Kareem Khalifa

Building upon previous investigations of scientific realism “in the wild” – i.e., among practicing scientists – we report the results of an empirical study that examined the attitudes of scientists from physics, biology, psychology, and anthropology (N = 777) toward various issues in the scientific realism debate. Out of all the major issues that have fallen under the heading of scientific realism, we found that the mind-independence of scientific phenomena and the ideas that comprise the no-miracles argument were most central to scientists’ thinking about this cluster of issues and that ideas pertaining to instrumentalism and the pessimistic induction were least central. We also found that scientists’ judgments about the status of unobservable entities posited in their fields failed to cohere with their responses to other issues in the debate and that the fields where scientists most strongly endorsed realism on the whole were the ones that expressed the most skepticism about unobservables. Additionally, we found that scientists were significantly inclined toward realism, with natural scientists being more so than social scientists, and that this inclination was generally accompanied by a modestly antirealist endorsement of the pessimistic induction. Implications of these findings for the philosophical debate over scientific realism are considered.

In this paper, we propose a new account of understanding. Its guiding idea is that the objects that are understood have the semantic structure of questions. Understanding is achieved by grasping correct and complete answers to these questions. We develop this idea, and then argue that various kinds of propositional, objectual, and explanatory understanding are limiting cases of our “interrogative” account of understanding. In doing so, we also highlight several kinds of understanding that have received scant attention.

Thick concepts have both descriptive and evaluative dimensions to their meaning. Some have argued that because the descriptive and evaluative dimensions cannot be separated (they are “blended”), the implicit values influence the confirmation of any “mixed claims’’ containing the thick concept. Using the development of the concept of hypersegregation as a case study, we argue for a distinction between the semantic function of definitions and the epistemic function of indicators. While thick concepts are semantically blended, the evaluative meaning of a thick concept does not influence the function of epistemic indicators. Therefore, mixed claims can and should be tested impartially.

Many successful explanations show how causally individuated parts are responsible for the occurrence of the phenomena that scientists seek to explain. On this view, parts that are chosen only by convention, and related only through correlations, cannot possibly figure in successful explanations. This is because without some form of causal grounding, it seems unintelligible why any explanatory relation between these parts and the phenomenon of interest would hold. This problem is particularly pronounced in functional connectivity models (FC) in neuroscience. These models typically represent time series of recurrent neural activity in conventionally determined spatial regions (as a network’s nodes) and synchronization likelihoods among these time series (as its edges). Many neuroscientists and philosophers maintain that because of this, FC models cannot provide explanations. We formulate this problem more precisely and then show that it rests on an impoverished interpretation of scientific models in general and FC models in particular. We then provide a positive account of how FC models provide a variety of neuroscientific explanations.

Many have argued that unified theories ought to be pursued wherever possible. We deny this on the basis of social-epistemological and decision-theoretic considerations. Consequently, those seeking a more ubiquitous role for unification must either attend to the scientific community’s social structure in greater detail than has been the case, and/or radically revise their conception of unification.Son muchos los que han defendido que deberían buscarse teorías unificadas siempre que sea posible. Nosotros lo negamos a partir de consideraciones socio-epistemológicas y de teoría de la decisión. En consecuencia, aquellos que busquen un papel más omnipresente para la unificación han de prestar un mayor grado de atención a la estructura social de la comunidad científica del que se le ha prestado, y/o revisar radicalmente su concepción de la unificación.

Scientific understanding typically involves multiple specialists performing interdependent tasks. According to several social–epistemological accounts, this suggests that scientific communities are collective epistemic subjects. We argue instead that the data does not warrant the postulation of a collective subject. Our position, rather, is fictionalist: we argue that the use of sentences attributing understanding to scientific communities amounts to loose talk which is best construed as indicating how social environments associated with a scientific community promote individual scientists' understanding.

Our best social scientific theories try to tell us something about the social world. But is talk of a “social world” a metaphor that we ought not take too seriously? In particular, do the denizens of the social world—cultural values like the Protestant work ethic, firms like ExxonMobil, norms like standards of dress and behavior, institutions like the legal system, teams like FC Barcelona, conventions like marriages—exist? The question is not merely academic. Social scientists use these different social entities to explain social phenomena such as the rise of capitalism, the decline in oil prices, or the effect of unions on the sports labor market. But how could these explanations possibly work if social entities don’t exist?

The ontology of race is often seen as answering two central questions. First, do races exist? Second, if races do exist, then what are they? Consequently, determining the best methods for answering these questions falls within the metaontology of race. Within the ontology of race, it is common to select a privileged representation of race in order to draw ontological lessons. While ontological lessons are direct answers to the ontological questions raised above, privileged representations are the basis for inferring those lessons. Using the Office of Management and Budget’s racial classification as an illustration, we argue that certain social-scientific methods are better than mainstream methods at ruling out just-so stories about race, and therefore outperform more mainstream philosophical methods in determining which representations of race should be privileged. In defending this position, we also argue that the widespread philosophical practice of sharply distinguishing social-scientific from ordinary-language conceptions of race is unmotivated.

We present a novel kind of “socio-functional” foundationalism rooted in the division of scientific labor. Our foundationalism is social in that it involves a socio-epistemic phenomenon we dub epistemic outsourcing, whereby claims from one group of scientists provide epistemological foundations for another group of scientists. We argue that: (1) epistemic outsourcing results in a legitimate form of epistemic foundationalism, (2) this sort of foundationalism can be used to shed light on the epistemology of measurement; and (3) epistemic outsourcing is a distinctively collective epistemic phenomenon.

This essay presents a fully inferentialist-expressivist account of scientific representation. In general, inferentialist approaches to scientific representation argue that the capacity of a model to represent a target system depends on inferences from models to target systems. Inferentialism is attractive because it makes the epistemic function of models central to their representational capacity. Prior inferentialist approaches to scientific representation, however, have depended on some representational element, such as denotation or representational force. Brandom’s Making It Explicit provides a model of how to fully discharge such representational vocabulary, but it cannot be applied directly to scientific representations. Pursuing a strategy parallel to Brandom’s, this essay begins with an account of how surrogative inference is justified. Scientific representation and the denotation of model elements are then explained in terms of surrogative inference by treating scientific representation and denotation as expressive, analogous to Brandom’s account of truth. The result is a thoroughgoing inferentialism: M is a scientific representation of T if and only if M has scientifically justified surrogative consequences that are answers to questions about T.

In earlier work, I criticized de Regt’s contextual theory of understanding, and advertised the advantages of my own, knowledge-based account. Using the early history of the standard model in particle physics as an illustration, I instead consider the benefits of unifying these two accounts of understanding. I argue that de Regt’s account substantially improves my own account of explanatory consideration, and that my account of explanatory comparison substantially improves upon his account of explanatory evaluation. De Regt and my apparent disagreement about the so-called factivity of understanding is defused through a voluntaristic approach to the scientific realism debates.

Among epistemologists, it is common to assume that insofar as coherence bears on the justification of belief, the only relevant coherence relations are those _within_ an individual subject’s web of beliefs. After clarifying this view and exploring some plausible motivations for it, we argue that this individualistic account of the epistemic relevance of coherence fails to account for central facets of scientific practice. In its place we propose a social account of coherence. According to the view we propose, a scientist _S_’s belief that _p_ is _prima facie_ unjustified if this belief negatively coheres with justified scientific claims in her scientific community. This account of coherence yields an epistemology for scientific belief which, we argue, has all of the benefits and none of the liabilities of its more individualistic predecessor.

We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. We conclude from this that topological explanations are autonomous from their mechanistic counterparts.

We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.

Criticism is a staple of the scientific enterprise and of the social epistemology of science. Philosophical discussions of criticism have traditionally focused on its roles in relation to objectivity, confirmation, and theory choice. However, attention to criticism and to criticizability should also inform our thinking about scientific pursuits: the allocation of resources with the aim of developing scientific tools and ideas. In this paper, we offer an account of scientific pursuitworthiness which takes criticizability as its starting point. We call this the apokritic model of pursuit. Its core ideas are that pursuits are practices governed by norms for asking and answering questions, and that criticism arises from the breach of these norms. We illustrate and advertise our approach using examples from institutional grant review, neuroscience, and sociology. We show that the apokritic model can unify several indices of criticizability, that it can account for the importance of criticizing pursuits in scientific practice, and that it can offer ameliorative advice to erstwhile pursuers.

Proponents of ontic conceptions of explanation require all explanations to be backed by causal, constitutive, or similar relations. Among their justifications is that only ontic conceptions can do justice to the ‘directionality’ of explanation, i.e., the requirement that if X explains Y , then not-Y does not explain not-X . Using topological explanations as an illustration, we argue that non-ontic conceptions of explanation have ample resources for securing the directionality of explanations. The different ways in which neuroscientists rely on multiplexes involving both functional and anatomical connectivity in their topological explanations vividly illustrate why ontic considerations are frequently (if not always) irrelevant to explanatory directionality. Therefore, directionality poses no problem to non-ontic conceptions of explanation.

In this paper, we argue that a person is obligated to explain why p just in case she has a role-responsibility to answer the question “Why p?”. This entails that the normative force of explanatory obligations is fundamentally social. We contrast our view with other accounts of explanatory obligations or the so-called “need for explanation,” in which the aforementioned normative force is epistemic, determined by an inquirer's interests, or a combination thereof. We argue that our account outperforms these alternatives.

Several argue that truth cannot be science’s sole epistemic goal, for it would fail to do justice to several scientific practices that advance understanding. I challenge these arguments, but only after making a small concession: science’s sole epistemic goal is not truth as such; rather, its goal is finding true answers to relevant questions. Using examples from the natural and social sciences, I then show that scientific understanding’s epistemically valuable features are either true answers to relevant questions or a means thereof.