The paper provides a systematic overview of recent debates in epistemology and philosophy of science on the nature of understanding. We explain why philosophers have turned their attention to understanding and discuss conditions for “explanatory” understanding of why something is the case and for “objectual” understanding of a whole subject matter. The most debated conditions for these types of understanding roughly resemble the three traditional conditions for knowledge: truth, justification and belief. We discuss prominent views about how to construe these (...) conditions for understanding, whether understanding indeed requires conditions of all three types and whether additional conditions are needed. (shrink)

Claims pertaining to understanding are made in a variety of contexts and ways. As a result, few in the philosophical literature have made an attempt to precisely characterize the state that is y understanding x. This paper builds an account that does just that. The account is motivated by two main observations. First, understanding x is somehow related to being able to manipulate x. Second, understanding is a mental phenomenon, and so what manipulations are required to be an understander must (...) only be mental manipulations. Combining these two insights, the paper builds an account (URM) of understanding as a certain representational capacity—specifically, understanding x involves possessing a representation of x that could be manipulated in useful ways. By tying understanding to representation, the account correctly identifies that understanding is a fundamentally cognitive achievement. However, by also demanding that which representations count as understanding-conferring be determined by their practical effects, URM captures the insight that understanding is vitally connected to practice. URM is fully general, and can apply equally well to understanding states of affairs, understanding events, and even understanding people and works of art. The ultimate test of URM is its applicability in actual scientific and philosophical discourse. To that end the paper discusses the importance of understanding in the philosophy of science, psychology, and computer science. (shrink)

What role does the imagination play in scientific progress? After examining several studies in cognitive science, I argue that one thing the imagination does is help to increase scientific understanding, which is itself indispensable for scientific progress. Then, I sketch a transcendental justification of the role of imagination in this process.

This paper discusses the epistemic import of highly abstract and simplified theoretical models using Thomas Schelling’s checkerboard model as an example. We argue that the epistemic contribution of theoretical models can be better understood in the context of a cluster of models relevant to the explanatory task at hand. The central claim of the paper is that theoretical models make better sense in the context of a menu of possible explanations. In order to justify this claim, we introduce a distinction (...) between causal scenarios and causal mechanism schemes. These conceptual tools help us to articulate the basis for modelers’ intuitive confidence that their models make an important epistemic contribution. By focusing on the role of the menu of possible explanations in the evaluation of explanatory hypotheses, it is possible to understand how a causal mechanism scheme can improve our explanatory understanding even in cases where it does not describe the actual cause of a particular phenomenon. (shrink)

Testimony spreads information. It is also commonly agreed that it can transfer knowledge. Whether it can work as an epistemic source of understanding is a matter of dispute. However, testimony certainly plays a pivotal role in the proliferation of understanding in the epistemic community. But how exactly do we learn, and how do we make advancements in understanding on the basis of one another’s words? And what can we do to maximize the probability that the process of acquiring understanding from (...) one another succeeds? These are very important questions in our current epistemological landscape, especially in light of the attention that has been paid to understanding as an epistemic achievement of purely epistemic value. Somewhat surprisingly, the recent literature in social epistemology does not offer much on the topic. The overarching aim of this paper is to provide a tentative model of understanding that goes in-depth enough to safely address the question of how understanding and testimony are related to one another. The hope is to contribute, in some measure, to the effort to understand understanding, and to explain two facts about our epistemic practices: the fact that knowledge and understanding relate differently to testimony, and the fact that some pieces of testimonial information are better than others for the sake of providing one with understanding and of yielding advancements in one’s epistemic standing. (shrink)

This paper provides an inferentialist account of model-based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, (...) the paper shows how the contrastive counterfactual theory of explanation can provide tools for assessing the explanatory power of models. (shrink)

In this paper, I argue that explanations just ARE those sorts of things that, under the right circumstances and in the right sort of way, bring about understanding. This raises the question of why such a seemingly simple account of explanation, if correct, would not have been identified and agreed upon decades ago. The answer is that only recently has it been made possible to analyze explanation in terms of understanding without the risk of collapsing both to merely phenomenological states. (...) For the most part, theories of explanation were for 50 years held hostage to the historical accident that they far outstripped in sophistication corresponding accounts of understanding. (shrink)

This article discusses agent-based simulation (ABS) as a tool of sociological understanding. I argue that agent-based simulations can play an important role in the expansion of explanatory understanding in the social sciences. The argument is based on an inferential account of understanding (Ylikoski 2009, Ylikoski & Kuorikoski 2010), according to which computer simulations increase our explanatory understanding by expanding our ability to make what-if inferences about social processes and by making these inferences more reliable. The inferential account also suggests a (...) number of ways in which the use of simulation methodology might give rise to illusory understanding. The structure of the article is as .. (shrink)

Recent work in epistemology and philosophy of science has argued that understanding is an important cognitive state that philosophers should seek to analyse. This paper offers a new perspective on understanding by looking to work in philosophy of mind and cognitive science. Understanding is normally taken to be inside the head. I argue that this view is mistaken. Often, understanding is a state that criss-crosses brain, body and world. To support this claim, I draw on extended cognition, a burgeoning framework (...) in cognitive science that stresses the crucial role played by tools, material representations and the wider environment in our cognitive processes. I defend an extended view of understanding against likely objections and argue that it has important consequences for questions concerning the nature of understanding and its relationship to explanation. (shrink)

How can we use models to understand real phenomena if models misrepresent the very phenomena we seek to understand? Some accounts suggest that models may afford understanding by providing causal knowledge about phenomena via how-possibly explanations. However, general equilibrium models, for example, pose a challenge to this solution since their contribution appears to be purely mathematical results. Despite this, practitioners widely acknowledge that it improves our understanding of the world. I argue that the Arrow–Debreu model provides a mathematical how-possibly explanation (...) which establishes claims of mathematical dependence. The account developed reveals how mathematical knowledge can inform claims about the world, allow ‘what-if-things-had-been-different’ inferences, and thus improve our understanding. (shrink)

Our goal in this paper is to experimentally investigate whether folk conceptions of explanation are psychologistic. In particular, are people more likely to classify speech acts as explanations when they cause understanding in their recipient? The empirical evidence that we present suggests this is so. Using the side-effect effect as a marker of mental state ascriptions, we argue that lay judgments of explanatory status are mediated by judgments of a speaker’s and/or audience’s mental states. First, we show that attributions of (...) both understanding and explanation exhibit a side-effect effect. Next, we show that when the speaker’s and audience’s level of understanding is stipulated, the explanation side-effect effect goes away entirely. These results not only extend the side-effect effect to attributions of understanding, they also suggest that attributions of explanation exhibit a side-effect effect because they depend upon attributions of understanding, supporting the idea that folk conceptions of explanation are psychologistic. (shrink)

One can find in the literature two sets of views concerning the relationship between understanding and explanation: that one understands only if 1) one has knowledge of causes and 2) that knowledge is provided by an explanation. Taken together, these tenets characterize what I call the narrow knowledge account of understanding. While the first tenet has recently come under severe attack, the second has been more resistant to change. I argue that we have good reasons to reject it on the (...) basis of theoretical models that provide how-possibly explanations. These models, while they do not explain in the strict sense, afford understanding. In response, I propose an alternative epistemology of understanding, broad KAU, that takes cases of theoretical modelling into account. (shrink)

In debates about the nature of scientific explanation, one theme repeatedly arises: that explanation is about providing understanding. However, the concept of understanding has only recently been explored in any depth, and this paper attempts to introduce a useful concept of understanding to that literature and explore it. Understanding is a higher level cognition, the recognition of connections between various pieces of knowledge. This conception can be brought to bear on the conceptual issues that have thus far been unclear in (...) the literature. Though this notion of understanding is broad, explaining various concepts of explanation, it is robust enough to underwrite an objective and useful notion of explanation. (shrink)

Despre analogia existentă între aspectele epistemologice şi metodologice ale activităţii serviciilor de informaţii şi unele discipline ştiinţifice, pledând pentru o abordare mai ştiinţifică a procesului de culegere şi analiză de informaţii din cadrul ciclului de informaţii. Afirm că în prezent aspectele teoretice, ontologice şi epistemologice, în activitatea multor servicii de informaţii, sunt subestimate, determinând înţelegere incompletă a fenomenelor actuale şi creând confuzie în colaborarea inter-instituţională. După o scurtă Introducere, care include o istorie a evoluţiei conceptului de serviciu de informaţii după (...) al doilea război mondial, în Activitatea de informaţii definesc obiectivele şi organizarea serviciilor de informaţii, modelul de bază al acestor organizaţii (ciclul informaţional), şi aspectele relevante ale culegerii de informaţii şi analizei de informaţii. În secţiunea Ontologia evidenţiez aspectele ontologice şi entităţile care ameninţă şi sunt ameninţate. Secţiunea Epistemologie include aspecte specifice activităţii de informaţii, cu analiza principalului model (Singer) folosit în mod tradiţional, şi expun o posibilă abordare epistemologică prin prisma conceptului de cunoaştere tacită dezvoltat de omul de ştiinţă Michael Polanyi. În secţiunea Metodologii prezint diverse teorii metodologice cu accent pe tehnicile analitice structurale, şi câteva analogii, cu ştiinţa, arheologia, afacerile şi medicina. Lucrarea se încheie cu Concluziile privind posibilitatea unei abordări mai ştiinţifice a metodelor de culegere şi analiză a informaţiilor din cadrul serviciilor de informaţii. -/- Cuvinte cheie: servicii de informaţii, agenţii de informaţii, informaţii, intelligence -/- CUPRINS -/- Abstract 1. Introducere 1.1. Istorie 2. Activitatea de informaţii 2.1. Organizaţii 2.2. Ciclul informaţional 2.3. Colectarea informaţiilor 2.4. Analiza informaţiilor 2.5. Contrainformatii 2.6. Comunităţi epistemice 3. Ontologia 4. Epistemologia 4.1. Cunoaşterea tacită (Polanyi) 5. Metodologii 6. Analogii cu alte discipline 6.1. Stiinta 6.2. Arheologia 6.3. Afaceri 6.4. Medicina 7. Concluzii Bibliografie -/- ISBN: 978-606-033-160-5 , DOI: 10.58679/TW56764. (shrink)

In this paper, we aim at explaining the relevance of thought experiments in philosophy and the history of science by describing them as particular instances of two categories of creative thinking: metaphorical reasoning and abductive cognition. As a result of this definition, we will claim that TEs hold an ignorance-preserving trait that is evidenced in both TEs inferential structure and in the process of scenario creation they presuppose. Elaborating this thesis will allow us to explain the wonder that philosophers of (...) science have consistently shown for TEs, as well as the high functionality of TEs in the creative aspects of scientific and philosophical praxis. (shrink)

Many of the arguments for neuroeconomics rely on mistaken assumptions about criteria of explanatory relevance across disciplinary boundaries and fail to distinguish between evidential and explanatory relevance. Building on recent philosophical work on mechanistic research programmes and the contrastive counterfactual theory of explanation, we argue that explaining an explanatory presupposition or providing a lower-level explanation does not necessarily constitute explanatory improvement. Neuroscientific findings have explanatory relevance only when they inform a causal and explanatory account of the psychology of human decision-making.

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also (...) provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label “understandable AI” to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following De Regt (2017) and Kuorikoski and Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent’s success in using the system. (shrink)

Scientific understanding as a subject of inquiry has become widely discussed in philosophy of science and is often addressed through case studies from history of science. Even though these historical reconstructions engage with details of scientific practice, they usually provide only limited information about the gradual formation of understanding in ongoing processes of model and theory construction. Based on a qualitative ethnographic study of an ecological research project, this article shifts attention from understanding in the context of historical case studies (...) to evidence of current case studies. By taking de Regt’s contextual theory of scientific understanding into the field, it confirms core tenets of the contextual theory suggesting a normative character with respect to scientific activities. However, the case study also shows the limitations of de Regt’s latest version of this theory as an attempt to explain the development of understanding in current practice. This article provides a model representing the emergence of scientific understanding that exposes main features of scientific understanding such as its gradual formation, its relation to skills and imagination, and its capacity for knowledge selectivity. The ethnographic evidence presented here supports the claim that something unique can be learned by looking into ongoing research practices that can’t be gained by studying historical case studies. (shrink)

Two of the most important outcomes of The Contradictory Christ include: identifying Christ as an unproblematically contradictory being as well as laying the foundations of an investigation of the logical consequences of the existence of Christ, qua contradictory, within a particular 'theory'. In light of the enormous reluevance of Beall’s The contradictory Christ for the study of inconsistency, my main concern here is to explore the effect of some methodological choices behind Beall’s proposal -this in order to recognize in more (...) detail the scope of Beall’s contribution. To do so, I will focus on three main questions: 1. What is required for the identification of a contradiction? 2. How can we recognize a true contradiction from either an apparent or a temporal contradiction? 3. If we identify a true contradiction within a theory, where can we actually go from there? (shrink)

Understanding is a valued trait in any epistemic practice, scientific or not. Yet, when it comes to characterizing its nature, the notion has not received the philosophical attention it deserves. We have set ourselves three tasks in this paper. First, we defend the importance of this endeavor. Second, we consider and criticize a number of proposals to this effect. Third, we defend an alternative account, focusing on abilities as the proper mark of understanding.

Is understanding subject to a factivity constraint? That is, must the agent’s representation of some subject matter be accurate in order for her to understand that subject matter? ‘No’, I argue in this paper. As an alternative, I formulate a novel manipulationist account of understanding. Rather than correctly representing, understanding, on this account, is a matter of being able to manipulate a representation of the world to satisfy contextually salient interests. This account of understanding is preferable to factivism, I argue, (...) mainly for simplicity reasons. While it explains the intuitive data about understanding as successfully as factivist accounts, it is simpler by virtue of reducing the value truth bestows on understanding to that of usability. (shrink)

Could groups ever be an understanding subject (an epistemic agent ascribed with understanding) or should we keep our focus exclusively on the individuals that make up the group? The way this paper will shape an answer to this question is by starting from a case we are most willing to accept as group understanding, then mark out the crucial differences with an unconvincing case, and, ultimately, explain why these differences matter. In order to concoct the cases, however, we need to (...) elucidate what it means to be attributed with understanding and what makes up an epistemic agent. I shall argue that it is abilities, above all, that guide our attributions of understanding. While it is true that understanding must go beyond single acts, this is not going beyond as in going behind them (to private occurrences which are impossible to discern and don’t themselves contribute anything of value), but beyond as in considering what people could and would do: their discernable and valuable abilities. To conceptualize whom the abilities belong to, I will specify what it means to be an epistemic agent. I shall argue that it is being a successful target of the epistemic stance. The epistemic stance (heavily inspired by Dennett’s intentional stance) forms an instrumental abstraction by attributing epistemic properties (i.e., beliefs, epistemic aims, problem-solving tactics) to an entity to explain or predict its behavior. Macro-systematicity (a higher-level pattern which a theory can exploit) is what makes the epistemic stance’s abstraction an explanatory powerful one (regardless of how that macro-systematicity is realized) and emergence (the lack of a straightforward mapping-relation between the micro and the macro) is what makes its power unique to a particular level of explanation. To end, I suggest two kinds of mapping-relations and walk through the shift from reducibility to emergence. (shrink)

Evolution is often characterized as a tinkerer that creates efficient but messy solutions to problems. We analyze the nature of the problems that arise when we try to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem – solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show that the reason for why evolutionary (...) designs are often hard to understand is that they exhibit non-modular functionality, and that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation. (shrink)

It is widely believed that mathematics carries a substantial part of the explanatory burden in science. However, mathematics can also play important heuristic roles of a different kind, being a source of new ideas and approaches, allowing us to build toy models, enhancing expressive power and providing fruitful conceptualizations. In this paper, we focus on the application of dynamical systems theory (DST) within the extended cognition (EC) field of cognitive science, considering this case study to be a good illustration of (...) a general phenomenon. In the paper, we justify both a negative and a positive claim. The negative claim is that dynamical systems theory hardly plays any explanatory role in EC research. We justify our claim by analyzing several accounts of the explanatory role of mathematics and stressing the way mathematical arguments are used in explanations. Our positive claim is that even though, for now, DST has no explanatory power in many of the EC approaches, it still plays an important heuristic role there. In particular, using mathematical notions improves the expressive power of the language and gives a sense of understanding of the phenomena under investigation. The case study of EC allows us to identify and analyze this important role of mathematics, which seems to be neglected in contemporary discussions. (shrink)

Highly idealized models may serve various epistemic functions, notably explanation, in virtue of representing the world. Inferentialism provides a prima facie compelling characterization of what constitutes the representation relation. In this paper, I argue that what I call factive inferentialism does not provide a satisfactory solution to the puzzle of model-based—factive—explanation. In particular, I show that making explanatory counterfactual inferences is not a sufficient guide for accurate representation, factivity, or realism. I conclude by calling for a more explicit specification of (...) model-world mismatches and properties imputation. (shrink)

Wir Menschen streben danach, die Wirklichkeit zu verstehen. Eine Welt, die wir gut verstehen, ist eine, die wir "im Griff" haben, mit der wir gut umgehen können. Aber was heißt es genau, ein Phänomen der Wirklichkeit zu verstehen? Wie sieht unser Weltbild aus, wenn wir ein Phänomen verstanden haben? Welche Bedingungen müssen erfüllt sein, damit Verstehen gelingt? Die Kernthese des Buches ist, dass wir Phänomene der Wirklichkeit durch noetische Integration verstehen. Wir verstehen Phänomene, indem wir den entsprechenden Informationseinheiten eine sinnvolle (...) und angemessene Position in unserem Weltbild zuschreiben und insofern unser Weltbild in gewissem Maße der Wirklichkeit entspricht. (shrink)

In recent years, there has been a lot of interest in scientific understanding. There was already a consensus in the philosophy of science about the fact that science provides understanding of the world, but until the 70s there was not a proper and systematic investigation of the matter. This investigation has been notoriously incremental in the last two decades. Nevertheless, there is still no agreed upon way of how to understand “understanding” . Faye’s contribution to the debate proves to be (...) a departure from the previous standard works on explanation and scientific inference. The author’s main purpose is, in his own words, describing “how our human cognitive capacities allow scientists to acquire an understanding of nature by means of representation, interpretation, and explanation” . He starts from a D .. (shrink)

L'analyse du renseignement présente de nombreuses similitudes épistémologiques importantes avec la science (résolution de problèmes, découverte, utilisation habile des outils, vérification des demandes de connaissances) et s'intéresse davantage aux connaissances a posteriori qu'aux a priori sur la manière ou la base sur laquelle une proposition peut être connue. Tant l'analyse du renseignement que la science se concentrent sur les connaissances acquises à partir d'observations empiriques, connaissances qui sont typiquement a posteriori. La métaphore du puzzle est utilisée à la fois dans (...) le renseignement et l'archéologie. Les deux disciplines impliquent la collecte de preuves pour construire une image aussi complète que possible. DOI: 10.13140/RG.2.2.16740.09603 . (shrink)

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2012.

Intelligence analysis has many important epistemological resemblances with science (problem solving, discovery, skillful use of tools, knowledge verification) and is more interested in a posteriori than a priori knowledge, on how or the basis on which a proposition may be known. The puzzle metaphor is used in both information and archeology. Both disciplines involve collecting evidence to build as complete a picture as possible. The process of converting raw information into actionable processed intelligence is almost identical for governmental and business (...) organizations. The medical practice of diagnosing identification, collection, analysis and dissemination is similar to that of intelligence. DOI: 10.13140/RG.2.2.26511.12963. (shrink)

Une analogie entre les aspects épistémologiques et méthodologiques de l'activité des services de renseignement et certaines disciplines scientifiques, en préconisant une approche plus scientifique du processus de collecte et d'analyse de l'information au sein du cycle du renseignement. J'affirme que les aspects théoriques, ontologiques et épistémologiques de l'activité de nombreux services de renseignement sont sous-estimés, ce qui conduit à une compréhension incomplète des phénomènes actuels et à une confusion dans la collaboration interinstitutionnelle. Après une brève Introduction, qui inclut un historique (...) de l'évolution du concept de service de renseignement après la Seconde Guerre mondiale, Renseignement définit les objectifs et l'organisation des services de renseignement, le modèle de base de ces organisations (le cycle du renseignement) et les aspects pertinents de la collection de l'information et l'analyse du renseignement. Dans la section Ontologie, je souligne les aspects ontologiques et les entités qui menacent et qui sont menacés. La section Épistémologie comprend des aspects spécifiques à l'activité de renseignement, avec l'analyse du modèle traditionnel (Singer) utilisé traditionnellement, et j'expose une approche épistémologique possible à travers le concept de connaissance tacite développé par le scientifique Michael Polanyi. La section Méthodologie contient diverses théories méthodologiques mettant l’accent sur les techniques analytiques structurelles ainsi que certaines analogies avec la science, l’archéologie, les affaires et la médecine. L'article se termine par les Conclusions sur la possibilité d'une approche plus scientifique des méthodes de collecte d'information et d'analyse des services de renseignement. SOMMAIRE: Abstract 1. Introduction 1.1. Histoire du renseignement 2. Renseignement 2.1. Organisations 2.2. Cycle du renseignement 2.3. La collecte du renseignement 2.4. Analyse du renseignement 2.5. Contre-espionnage 2.6. Communautés épistémiques 3. Ontologie 4. Épistémologie 4.1. La connaissance tacite (Polanyi) 5. Méthodologies 6. Analogies avec d'autres disciplines 6.1. Science 6.2. Archéologie 6.3. Affaires 6.4. Médecine 7. Conclusions Bibliographie DOI: 10.13140/RG.2.2.35970.22729 . (shrink)