Dissertations / Theses: 'Explanation'

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Deulofeu, Batllori Roger. "Scientific explanation in biology. Beyond mechanistic explanation." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/668748.

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Understanding how scientists explain has been one of the major goals of the philosophy of science. Given that explaining is one of the most important tasks that scientists aim at and given the high specialization that currently affects all scientific disciplines, we encounter what might at first glance appear to us as many different types of explanations and very different ways of explaining natural phenomena. This suggests a pluralist picture regarding scientific explanation, particularly in biology, namely the existence of different accounts of explanation that do not share an interesting common core. However, the main goal of the traditional analysis of scientific explanation was to elaborate a monist theory of explanation according to which all scientific explanations share a common core that makes them what they are - i.e. that they can be identified by a commonly shared set of necessary and jointly sufficient conditions. The monist accounts mainly draw on examples from physics to illustrate how this is supposed to work, leaving examples from the special science, like biology, aside. In the last twenty years, nonetheless, the rise of the New Mechanism philosophy, with its notion of mechanistic explanation, has become the dominant and widely accepted account among the philosophers of science to analyze scientific explanation in biology, challenging the pluralist view. The New mechanist account of scientific explanation is essentially monist since their defenders claim that mechanisms are all what really matters to explanation. According to mechanistic explanation, in order to explain a biological phenomenon, we have to discover the mechanism that is responsible for it. Further, we have to decompose this mechanism in order to identify its component parts and identify the causal story that connects the components with the phenomenon. Mechanistic explanations are thus considered causal explanations. The New Mechanism philosophy has arguably been very successful in analyzing how explanation works in a huge diversity of models in biology, suggesting that their account of mechanistic explanation is the only legitimate of in biology. Furthermore, New Mechanism philosophy provides a new framework that contributed to tackle traditional problems of the philosophy of science related to notions such as laws of nature, function, causation, etc. Although mechanistic explanation has proved very successful in analyzing the explanatory force of many biological models, its scope in biology is still under discussion. In the last few years, there has been voices limiting the extension of this account. On the one hand, there has been philosophers claiming that in some biological models, mathematics plays not only a representational role but an explanatory role, suggesting that those models provide explanations that rather than identifying a mechanism with its components and causal story, identify mathematical properties that are explanatory of some phenomenon. They claim that in those explanations, the system under analysis has a mathematical structure whose mathematical properties are explanatory of a particular range of explananda. On the other hand, and despite the claim widely accepted that there are no laws in biology, some philosophers claim we can still consider that some biological models explain by appeal to laws of nature, suggesting covering law accounts of scientific explanation. The present thesis dissertation is a contribution to the aforementioned debate. It provides examples of biological models whose explanatory power does not lie in its identification of mechanisms with its parts and causal story, even if the models look somehow mechanistic. I claim they provide non-mechanistic (and non-causal) explanations, in so far as the models, even if they could identify a mechanism, do not explain by pinpointing information about its causal story.

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Paez, Andres. "Explanations in K : an analysis of explanation as a belief revision operation /." Oberhausen : Athena, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=015470212&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Hegemann, Lena. "Reciprocal Explanations : An Explanation Technique for Human-AI Partnership in Design Ideation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281339.

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Advancements in creative artificial intelligence (AI) are leading to systems that can actively work together with designers in tasks such as ideation, i.e. the creation, development, and communication of ideas. In human group work, making suggestions and explaining the reasoning behind them as well as comprehending other group member’s explanations aids reflection, trust, alignment of goals and inspiration through diverse perspectives. Despite their ability to inspire through independent suggestions, state-of-the-art creative AI systems do not leverage these advantages of group work due to missing or one-sided explanations. For other use cases, AI systems that explain their reasoning are already gathering wide research interest. However, there is a knowledge gap on the effects of explanations on creativity. Furthermore, it is unknown whether a user can benefit from also explaining their contributions to an AI system. This thesis investigates whether reciprocal explanations, a novel technique which combines explanations from and to an AI system, improve the designers’ and AI’s joint exploration of ideas. I integrated reciprocal explanations into an AI aided tool for mood board design, a common method for ideation. In our implementation, the AI system uses text to explain which features of its suggestions match or complement the current mood board. Occasionally, it asks for user explanations providing several options for answers that it reacts to by aligning its strategy. A study was conducted with 16 professional designers who used the tool to create mood boards followed by presentations and semi-structured interviews. The study emphasized a need for explanations that make the principles of the system transparent and showed that alignment of goals motivated participants to provide explanations to the system. Also, enabling users to explain their contributions to the AI system facilitated reflection on their own reasons.
Framsteg inom kreativ artificiell intelligens (AI) har lett till system som aktivt kan samarbeta med designers under idéutformningsprocessen, dvs vid skapande, utveckling och kommunikation av idéer. I grupparbete är det viktigt att kunna göra förslag och förklara resonemanget bakom dem, samt förstå de andra gruppmedlemmarnas resonemang. Detta ökar reflektionsförmågan och förtroende hos medlemmarna, samt underlättar sammanjämkning av mål och ger inspiration genom att höra olika perspektiv. Trots att system, baserade på kreativ artificiell intelligens, har förmågan att inspirera genom sina oberoende förslag, utnyttjar de allra senaste kreativa AI-systemen inte dessa fördelar för att facilitera grupparbete. Detta är på grund av AI-systemens bristfälliga förmåga att resonera över sina förslag. Resonemangen är ofta ensidiga, eller saknas totalt. AI-system som kan förklara sina resonemang är redan ett stort forskningsintresse inom många användningsområden. Dock finns det brist på kunskap om AI-systemens påverkan på den kreativa processen. Dessutom är det okänt om en användare verkligen kan dra nytta av möjligheten att kunna förklara sina designbeslut till ett AI-system. Denna avhandling undersöker om ömsesidiga förklaringar, en ny teknik som kombinerar förklaringar från och till ett AI system, kan förbättra designerns och AI:s samarbete under utforskningen av idéer. Jag integrerade ömsesidiga förklaringar i ett AI-hjälpmedel som underlättar skapandet av stämningsplank (eng. mood board), som är en vanlig metod för konceptutveckling. I vår implementering använder AI-systemet textbeskrivningar för att förklara vilka delar av dess förslag som matchar eller kompletterar det nuvarande stämningsplanket. Ibland ber den användaren ge förklaringar, så den kan anpassa sin förslagsstrategi efter användarens önskemål. Vi genomförde en studie med 16 professionella designers som använde verktyget för att skapa stämningsplank. Feedback samlades genom presentationer och semistrukturerade intervjuer. Studien betonade behovet av förklaringar och resonemang som gör principerna bakom AI-systemet transparenta för användaren. Höjd sammanjämkning mellan användarens och systemets mål motiverade deltagarna att ge förklaringar till systemet. Genom att göra det möjligt för användare att förklara sina designbeslut för AI-systemet, förbättrades också användarens reflektionsförmåga över sina val.

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Almeqdad, Qais Ibrahim. "Self-explanation and explanation in children with learning difficulties." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612344.

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Roberts, Rosemary. "What makes an explanation a good explanation? : adult learners' criteria for acceptance of a good explanation /." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0006/MQ42436.pdf.

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Hörberg, Eric. "Is explanation overrated? : A research on how explanation affects performance." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157513.

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School results are dropping in Sweden and actions are taken by the swedish government to prevent it. This report questions these actions.With a parallel between school and video games, in that they are both about teaching a student/player how to do something, a game is made to test how further explanation of the games mechanics affects the players ability to learn about them. The results are in line with other studies, overexplaining is hurting the players ability to learn about the game.

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Lipton, P. "Explanation and evidence." Thesis, University of Oxford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371691.

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Bodle, Matthew James. "Grounding and explanation." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/grounding-and-explanation(23a3509e-ffbe-4750-a928-7cb031e0c6de).html.

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This thesis defends the notion of grounding — an explanatory connection of non-causal determination. I present four challenges to developing a systematic theory of grounding, and show that they can be met satisfactorily. The first challenge is that grounding is unintelligible or uninformative—or at any rate, that its work can be done by more familiar notions. If so, the notion of grounding is not even prima facie justified. I argue that grounding is at least as informative as—and, in some respects, more informative than—the more familiar notions it is supposed to supplant. It is necessary because we can express with it certain explanatory relation-ships which are just inexpressible with weaker notions of determination. My defence of grounding is preferable to extant defences since it is less concessive, requiring fewer assumptions about the nature of grounding. A key motivation for grounding is that it is an explanatory connection. The second challenge is that the sense in which grounding is a (distinctly) explanatory relation is unclear, wherefore the case for grounding is severely weakened. I motivate a theory of explanation and argue that it comports nicely with the sense in which grounding is explanatory. Moreover, I characterise a new explanatory notion I call philosophical ex-planation with grounding at its core. This notion illustrates the importance of grounding for philosophical methodology generally. The third challenge is to the internal coherence of grounding theory. A dilemma apparently show that grounding connections can be neither grounded nor ungrounded. Several treatments of this problem already exist, but none is satisfactory. Some imply implausible explanations. Others require new—dubious—posits. I present a new solu-tion, which o ̇ers satisfying explanations but requires no dubious posits. It explains, moreover, why some grounding connections appear to admit of explanation but others do not. The last challenge is to the usefulness of grounding. While it is an interesting meta-metaphysical posit, it o ̇ers little to the metaphysician working on first-order problems. I show how grounding can be fruitfully applied to breaking the deadlock in the debate about laws of nature.

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Nickel, Bernhard Ph D. Massachusetts Institute of Technology. "Truth in explanation." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33711.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2005.
Includes bibliographical references (p. 155-163).
My thesis consists of three papers on truth and explanations in science. Broadly, the question I ask is semantic. Should the best account of certain bits of our scientific practice focus on the concept of truth? More specifically, should the crucial distinctions between good and bad aspects of that practice be drawn in terms of truth? My thesis consists of three case studies: ceteris paribus laws in the special sciences, appeals to idealizations in the application of theories, and the analysis of explanations quite generally, exemplified in the asymmetry of explanation. In each case, prominent philosophers have argued that a proper treatment does not focus on truth. In each case, I argue that truth should play a central role. And in each case, the issue turns, at least in part, on the connection between the scientific practice in question and explanations.
by Bernhard Nickel.
Ph.D.

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Taylor, Elanor Lycan William G. "Models and explanation." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1914.

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Thesis (M.A.)--University of North Carolina at Chapel Hill, 2008.
Title from electronic title page (viewed Dec. 11, 2008). "... in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Philosophy." Discipline: Philosophy; Department/School: Philosophy.

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Quinn, Laleh Kathleen. "Consciousness and explanation." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/289172.

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We have yet to develop a theory of explanation that will account for all of consciousness. Recent debate on this topic has been impaired because it has in large part proceeded without any explicit attention to the nature of explanation. On the one hand, the lack of commitment to any well-specified theory of explanation leads to imprecision and vagueness. On the other hand, much of the optimism concerning the possibility of explaining all aspects of consciousness stems from an attachment to the only developed theory of psychological phenomena at our disposal and the belief that all of consciousness can be captured by such a theory. Some of the inadequacy in the literature on consciousness is due to a conflation between consciousness construed as mode of presentation , that is, the way content is presented to the agent, and consciousness construed as subjective or qualitative feel. Once the two objects of concern are distinguished, we have a much clearer vision of what needs to be explained, and we can turn our focus on the proper way to do so. I argue that subjective feel is an important aspect of consciousness in need of explanation, and that an explanation of this phenomenon is distinct from an explanation of mode of presentation or representation. Furthermore, while there are well-articulated methods of explanation that properly address mode of presentation and representation, this is not the case for subjective feel. I delineate several genera of scientific explanation in an attempt to exhaust the possible methods by which we may be capable of explaining subjective feel. This involves the taxonomizing of types of phenomena that are the targets of our explanatory methods. While one type of explanatory strategy may be adequate when the target explanandum is a property, the same strategy may fall short in explaining a single event, event type, or regularity. Subjective feel is best construed as a property. However, while the method employed by cognitive science to explain mental properties may be adequate for explaining much cognitive phenomena, I argue that it is incapable of explaining subjective feel.

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Taylor, Kaetlin Diane. "The Epistemic and Ontic Conceptions of Scientific Explanation." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78011.

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While Wesley Salmon attributes the debate on scientific explanation between Carl Hempel and Peter Railton (or between the epistemic and ontic conceptions of scientific explanation, more generally) as one over which conception of explanation is correct, I claim that Hempel and Railton were responding to two different questions altogether. Hempel was addressing a question akin to 'what is scientific explanation?', while Railton was focused on a question more similar to 'what is scientific explanation?' In this paper I discuss the different questions Hempel and Railton were addressing, and how distinguishing these two questions can aid in the discussion of the requirements and adequacy of models of scientific explanation. While these two questions are clearly inter-related, I claim that we should not judge the adequacy of an answer to one of these questions on the basis of the adequacy of an answer to the other.
Master of Arts

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González, del Solar Sarría Rafael. "Mechanismic explanation in ecology." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/381073.

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La ecología es una ciencia importante, tanto desde el punto de vista práctico como desde el teórico, que recientemente a comenzado a atraer la atención de los filósofos profesionales. Con todo, la investigación sobre los fundamentos filosóficos de la ecología, en particular sobre sus prácticas explicativas, está aún poco desarrollada; y ello pese a que los propios ecólogos perciben que el debate sobre la explicación ecológica es importante. En esta tesis doctoral comparo las principales tesis ontológicas y epistemológicas de tres proyectos filosóficos que ofrecen un análisis de la explicación científica en términos de mecanismos, con la descripción de dos casos de explicación en ecología basados en mecanismos, tal como los entienden los ecólogos, los mecanismos de facilitación y la competencia ecológicas. Los ejemplos que analizo aquí provienen del campo de la sucesión ecológica, aunque tanto la facilitación como la competencia son interacciones muy extendidas en todo el ámbito de la ecología. Sobre la base de mi análisis, sostengo que si bien las contribuciones epistemológicas que los proyectos filosóficos estudiados han realizado al debate de la explicación científica son importantes, pero que aún hay mucho espacio para mejorar la caracterización de la naturaleza de los mecanismos ecológicos y de la explicación mecanísmica en ecología. Basado en el trabajo previo del filósofo sistemista Mario Bunge, propongo que los mecanismos ecológicos son procesos específicos que ocurren en sistemas y que las explicaciones mecanísmicas en ecología pueden asumir diversas formas, pero que consisten en descripciones de esos procesos en el marco de la descripción más general del sistema de interés.
Ecology is a science of practical and theoretical importance that has recently begun to appeal to professional philosophers. Yet, work on the philosophical foundations of ecology, particularly on its explanatory practices, is still scarce, even though ecologists perceive the debate on ecological explanation as an important one. In this dissertation, I contrast the main theses of three different philosophical projects that attempt to account for scientific explanation in terms of mechanisms descriptions with two cases of ecological explanation based on mechanisms, as ecologists understand the term: the mechanisms of ecological facilitation and competition. The examples I study come from the subfield of ecological succession, though both facilitation and competition are widespread along the whole of ecology. Based on my analysis of those cases I argue that those projects have contributed important elements to the ontology and epistemology of scientific explanation, but that there is still room for improvement towards an adequate characterization of the precise nature of ecological mechanisms and mechanismic explanation in ecology. Following the lead of previous work by systemist philosopher Mario Bunge, I suggest that ecological mechanisms are specific processes in systems, and that, even though they may take different forms, mechanismic explanations consist in descriptions of those processes in the context of a description of the system of interest.

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Sutton, Peter Andrew. "Models of scientific explanation." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2372.

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Ever since Hempel and Oppenheim's development of the Deductive Nomological model of scientific explanation in 1948, a great deal of philosophical energy has been dedicated to constructing a viable model of explanation that concurs both with our intuitions and with the general project of science. Here I critically examine the developments in this field of study over the last half century, and conclude that Humphreys' aleatory model is superior to its competitors. There are, however, some problems with Humphreys' account of the relative quality of an explanation, so in the end I develop and defend a modified version of the aleatory account.

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Rood, Tim C. B. "Thucydides : narrative and explanation /." Oxford [u.a.] : Clarendon Press, 1998. http://www.loc.gov/catdir/enhancements/fy0604/98007982-d.html.

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Corbett-Clark, Timothy Alexander. "Explanation from neural networks." Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:b94d702a-1243-4702-b751-68784c855ab2.

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Neural networks have frequently been found to give accurate solutions to hard classification problems. However neural networks do not make explained classifications because the class boundaries are implicitly defined by the network weights, and these weights do not lend themselves to simple analysis. Explanation is desirable because it gives problem insight both to the designer and to the user of the classifier. Many methods have been suggested for explaining the classification given by a neural network, but they all suffer from one or more of the following disadvantages: a lack of equivalence between the network and the explanation; the absence of a probability framework required to express the uncertainty present in the data; a restriction to problems with binary or coarsely discretised features; reliance on axis-aligned rules, which are intrinsically poor at describing the boundaries generated by neural networks. The structure of the solution presented in this thesis rests on the following steps: Train a standard neural network to estimate the class conditional probabilities. Bayes’ rule then defines the optimal class boundaries. Obtain an explicit representation of these class boundaries using a piece-wise linearisation technique. Note that the class boundaries are otherwise only implicitly defined by the network weights. Obtain a safe but possibly partial description of this explicit representation using rules based upon the city-block distance to a prototype pattern. The methods required to achieve the last two represent novel work which seeks to explain the answers given by a proven neural network solution to the classification problem.

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Ooms, Renard Nicole Marie Anne. "Plato's metaphysics of explanation." Thesis, King's College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324884.

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Zilhao, Antonio Jose Teiga. "Action, explanation and rationality." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288024.

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Olbrich, David. "Normativity and contrastive explanation." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1474151/.

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My thesis concentrates on the distinction between pro tanto reasons and all-things-considered judgments, and their relation to normative justification. Negatively, it seeks to show that a prevailing kind of account of this relation should be rejected, namely that family of views which takes it that every reason has an associated weight, and the truth with respect to any issue is established by which set of reasons is weightiest. Through an examination of Ross’ doctrine of prima facie duties, this discussion also leads to a formulation of the central problem which any account of this relation must seek to solve. Positively, this thesis develops a new account of the relation between pro tanto reasons and all-things-considered judgements, based on the fundamental insight that a justification of normative propositions is identical to an explanation of their truth, were they to be true. I defend this identity claim, and seek to generate an account of justification from an account of explanation. Drawing on a deservedly popular ‘contrastive’ conception of explanation in the philosophy of science, I show how we can fruitfully think of justification as itself contrastive. Part of this is showing how the notion of a burden of explanation can shed light on the notion of a burden of justification, so a conception of justification emerges according to which a justification for a normative proposition consists in an solution to all those burdens of justification which it incurs. In turn, this feeds a conception of reasons, and their role in justification, alternative to that envisaged in a weighing model: pro tanto reasons determine the correct all-things-considered judgment insofar as they determine to what extent the truth of that judgement has an adequate explanation, such that the correct all-things-considered judgement is just that judgement whose truth would have a fully adequate explanation.

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White, Roger (Roger Lewis) 1967. "Probability, explanation, and reasoning." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8841.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2000.
Includes bibliographical references (p. 96).
Three topics are discussed concerning the application probability and explanation to the confirmation of theories. The first concerns the debate over prediction versus accommodation. I argue that we typically have reason to be more confident of a theory given that it was constructed independently of the knowledge of certain data than if it was designed to accommodate those data. The second concerns the puzzle of the apparent 'fine-tuning' of the universe for life. I argue that the fact that our universe meets the extremely improbable yet necessary conditions for life provides no evidence for the thesis that there are, or have been, very many universes. The third chapter concerns the need to explain the existence of life. I argue that if life's existence needs an explanation at all, the place to look is in a teleological explanation. If this option is rejected, we should be content to see the origin of life as an extremely improbable fluke.
by Roger White.
Ph.D.

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Patterson, Sarah Charlotte. "Content and psychological explanation." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/13941.

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Emery, Nina R. (Nina Rebecca). "Chance, indeterminacy, and explanation." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72921.

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Thesis (Ph. D. in Philosophy)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2012.
"June 2012." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 97-101).
This thesis is about the philosophical and scientific significance of chance. Specifically, I ask whether there is a single notion of chance that both plays a well-defined scientific role and proves useful for various philosophical projects. I argue that there is, but that this notion of chance is importantly different from the one that we usually come across in the philosophical literature. In the first chapter, "Chance, Indeterminacy, and Explanation", I argue against the common and influential view that chances are those probabilities that arise when the fundamental laws are indeterministic. The problem with this view, I claim, is not that it conflicts with some antecedently plausible metaphysics of chance, but rather that it renders the distinction between chance and other sorts of probability incapable of playing any scientifically significant role. I suggest an alternative view, according to which chances are the probabilities that play a certain explanatory role-they are probabilities that explain associated frequencies. In the second chapter, "Chance, Explanation, and Measure", I build on the view that chances are the probabilities that play a certain explanatory role by developing an account of non-fundamental chances-chances that arise when the fundamental laws are deterministic. On this account, non-fundamental chances are objective measures over relevant classes of alternative possibilities. In the third chapter, "Chance and Counterfactuals", I show how the sort of chances I have argued for can play an important role in a very different sort of philosophical project. According to a number of recent arguments, one consequence of our current scientific theories is that most ordinary counterfactuals are not true. I argue that the best response to these arguments makes use of the non-fundamental chances that I have argued for in the first two chapters of the dissertation.
by Nina R. Emery.
Ph.D.in Philosophy

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Chama, Victoria. "Explanation for defeasible entailment." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32206.

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Explanation facilities are an essential part of tools for knowledge representation and reasoning systems. Knowledge representation and reasoning systems allow users to capture information about the world and reason about it. They are useful in understanding entailments which allow users to derive implicit knowledge that can be made explicit through inferences. Additionally, explanations also assist users in debugging and repairing knowledge bases when conflicts arise. Understanding the conclusions drawn from logic-based systems are complex and requires expert knowledge, especially when defeasible knowledge bases are taken into account for both expert and general users. A defeasible knowledge base represents statements that can be retracted because they refer to information in which there are exceptions to stated rules. That is, any defeasible statement is one that may be withdrawn upon learning of an exception. Explanations for classical logics such as description logics which are well-known formalisms for reasoning about information in a given domain are provided through the notion of justifications. Simply providing or listing the statements that are responsible for an entailment in the classical case is enough to justify an entailment. However, when looking at the defeasible case where entailed statements can be retracted, this is not adequate because the way in which entailment is performed is more complicated than the classical case. In this dissertation, we combine explanations with a particular approach to dealing with defeasible reasoning. We provide an algorithm to compute justification-based explanations for defeasible knowledge bases. It is shown that in order to accurately derive justifications for defeasible knowledge bases, we need to establish the point at which conflicts arise by using an algorithm to come up with a ranking of defeasible statements. This means that only a portion of the knowledge is considered because the statements that cause conflicts are discarded. The final algorithm consists of two parts; the first part establishes the point at which the conflicts occur and the second part uses the information obtained from the first algorithm to compute justifications for defeasible knowledge bases.

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Rood, Tim. "Thucydides : narrative and explanation /." Oxford : Clarendon Press, 1998. http://catalogue.bnf.fr/ark:/12148/cb370800469.

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Lusk, Gregory S. "Models and scientific explanation." Ohio : Ohio University, 2009. http://www.ohiolink.edu/etd/view.cgi?ohiou1250816101.

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Bond, Brandon Stephenson. "By Way of Explanation." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/639.

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David-Rus, Richard. "Explanation and understanding through scientific models : perspectives for a new approach to scientific explanation." kostenfrei, 2010. http://d-nb.info/1001624556/34.

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Ward, Bryan. "Making sense of functional explanation." Diss., Connect to the thesis, 2004. http://hdl.handle.net/10066/698.

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Rives, Bradley. "Concepts taking psychological explanation seriously /." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2894.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Philosophy. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Helldin, Tove. "Explanation Methods for Bayesian Networks." Thesis, University of Skövde, School of Humanities and Informatics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3193.

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The international maritime industry is growing fast due to an increasing number of transportations over sea. In pace with this development, the maritime surveillance capacity must be expanded as well, in order to be able to handle the increasing numbers of hazardous cargo transports, attacks, piracy etc. In order to detect such events, anomaly detection methods and techniques can be used. Moreover, since surveillance systems process huge amounts of sensor data, anomaly detection techniques can be used to filter out or highlight interesting objects or situations to an operator. Making decisions upon large amounts of sensor data can be a challenging and demanding activity for the operator, not only due to the quantity of the data, but factors such as time pressure, high stress and uncertain information further aggravate the task. Bayesian networks can be used in order to detect anomalies in data and have, in contrast to many other opaque machine learning techniques, some important advantages. One of these advantages is the fact that it is possible for a user to understand and interpret the model, due to its graphical nature.

This thesis aims to investigate how the output from a Bayesian network can be explained to a user by first reviewing and presenting which methods exist and second, by making experiments. The experiments aim to investigate if two explanation methods can be used in order to give an explanation to the inferences made by a Bayesian network in order to support the operator’s situation awareness and decision making process when deployed in an anomaly detection problem in the maritime domain.

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Nance, Ian Thomas. "Intentional Actions: Explanation and Epistemology." UNIVERSITY OF CALIFORNIA, SANTA BARBARA, 2012. http://pqdtopen.proquest.com/#viewpdf?dispub=3482014.

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Lillehaug, Marvin Bredal. "Explanation-aware Case-based Reasoning." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14197.

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When tasks traditionally performed by humans are automated it is important thatthe machines are able to communicate how these tasks are solved and why. Whena user is surprised by the point of time where the task is executed, there is a needto be able to get an explanation to why this point in time was chosen.This project aims at investigating how intelligent systems in general, and case-based reasoning systems in particular can become explanation-aware. Our aim isprimarily to investigate existing case-based reasoning systems to see if explanation-awareness is achievable. Secondary, our aim is to develop a simple case-based rea-soning engine that complies with our theoretical work on explanation-awareness.

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Stein, Nathaniel. "The metaphysics of Aristotelian explanation." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530078.

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Butchart, Samuel John 1971. "Evidence and explanation in mathematics." Monash University, Dept. of Philosophy, 2001. http://arrow.monash.edu.au/hdl/1959.1/8616.

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Suermondt, Henri Jacques. "Explanation in Bayesian belief networks." Full text available online (restricted access), 1992. http://images.lib.monash.edu.au/ts/theses/suermondt.pdf.

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Dixon, Joan Elizabeth. "Time, consciousness and scientific explanation." Thesis, University of Warwick, 1997. http://wrap.warwick.ac.uk/4309/.

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To date, there is no universal and coherent theory concerning the nature or the function of time. Furthermore, important and unresolved controversies raging within both philosophy and the natural sciences apparently indicate that there is little hope of constructing a single, unified theory. Even so-called "folk" theories of time, embedded within different cultural traditions, show no common elements, and therefore can not provide a pre-theoretical description of time, towards which an explanatory framework could be constructed. This lack of consensus indicates that the concept as it is currently being used is ill defined, and, at the very least, needs to be considerably revised. The conceptual disarray surrounding time has aided and abetted the arguments of certain thinkers, especially Ricoeur, working within the phenomenological tradition who make de principe claims that there can not be a single theory of time. My intention is not to try and to produce a concept of time that was capable of unifying all these different elements. Rather, Ricoeur's arguments and those of others working in the phenomenological tradition dissatisfied me. I believed that their arguments were informed by a myopic, muddled and positively 19th Century understanding of the scientific project. Hence, my aim is to show that Ricoeur's claim will not stand up to scrutiny, and that there are no principled arguments against the possibility of a unified theory of time. We examine the major arguments against unification in general, and also with particular reference to theories of time, such as Husserlian phenomenology, conventionalism, instrumentalism, anti-reductive positions in general, as well as the specific problem of reducing subjective experience to objective description. We demonstrate that none of these objections constitutes a watertight a priori argument against a unified theory of time. Furthermore, we demonstrate that recent developments in the philosophy of science and the philosophy of mind have made such a unified theory a plausible goal. We argue that post-positivist philosophy of science, with its emphasis on research programmes, the co-evolution of theories and super-empirical rational support, opens the way for new types of evidence to be brought to bear on questions about time. Also, recent developments in the brain sciences mean that a neurologically plausible and fully naturalised analysis of our experience of time is being developed. Although much work in this direction has begun, we argue that it is fragmented, partly through the limitations of our current knowledge, but more particularly through an inadequate background of coherent philosophical thought. This has lead both philosophers and scientists to attempt grand metaphysical answers to muddled philosophical questions which threaten the progress which natural science and the philosophy of science have offered in the second half of the twentieth century.

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Stout, Rowland. "The teleological explanation of action." Thesis, University of Oxford, 1991. http://ora.ox.ac.uk/objects/uuid:0f9add24-82bb-4777-b2c4-669262f2015b.

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A different analytical approach to that of the standard causal theory to the explanation of action is proposed. It is argued that the most basic kind of explanation of action is teleological explanation in terms of external reasons. what this amounts to is that an action is the result of a causal process which adapts its results to whatever is objectively practically rational. Explanation in terms of psychological states depends on being able to make this externalist sort of explanation. Central to this account is a theory of causal explanation which depends on the notion of a causal process. A causal process is a real entity distinct from an event. A phenomenon is causally explained when a description of the phenomenon is determined by a theoretical structure which represents how a process which results in the phenomenon works. In teleological explanation, the theoretical structure is that of practical rationality. It is argued that this must be regarded as objective practical rationality. Only purposeful activity can be explained in this way. An account of evolutionary function is provided to show why. it differs from this. This account of teleological explanation, because it does not involve internal mental states, may be used to show how we attribute such states. An agent is essentially a teleological machine. Accounts of perception, beliefs and intentions are provided based on this.

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Ruthven, Ian. "Abduction, explanation and relevance feedback." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392605.

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Finch, Ian. "Intelligent explanation from expert systems." Thesis, University of Liverpool, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316575.

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Stein, Joanne. "Interpretation and explanation in psychoanalysis." Master's thesis, University of Cape Town, 1991. http://hdl.handle.net/11427/13545.

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Bibliography: leaves 150-155.
By exploring the logical status of the psychoanalytic object of investigation, the compromise-formation, this dissertation suggests that although Freud's defence of Psychoanalysis as a natural science has been legitimately rejected as problematic, the reconstrual of Psychoanalysis as an interpretive or hermeneutic knowledge is likewise inappropriate to the psychoanalytic object. On the basis of the work of Donald Davidson and Arthur Dante, it is argued instead that the nature and status of Psychoanalysis as a knowledge is best understood and assessed in terms of a third alternative provided by the historical epistemology germane to the psychoanalytic object. In this way, the case against Psychoanalysis as a natural science is granted, while psychoanalytic epistemology is nevertheless defended as explanatory rather than interpretive.

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Hatcher, Michael. "A Deontological Explanation of Accessibilism." Thesis, University of Southern California, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10268338.

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In the 1980s, epistemology faced an impasse between traditional internalist approaches to the justification of belief and new externalist approaches. While internalists like BonJour and Ginet held that justification is fixed by internal factors such as beliefs, experiences, and what is accessible to reflection, externalists like Armstrong, Goldman, and Dretske held that external factors such as reliability and causal relations to the environment also make a difference to justification. By 1988, Goldman suggested we have two independently interesting notions of justification, one amenable to internalist analysis and the other not. Fast forwarding to the present, the gulf between internalists and externalists is even wider. For the many externalists influenced by Williamsonian views, on which knowledge is an unanalyzable mental state in terms of which properties like justification are to be understood, it is doubtful whether there is anything interesting left about which internalism could be true. But internalists still hold that externalism is missing something important.

With my sympathies on the side of internalism, my dissertation seeks to break up this impasse. In a central chapter, I develop a new argument for a kind of internalism about blameworthiness. In other chapters, I address fundamental issues about the nature of belief and its relation to action and practical reasons, the upshot of which is that one can be blameworthy for belief. This upshot makes room for an analysis of justification in terms of blameworthiness. The overall result is a motivation for epistemic internalism which is driven by contemporary action theory and philosophy of mind while being, at the same time, a vindication of an idea arguably traceable at least as far back as Descartes and Locke. This is the idea that justification is internal because it is to be analyzed in terms of blameworthiness.

My central argument for internalism about blameworthiness has the following structure. A person is blameworthy only if she herself exercises control. But control always involves responsiveness to reasons, and the person herself, as opposed to a part of her, responds to reasons only when she is conscious of them. This engagement with a core concern in action theory yields the upshot that blameworthiness is fixed by what one is conscious of at the time. And this means it is fixed by what is internal, in a sense of ‘what is internal’ which I clarify in a preliminary chapter. In another chapter, I develop an account on which belief is an exercise of control. More specifically, I develop a new account on which outright belief is irreducible to credence, an account on which outright belief is grounded in a temporally extended activity of organizing one’s attention. This focus on the nature of belief, a central concern in the philosophy of mind, results in a picture on which outright belief can be as much an exercise of control as paradigmatic actions. And if this is right, we should expect the justification of outright belief to be amenable to analysis in terms of blameworthiness.

Being grounded in activity on my picture, outright belief is responsive to practical reasons in additional to evidential reasons. In a later chapter, I develop an account of the relationship between evidential and practical reasons. I argue that evidential reasons are not in general sufficient to settle the question of whether to believe a proposition outright. Then I develop a proposal about how practical reasons can help settle this question. On this proposal, outright belief is correct if true and incorrect if false, but correctness and incorrectness come in degrees which depend on the practical facts. This allows evidential and practical reasons to work together to yield an expected correctness value of outright belief, as against the alternatives of suspension and denial.

Moreover, as I show in a concluding chapter, I have the resources with which to dispatch a kind of dilemma often traced to Sellars and recently revived by Bergmann. When this kind of dilemma is aimed at my picture, it has the following shape. Either the consciousness of reasons which enables justified belief itself involves belief, or it does not. On the first horn, my picture conflicts with foundationalism, for then it implies justified belief always depends on other beliefs. But on the second horn, on which consciousness of reasons does not involve belief, it becomes hard to see why, on my picture, consciousness of reasons is needed for justification. For, so the thought goes, it is precisely what one believes which determines what it is blameworthy for her to do. The heavy-lifting of prior chapters allows us to dispatch with this dilemma. My version of internalism is about outright belief, not credence. Thus, so long as conscious credences can qualify as consciousness of reasons, and so fix what it is blameworthy for one to do, we can preserve both foundationalism about outright belief and our motivation for internalism. The dilemma is dissolved by the different theoretical roles of credence and outright belief.

As the view that justification is to be analyzed in terms of blameworthiness is a version of deontologism in epistemology, another way to put my dissertation’s upshot is that we can give a deontological explanation of why justification should be fixed by what one is conscious of. And since what one is conscious of qualifies as what is internal according to accessibilist versions of internalism, in particular, it is first and foremost accessibilism which deontologism has promise to explain. In this way, my dissertation develops a deontological explanation of accessibilism.

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Flockemann, Richard. "Externalism, self-knowledge and explanation." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1008060.

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In recent years, much attention has been given to the question of whether content externalism is compatible with an account of self-knowledge maintaining that we have an epistemically privileged access to the content of our propositional mental states. Philosophers who maintain the two are incompatible (incompatibilists) have put forward two majors types of challenge, which I call - following Martin Davies - the Achievement and Consequence Problems, which aim to demonstrate that self-knowledge cannot be reconciled with externalism. These challenges have spawned a great deal of literature, and a diverse range of arguments and positions have emerged in response. In this dissertation, I intend to focus on examples of these different avenues of response, and show how none of them are adequate. In the first chapter, I lay the groundwork for the debate, setting up how externalism and self-knowledge are to be understood, and outlining both the incompatibilist challenges as well as the available responses to them. In the second chapter I examine these responses in more detail, concluding finally that the best available response is Tyler Burge's. Burge has two arguments that together establish his compatibilist position. First, he shows that even if externalism is true, our judgements about our occurrent thoughts are immunejrom error. This establishes that our judgements about our thoughts must be true. Second, he offers a transcendental argument for self-knowledge, arguing that our access to our mental states must be not only true, but non-accidentally true, in a way sufficient for genuine knowledge. This establishes that we possess the correct epistemic entitlement to our thoughts. In the third chapter, I argue Burge's arguments do not, in fact, give us good reason to suppose externalism and self-knowledge to be compatible. This, I argue, is because B urge relies upon a transcendental argument, which, in this context, cannot establish that we have self-knowledge if externalism is true. All it establishes, I argue, is that we do possess self-knowledge. And this is insufficient to establish that externalism and self-knowledge are compatible.
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Horridge, Matthew. "Justification based explanation in ontologies." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/justification-based-explanation-in-ontologies(7a9d7700-e12f-43be-93b3-c79966f3a521).html.

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The Web Ontology Language, OWL, is the latest standard in logic based ontology languages. It is built upon the foundations of highly expressive Description Logics, which are fragments of First Order Logic. These logical foundations mean that it is possible to compute what is entailed by an OWL ontology. The reasons for entailments can range from fairly simple localised reasons through to highly non-obvious reasons. In both cases, without tool support that provides explanations for entailments, it can be very difficult or impossible to understand why an entailment holds. In the OWL world, justifications, which are minimal entailing subsets of ontologies, have emerged as the dominant form of explanation. This thesis investigates justification based explanation techniques. The core of the thesis is devoted to defining and analysing Laconic and Precise Justifications. These are fine-grained justifications whose axioms do not contain any superfluous parts. Optimised algorithms for computing these justifications are presented, and an extensive empirical investigation shows that these algorithms perform well on state of the art, large and expressive bio-medical ontologies. The investigation also highlights the prevalence of superfluity in real ontologies, along with the related phenomena of justification masking. The practicality of computing Laconic Justifications coupled with the prevalence of non-laconic justifications in the wild indicates that Laconic and Precise justifications are likely to be useful in practice. The work presented in this thesis should be of interest to researchers in the area of knowledge representation and reasoning, and developers of reasoners and ontology editors, who wish to incorporate explanation generation techniques into their systems.

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Pexton, Mark. "Non-casual explanation in science." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/4872/.

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Non-causal and causal explanation in science are unified under an extension of James Woodward's manipulationist account of causal explanation. Scientific explanation is about capturing and representing the modal structure of the world. Both causal and non-causal explanations often involve implicit and explicit modelling steps. Manipulationism can be extended to models; models have an endogenous set of rules that allow the specification of model analogues of manipulations and explanatory invariances. A pluralist view of explanation is defended. Models can explain despite, and sometimes because of, ineliminable fictions they contain. These fictions do not undermine an ontic account of explanation if the intuitions informing ontic sensibilities are suitably disaggregated. Ontic explanation is a two-levelled process. On the one hand, if we can connect variables with objective modal connections and those variables correspond to entities or properties of entities, or real structures in the world, then we have a correspondence explanation. If, on the other hand, we can still objectively produce modal connections but the ontology of the model is strictly false, then the variable terms do not correspond to real entities. It only appears as if they do, and we have a quasi-explanation. A quasiexplanation is only applicable in a certain empirical domains. This disaggregation has implications for realism. Often explanations will only license an attenuated realistic-or surrealistic- attitude to the ontology of models. This extension of manipulationism to models is far reaching, and as well as unifying many types of scientific explanation it also has applications in pure mathematics.

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Kazez, Jean Rahel. "Mental representation and causal explanation." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185312.

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Mental causation has been a concern in the philosophy of mind since Descartes. Intuitively, thoughts are causes of behavior, and they are causes of behavior in virtue of their mental properties. The computational theory of mind views thoughts as symbol tokenings, and thus as causes. However, if the computational theory of mind is correct, the causal efficacy of mental properties is problematic. A representation tokening causes further representation tokenings or behaviors in virtue of local computational properties of the representation. Mental properties could explain mental causation as well, if they could be identified with, or they supervened upon, causally relevant computational properties of representations. But on plausible construals of the nature of mental properties, they do not. If mental properties are assigned relevance in our mental lives, the result is a picture in which the effects of mental events are overdetermined by their mental and physical properties. Since such overdetermination is implausible, the causal efficacy of mental properties should be denied. A number of philosophers have proposed sufficient conditions for causal relevance and argued that mental properties meet those conditions. The role of mental properties in laws or counterfactuals is taken to be pivotal. But there are serious problems with each of the proposed accounts. A property can play an explanatory role, even if it does not play a causal-explanatory role. The point of assigning mental properties to representations is to account for a system's information processing capacities. Mental properties can play this explanatory role without accounting for cause-effect relationships. The causal efficacy of mental properties can be denied, while an explanatory role for mental properties is maintained.

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Veneri, Alberto <1996&gt. "Forest explanation through pattern discovery." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19007.

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In Machine Learning, some of the most accurate models are practically black-boxes, challenging to be interpreted and analyzed. Consequently, different strategies have been adopted to overcome these limitations, giving birth to a research area called Explainable Artificial Intelligence. In this area, models considered black boxes are Deep Neural Networks and ensemble methods. In particular, even though a single decision tree is considered explainable, tree ensembles are regarded as black-box models due to the large number of trees they typically include. Relevant techniques to explain ensemble of decision (for classification and regression) trees are now mostly based on methods that examine the features and outcome relationships, or create an explanation via tree prototyping or approximate the model through explainable ones. Even though these approaches can give the end-user many meaningful insights into a model and its output, they do not produce a global model explanation by design and/or do not specify the type of interaction between features. In this thesis, we move towards a new way of approaching the model explanation problem over an ensemble of regression trees by discovering frequent patterns inside the forest. A frequent patterns analysis produced from synthetic datasets created by basic algebraic functions has been performed to answer some initial questions: are there some frequent patterns related to a type of algebraic operation between features? If yes, what happens when the model tries to learn a function composed of basic operations? Multiple sub-problems have been addressed to answer the aforementioned issues.

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Huuskonen, Pertti J. "Model-based explanation of plant knowledge /." Espoo : Technical Research Centre of Finland, 1997. http://www.vtt.fi/inf/pdf/publications/1997/P308.pdf.

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David-Rus, Richard. "Explanation and Understanding through Scientific Models." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-111655.

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Schweder, Rebecca. "A unificationist theory of scientific explanation /." Lund : Lund Univ, 2004. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014706727&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Moore, Jenifer Leigh. "Adequate yearly progress leaving explanation behind? /." Diss., Mississippi State : Mississippi State University, 2006. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.

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Dissertations / Theses on the topic 'Explanation'

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