relative and absolute robustness. (shrink)

This paper analyses when, and to what extent, the robustness of a result yields confirmation. I develop two inferential rules that specify how modellers and experimenters should update their conditional probabilities when new derivational or experimental information becomes available. While similar rules apply to derivational and experimental robustness, they are insufficient on their own to generate empirical confirmation from derivational robustness. That requires suitable indirect confirmation relations linking model results to empirical evidence. I examine several such relations (...) and show how robustness can increase confirmation by demonstrating that both an empirically validated result and a model prediction depend on the same components, while certain false auxiliaries are irrelevant. When empirical confirmation arises from derivational robustness, it does so by strengthening these indirect links rather than by securing a high absolute probability for a robust theorem. The resulting account clarifies both the scope and limits of robust confirmatory reasoning. (shrink)

Robustness is a fundamental property of biological systems, observed ubiquitously across species and at different levels of organization from gene regulation to ecosystem. The theory of biological robustness argues that robustness fosters evolv-ability and that together they entail various tradeoffs as well as characteristic architectures and mechanisms. We argue that classes of biological systems have evolved to enhance their robustness by extending their system boundary through a series of symbioses with foreign biological entities . A series (...) of major biological innovations has been achieved by events consistent with this framework: horizontal gene transfer, serial endosymbiosis, oocytes-mediated vertical infection, and host-symbiont mutualism for bacterial flora. Self-extending symbiosis contributes to robustness because symbiotic foreign biological entities can enhance the adaptative capacity of the system against environmental perturbations as well as contribute novel functions. In addition, evolutionary history indicates that the degree of symbiosis achieved has substantially changed from tight integration into the genome to loose integration as bacterial flora . The most dramatic example can be seen in the symbiosis of host immune system and bacterial flora in which substantial function of host defense depends on the proper maintenance of bacterial flora and its adaptive capability. Biological systems following this type of evolutionary path might have attained high levels of functionality, robustness, and evolvability. Thus, robustness, evolution, and self-extending symbiosis may form essential system principles for biology. (shrink)

Legal large language models (LLMs) deployed in high-stakes judicial settings must exhibit robustness against non-substantive linguistic variations while preserving acute sensitivity to legally determinative facts and norms. This study investigates this robustness–reliability trade-off within the context of Chinese legal tasks. We curate a dataset of 5,000 Chinese judicial question–answer pairs and generate semantic-preserving adversarial rewrites, retaining only those validated by an embedding-based semantic consistency filter. Holding the total training budget and fine-tuning protocol constant, we fine-tune model variants that (...) differ exclusively in their injection ratio of these verified rewrites, establishing seven distinct injection groups (G0–G6). We evaluate model reliability utilizing a composite protocol that incorporates objective accuracy on exam-style questions, expert evaluations of open-ended responses, and embedding-based semantic similarity. For trademark infringement reasoning tasks, we additionally assess verdict accuracy and rationale quality. Across varying model capacities (4B, 20B, and 32B backbones, including Qwen3-4B, GPT-OSS-20B, and Qwen3-VL-32B-Instruct) and both evaluated tasks, our findings reveal an inverted‑U relationship: moderate robustness data injection enhances reliability, whereas excessive injection degrades overall performance and induces characteristic failure modes, such as the attenuation of legally salient distinctions, the generation of boilerplate rationales, and overly cautious abstention. These findings substantiate “moderate robustness injection” as a practical heuristic and underscore a broader principle of differential sensitivity—achieving insensitivity to superficial variations without blunting the model’s sensitivity to legally decisive elements. (shrink)

Precaution is a relevant and much-invoked value in environmental risk analysis, as witnessed by the ongoing vivid discussion about the precautionary principle (PP). This article argues (i) against purely decision-theoretic explications of PP; (ii) that the construction, evaluation, and use of scientific models falls under the scope of PP; and (iii) that epistemic and decision-theoretic robustness are essential for precautionary policy making. These claims are elaborated and defended by means of case studies from climate science and conservation biology.

Generics are sentences that express generalizations about a category or about its members. They display a characteristic context-sensitivity: the same generic can express a statistical regularity, a principled connection, or a norm. Sally Haslanger (2014) argues that this phenomenon depends on the implicit content that generics carry in different contexts. -/- I elaborate on Haslanger’s proposal, arguing that the implicit content of generics is complex and constituted by two different propositions. A first proposition, that I here call the robustness (...) proposition, characterizes as robust the link between the category the generic is about and the predicated property. This proposition is relatively invariant and is, as I claimed elsewhere, a generalized conversational implicature. In this paper, I will argue that a second implicature, the ‘explanatory implicature’, arises which crucially depends on what explanation is called for in a certain context. Given its context-dependence, I conclude that this proposition is a particularized conversational implicature. While generics convey by default that the category and the property are strictly related, the specification of this relation hinges on the characteristics of the context in which the generic occurs. (shrink)

In robustness analysis, hypotheses are supported to the extent that a result proves robust, and a result is robust to the extent that we detect it in diverse ways. But what precise sense of diversity is at work here? In this paper, I show that the formal explications of evidential diversity most often appealed to in work on robustness – which all draw in one way or another on probabilistic independence – fail to shed light on the notion (...) of diversity relevant to robustness analysis. I close by briefly outlining a promising alternative approach inspired by Horwich’s (1982) eliminative account of evidential diversity. (shrink)

It is still a matter of controversy whether the Principle of the Common Cause (PCC) can be used as a basis for sound causal inference. It is thus to be expected that its application to quantum mechanics should be a correspondingly controversial issue. Indeed the early 90’s saw a flurry of papers addressing just this issue in connection with the EPR correlations. Yet, that debate does not seem to have caught up with the most recent literature on causal inference generally, (...) which has moved on to consider the virtues of a generalised PCC inspired condition, the so called Causal Markov Condition (CMC). In this paper we argue that the CMC is an appropriate benchmark for debating possible causal explanations of the EPR correlations. But we go on to take issue with some pronouncements on EPR by defenders of the CMC. (shrink)

Talk of ‘robustness’ remains vague, despite the fact that it is clearly an important parameter in evaluating models in general and game-theoretic results in particular. Here we want to make it a bit less vague by offering a graphic measure for a particular kind of robustness— ‘matrix robustness’— using a three dimensional display of the universe of 2 x 2 game theory. In a display of this form, familiar games such as the Prisoner’s Dilemma, Stag Hunt, Chicken (...) and Deadlock appear as volumes, making comparison easy regarding the extent of different game-theoretic effects. We illustrate such a comparison in robustness between the triumph of Tit for Tat in a spatialized environment (Grim 1995, Grim, Mar, and St. Denis 1998) and a spatialized modeling of the Contact Hypothesis regarding prejudice reduction (Grim, et. al 2005a, 2005b). The geometrical representation of relative robustness also offers a possibility for links between geometrical theorems and results regarding robustness in game theory. (shrink)

This chapter discusses two different issues about the relationship between legal positivism and robust normativity (understood as the most authoritative kind of normativity to which we appeal). First, the chapter argues that, in many contexts when discussing “legal positivism” and “legal antipositivism”, the discussion should be shifted from whether legal facts are ultimately partly grounded in moral facts to whether they are ultimately partly grounded in robustly normative facts. Second, the chapter explores an important difference within the kinds of arguments (...) that legal philosophers give for the (purported) truth of legal positivism. The difference concerns whether (purportedly) robustly normative facts are appealed to as premises in those arguments or not. (A closely connected issue is whether (purportedly) normative facts that bear one or more important connections to robustly normative facts are appealed to in premises to those arguments.) The chapter argues that thinking about this dividing line helps people better situate the positivist/antipositivist dispute, better understand the space of views in legal philosophy, better evaluate those views, and avoid having merely verbal disputes. (shrink)

All economic models involve abstractions and idealisations. Economic theory itself does not tell which idealizations are truly fatal or harmful for the result and which are not. This is why much of what is seen as theoretical contribution in economics is constituted by deriving familiar results from different modelling assumptions. If a modelling result is robust with respect to particular modelling assumptions, the empirical falsity of these particular assumptions does not provide grounds for criticizing the result. In this paper we (...) demonstrate how derivational robustness analysis does carry epistemic weight and answer criticism concerning its non-empirical nature and the problematic form of the required independence of the ways of derivation. The epistemic rationale and importance of robustness analysis also challenge some common conceptions of the role of theory in economics. (shrink)

Anchoring occurs when a quantitative estimate is biased toward an initially presented value (the anchor). Anchoring occurs both in high‐level explicit estimation of numeric quantities and in lower‐level perceptual tasks and persists even when reliable information about the quantity being estimated is directly available at the point of judgment. This suggests anchoring might derive from generic processing underpinning estimation. Such tasks, however, are almost exclusively lab‐based, and the information required to complete the task is rarely available to the participant in (...) a way that reflects how such information is sampled in real‐life. To address this, across two experiments, we immersed participants in a virtual world on a platform that could be placed at any height. After an anchor was presented, participants subsequently estimated their height naturally, by interpreting sensory and cognitive cues from the 3D environment in which they were immersed. We manipulated the amount of information directly available to the participant for making their judgment as well as the offset between the anchor and the actual height. To modulate the extent to which task‐relevant information was acquired naturally from the environment, we also contrasted anchoring effects in and out of VR. Although participants clearly used the available perceptual and cognitive information to make height estimates, anchoring effects were evident, displayed similar properties to those reported in previous lab‐based studies, and were consistent both in and out of VR. Our design also allowed us to recover a novel subjective anchoring measure that facilitated a particularly parsimonious descriptive model of our anchoring data. We conclude that anchoring is a generic feature of all estimation tasks, even when task‐relevant information is acquired naturally, as in real‐life estimation. These results emphasize the potential for this cognitive bias to have a significant impact on performance in any real‐world task requiring quantitative estimation. (shrink)

Scientists often use multiple independent methods of identification to distinguish reliable results from those produced in error. This process is referred to as ‘robustness analysis’. I argue that even though robustness analysis is useful for differentiating natural phenomena from artifacts, it fails to differentiate experimentally produced effects from artifacts. I argue that to bypass this problem, we can re-frame the role of robustness analysis to focus on cross-comparison between methods of production. Focusing on the production relation provides (...) information about how changes in conditions alter given effects, without first having to make a distinction between effect and artifact. (shrink)

Intractability and optimality are two sides of one coin: Optimal models are often intractable, that is, they tend to be excessively complex, or NP-hard. We explain the meaning of NP-hardness in detail and discuss how modem computer science circumvents intractability by introducing heuristics and shortcuts to optimality, often replacing optimality by means of sufficient sub-optimality. Since the principles of decision theory dictate balancing the cost of computation against gain in accuracy, statistical inference is currently being reshaped by a vigorous new (...) trend: the science of simplicity. Simple models, as we show for specific cases, are not just tractable, they also tend to be robust. Robustness is the ability of a model to extract relevant information from data, disregarding noise.Recently, Gigerenzer, Todd and the ABC Research Group (1999) have put forward a collection of fast and frugal heuristics as simple, boundedly rational inference strategies used by the unaided mind in real world inference problems. This collection of heuristics has suggestively been called the adaptive toolbox. In this paper we will focus on a comparison task in order to illustrate the simplicity and robustness of some of the heuristics in the adaptive toolbox in contrast to the intractability and the fragility of optimal solutions. We will concentrate on three important classes of models for comparison-based inference and, in each of these classes, search for that to be used as benchmarks to evaluate the performance of fast and frugal heuristics: lexicographic trees, linear modes and Bayesian networks. Lexicographic trees are interesting because they are particularly simple models that have been used by humans throughout the centuries. Linear models have been traditionally used by cognitive psychologists as models for human inference, while Bayesian networks have only recently been introduced in statistics and computer science. Yet it is the Bayesian networks that are the best possible benchmarks for evaluating the fast and frugal heuristics, as we will show in this paper. (shrink)

Functional robustness refers to a system’s ability to maintain a function in the face of perturbations to the causal structures that support performance of that function. Modularity, a crucial element of standard methods of causal inference and difference-making accounts of causation, refers to the independent manipulability of causal relationships within a system. Functional robustness appears to be at odds with modularity. If a function is maintained despite manipulation of some causal structure that supports that function, then the relationship (...) between that structure and function fails to be manipulable independent of other causal relationships within the system. Contrary to this line of reasoning, I argue that functional robustness often attends feedback control, rather than failures of modularity. Feedback control poses its own challenges to causal explanation and inference, but those challenges do not undermine modularity—and indeed, modularity is crucial to grappling with them. (shrink)

J. L. Mackie argued that moral thought and discourse involve commitment to an especially robust kind of normativity, which is too weird to exist. Thus, he concluded that moral thought and discourse involve systematic error. Much has been said about this argument in the last four decades or so. Nevertheless, at least one version of Mackie’s argument, specifically the one focusing on the intrinsic weirdness of the relevant kind of normativity, has not been fully unpacked. Thus, more needs to be (...) said about the issue of how to interpret Mackie’s argument. Moreover, I argue that by looking closely at Mackie’s discussion, we can extract two distinct versions of the argument which together present a tougher, and also more precise, challenge for moral realism than extant versions. In this paper I thus revisit Mackie’s discussion with an eye to making progress on an important issue in contemporary metaethics. (shrink)

In the philosophy of science and epistemology literature, robustness analysis has become an umbrella term that refers to a variety of strategies. One of the main purposes of this paper is to argue that different strategies rely on different criteria for justifications. More specifically, I will claim that: i) robustness analysis differs from de-idealization even though the two concepts have often been conflated in the literature; ii) the comparison of different model frameworks requires different justifications than the comparison (...) of models that differ only for the assumption under test; iii) the replacement of specific assumptions with different ones can encounter specific difficulties in scientific practice. These claims will be supported by a case study in population ecology and a case study in geographical economics. (shrink)

The concept of performance has gradually become established in health policies. Presented as necessary and positive, it is often reduced to efficiency, which results in policies and management styles aimed at optimisation. While they are supposed to guarantee the sustainability of our healthcare systems, these practices have made them fragile. Insights from the life sciences help us understand why. Indeed, biologists observe that living beings do not prioritise optimisation but robustness. To cope with fluctuations, a robust organisation operates with (...) redundancies, apparent waste, heterogeneity, organised fluctuations, slowness, and hesitation. It functions sub‐optimally. This article offers a theoretical reflection and management directions for more robust healthcare systems. (shrink)

Robustness analysis is widespread in science, but philosophers have struggled to justify its confirmatory power. We provide a positive account of robustness by analysing some explicit and implicit uses of within and across-model robustness in evolutionary theory. We argue that appeals to robustness are usually difficult to justify because they aim to increase the likeliness that a phenomenon obtains. However, we show that robust results are necessary for explanations of phenomena with specific properties. Across-model robustness (...) is necessary for how-possibly explanations of multiply instantiated phenomena, while within-model robustness is necessary for explanations of phenomena with multiple evolutionary starting points. In such cases, the appeal of robustness is explanatory rather than confirmatory. (shrink)

What is the nature of knowledge? A popular answer to that long-standing question comes from robust virtue epistemology, whose key idea is that knowing is just a matter of succeeding cognitively—i.e., coming to believe a proposition truly—due to an exercise of cognitive ability. Versions of robust virtue epistemology further developing and systematizing this idea offer different accounts of the relation that must hold between an agent’s cognitive success and the exercise of her cognitive abilities as well as of the very (...) nature of those abilities. This paper aims to give a new robust virtue epistemological account of knowledge based on a different understanding of the nature and structure of the kind of abilities that give rise to knowledge. (shrink)

Odenbaugh and Alexandrova provide a challenging critique of the epistemic benefits of robustness analysis, singling out for particular criticism the account we articulated in Kuorikoski et al.. Odenbaugh and Alexandrova offer two arguments against the confirmatory value of robustness analysis: robust theorems cannot specify causal mechanisms and models are rarely independent in the way required by robustness analysis. We address Odenbaugh and Alexandrova’s criticisms in order to clarify some of our original arguments and to shed further light (...) on the properties of robustness analysis and its epistemic rationale. (shrink)

The relevance of longevity among the traits in dairy cattle for which genetic improvements are desirable is undisputed as welfare and economic considerations underline this importance. Longevity, again as seen in the context of many relevant traits, is a difficult trait since early selection decisions are desired but the precise longevity of an individual animal can only be derived after its death and thus at a later stage in the trajectory of time. Several approaches for modelling of longevity exist, among (...) them are survival analysis and random regression models that by definition are highly suitable to model effects along the trajectory of time. In a broader sense, the concept of longevity has to be extended to robustness and resilience. This latter term has recently gained in popularity as in general definitions are sought that reflect an individual’s ability to cope with stressors, be it individual diseases or relevant effects such as hygiene, heat stress, and energy balance. The genetic ability for a long productive life of an individual in a dairy cattle population is only inadequately described by simple measurements of longevity. In this context, cow training sets with precise recordings of diseases and other relevant effects as well as new biomarkers giving more insight into the physiological status of a cow are needed. In future dairy cattle breeding, approaches that jointly combine information on longevity, health, and resilience will be the method of choice. (shrink)