Showing papers on "Abductive reasoning published in 2016"

Theorizing in Grounded Theory and creative abduction

Abstract: This article aims to contribute to the analysis of the logical arguments in Grounded Theory (GT), both in the version of Glaser (Basics of Grounded Theory analysis: emergence versus forcing, 1992) and of Strauss and Corbin (Basics of qualitative research, 1990) The article will focus both on stages of the coding process—that could be considered the core of the overall process of theorizing in GT—both on logic of GT, analysing in particular whether GT makes use of abductive thinking The article outlines the distinction between different modes of abductive reasoning, and focuses specifically on one of them: the “creative abduction” (Eco and Sebeok, The sign of three: Dupin, Holmes, Peirce, 1983) The distinction between different forms of abductive argument appears especially useful to explore the logic of GT By introducing this distinction, the article discuss how theorizing in GT makes use—both in Glaser and in Strauss and Corbin—of the same type of abductive reasoning: the creative abduction According to this analysis, the differences between the two versions of GT turn out to be much less severe

Abductive Reasoning: How Innovators Navigate in the Labyrinth of Complex Product Innovation

Abstract: Complex innovation processes such as drug discovery present challenges to innovators because they must proceed with limited feedback but face a system that involves enormous amounts of information and unknown interdependencies. Organizational scholars suggest that abductive reasoning fits complex situations and may address many of the challenges of complexity. Abductive reasoning is a form of reasoning that generates and evaluates hypotheses in order to make sense of puzzling facts. Existing research on abductive reasoning makes a number of important contributions, but does not explain how innovators can use abductive reasoning to formulate hypotheses for possible new products and then use these hypotheses to navigate in the labyrinth of complex product innovation. We interviewed 85 scientists and managers working in the biopharmaceutical industry and use grounded theory building to develop a new framework. Our framework identifies three social mechanisms that explain how innovators use abductive reasonin...

A rule-based support system for dissonance discovery and control applied to car driving

Abstract: Proposal of a tool to support dissonance discovery and control.Dissonances defined as conflicts into a knowledge base or between knowledge bases.Formalism based on rules to identify dissonances as affordances and inconsistencies.Feasibility study on car driving domain involving five rule bases.Validation involving 20 subjects. This paper is based on the concept of dissonance, that is, gaps or conflicts existing in a specific knowledge base or among different knowledge bases. It presents a rule-based system that assists human operators in dissonance discovery and control by taking into account two kinds of dissonance, i.e., affordance to study conflicts of use, and inconsistencies to study conflicts of intention and action, through the analysis of cognitive behavior implemented in knowledge bases. This system elaborates the knowledge base composed of rules, and analyzes the knowledge content to discover new knowledge by creating additional rules, or to identify inconsistencies when conflicts between rules occur. The affordance discovery control process uses a deductive and an inductive reasoning algorithm of which the aim is to establish new rules using existing ones. The inconsistency discovery control process applies an abductive reasoning algorithm in order to determine contradictory rules when existing rules may result in opposite intentions being accomplished. Two groups of inconsistencies are addressed: interferences involving several decision makers, and contradictions involving the same decision maker. A knowledge acquisition control process facilitates the creation of the initial rules that contain parameters such as intentions relating to the goals to be achieved, actions to be performed to achieve these intentions, objects used to carry out these actions and the decision makers who execute these actions using the corresponding objects. A feasibility study taking into account five rule bases relating to the manual use of an Automated Speed Control System (ASCS), the automated control of the car speed by the ASCS, the manual control of aquaplaning, the manual control of the car speed, and the manual control of car fuel consumption is proposed to validate the rule-based support system.

Realist theory construction for a mixed method multilevel study of neighbourhood context and postnatal depression.

Abstract: We have recently described a protocol for a study that aims to build a theory of neighbourhood context and postnatal depression. That protocol proposed a critical realist Explanatory Theory Building Method comprising of an: (1) emergent phase, (2) construction phase, and (3) confirmatory phase. A concurrent triangulated mixed method multilevel cross-sectional study design was described. The protocol also described in detail the Theory Construction Phase which will be presented here. The Theory Construction Phase will include: (1) defining stratified levels; (2) analytic resolution; (3) abductive reasoning; (4) comparative analysis (triangulation); (5) retroduction; (6) postulate and proposition development; (7) comparison and assessment of theories; and (8) conceptual frameworks and model development. The stratified levels of analysis in this study were predominantly social and psychological. The abductive analysis used the theoretical frames of: Stress Process; Social Isolation; Social Exclusion; Social Services; Social Capital, Acculturation Theory and Global-economic level mechanisms. Realist propositions are presented for each analysis of triangulated data. Inference to best explanation is used to assess and compare theories. A conceptual framework of maternal depression, stress and context is presented that includes examples of mechanisms at psychological, social, cultural and global-economic levels. Stress was identified as a necessary mechanism that has the tendency to cause several outcomes including depression, anxiety, and health harming behaviours. The conceptual framework subsequently included conditional mechanisms identified through the retroduction including the stressors of isolation and expectations and buffers of social support and trust. The meta-theory of critical realism is used here to generate and construct social epidemiological theory using stratified ontology and both abductive and retroductive analysis. The findings will be applied to the development of a middle range theory and subsequent programme theory for local perinatal child and family interventions.

Learning abductive reasoning using random examples

Abstract: We consider a new formulation of abduction in which degrees of "plausibility" of explanations, along with the rules of the domain, are learned from concrete examples (settings of attributes). Our version of abduction thus falls in the "learning to reason" framework of Khardon and Roth. Such approaches enable us to capture a natural notion of "plausibility" in a domain while avoiding the extremely difficult problem of specifying an explicit representation of what is "plausible." We specifically consider the question of which syntactic classes of formulas have efficient algorithms for abduction. We find that the class of k-DNF explanations can be found in polynomial time for any fixed k; but, we also find evidence that even weak versions of our abduction task are intractable for the usual class of conjunctions. This evidence is provided by a connection to the usual, inductive PAC-learning model proposed by Valiant. We also consider an exception-tolerant variant of abduction. We observe that it is possible for polynomial-time algorithms to tolerate a few adversarially chosen exceptions, again for the class of k-DNF explanations. All of the algorithms we study are particularly simple, and indeed are variants of a rule proposed by Mill.

Explicating concepts in reasoning from function to form by two-step innovative abductions

Abstract: The mechanism of design reasoning from function to form is suggested to consist of a two-step inference of the innovative ab- duction type. First is an inference from a desired functional aspect to an idea, concept, or solution principle to satisfy the function. This is followed by a second innovative abduction, from the latest concept to form, structure, or mechanism. The intermediate entity in the logical reasoning, the concept, is thus made explicit, which is significant in following and understanding a specific design process, for educating designers, and to build a logic-based computational model of design. The idea of a two-step ab- ductive reasoning process is developed from the critical examination of several propositions made by others. We use the notion of innovative abduction in design, as opposed to such abduction where the question is about selecting among known alternatives, and we adopt a previously proposed two-step process of abductive reasoning. However, our model is different in that the two abductions used follow the syllogistic pattern of innovative abduction. In addition to using a schematic example from the litera- ture to demonstrate our derivation, we apply the model to an existing, empirically derived method of conceptual design called “parameter analysis” and use two examples of real design processes. The two synthetic steps of the method are shown to follow the proposed double innovative abduction scheme, and the design processes are presented as sequences of double abductions from function to concept and from concept to form, with a subsequent deductive evaluation step.

Explanation for case-based reasoning via abstract argumentation

Abstract: Case-based reasoning (CBR) is extensively used in AI in support of several applications, to assess a new situation (or case) by recollecting past situations (or cases) and employing the ones most similar to the new situation to give the assessment. In this paper we study properties of a recently proposed method for CBR, based on instantiated Abstract Argumentation and referred to as AA-CBR, for problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. In addition, we study properties of explanations in AACBR and define a new notion of lean explanations that utilize solely relevant cases. Both forms of explanations can be seen as dialogical processes between a proponent and an opponent, with the burden of proof falling on the proponent.

The abductive leap: Eliding visual and participatory in research design

Abstract: This chapter seeks to problematise some of our assumptions about visual methods and their role in relation to participatory design and ethics in educational research. We make use of abductive reasoning (Peirce 1878, 1903) to explore the ways in which other researchers, but most specifically the ways we, have attributed causality and connection in this area. Our experience in exploring these assumptions to write this chapter suggests that the use of greater precision and transparency in framing the relationship between the researcher’s intent and the use of visual methods is a vital first step, which can set the context for a more reflective data collection process as well as a more reflexive discussion of intent, design and process.

The making of an expert detective : Thinking and deciding in criminal investigations

Abstract: Fahsing, I.A. (2016). The Making of an Expert Detective: Thinking and Deciding in Criminal Investigations. Department of Psychology, University of Gothenburg, Sweden. Drawing on theoretical frameworks developed in social and cognitive psychology, this thesis examines the degree to which individual and systemic factors may compensate for inherent biases in criminal detectives’ judgments and decision-making. Study I – an interview study – explored criminal detectives’ views of critical factors related to decision making in homicide investigations. Experienced homicide investigators in Norway (n = 15) and the UK (n = 20) were asked to identify decisional ‘tipping point’– decisions that could change detectives’ mind-set from suspect identification to suspect verification together with situational and individual factors relating to these decisions. In a content analysis, two types of decision were identified as typical and potentially critical tipping-points: (1) decisions to point-out, arrest, or charge a suspect, and (2) decisions concerning main strategies and lines of inquiry in the case. Moreover, 10 individual factors (e.g. experience) and 14 situational factors (e.g. who is the victim) were reported as related to the likelihood of mind-set shifts, most of which correspond well with previous decisionmaking research. Study II, using a quasi-experimental design, compared the quality of investigative decisions made by experienced detectives and novice police officers in two countries with markedly different models for the development of investigative expertise (England and Norway). In England, accredited homicide detectives vastly outperformed novice police officers in the number of adequate investigative hypotheses and actions reported. In Norway, however, bachelor educated police novices did marginally better than highly experienced homicide detectives. Adopting a similar design and the same stimulus material, Study III asked if a general test of cognitive abilities used in the selection process at the Norwegian Police University College could predict police students’ ability to generate investigative hypotheses. The findings did not support such a notion and this is somewhat in line with the available knowledge in the area showing that cognitive ability tests have low predictability for applied reasoning tasks. Taken together, this thesis suggests that investigative judgments are highly susceptible to the individual characteristics and biases of the detective. The results indicate that detective-expertise might act as a viable safeguard against biased decision-making, but length of experience alone does not predict sound judgments or decisions in critical stages of criminal investigations. Education and training is a solid foundation for the making of an expert detective. Nevertheless all participants’ researched across the two experiments were biased towards crime and guilt assumptive hypotheses. Hence, true abductive reasoning (i.e. to identify all competing explanations) and the presumption of innocence is hard to operationalise even for expert detectives with extensive

The Iconic Moment. Towards a Peircean Theory of Diagrammatic Imagination

Abstract: Einstein famously said, “Imagination is more important than knowledge”. But how to study imagination and how to represent and communicate what the content of imagination may be in the context of scientific discovery? In 1908 Peirce stated that deduction consists of “two sub-stages”, logical analysis and mathematical reasoning. Mathematical reasoning is further divisible into “corollarial and theorematic reasoning”, the latter concerning an invention of a new icon, or “imaginary object diagram”, while the former results from “previous logical analyses and mathematically reasoned conclusions”. The iconic moment is clearly stated here, as well as the imaginative character of theorematic reasoning. But translating propositions into a suitable diagrammatic language is also needed: A diagram is for Peirce “a concrete but possibly changing mental image of such a thing as it represents”. “A model”, he held, “may be employed to aid the imagination; but the essential thing to be performed is the act of imagining” (MS 616, 1906). Peirce had observed that the importance of imagination in scientific investigation is in supplying an inquirer, not with any fiction but, in quite stark contrast to what fiction is, with “an inkling of truth”. Since Peirce’s limit notion of truth precludes gaining any direct insight into the truth, in rational inquiry the question of what the truth may be or what it could be needs to be tackled by imagination. This imaginative faculty is aided by diagrams which are iconic in nature. The inquirers who imagine the truth “dream of explanations and laws”. Imagination becomes a crucial part of the method for attaining truth, that is, of the logic of science and scientific inquiry, so much so that Peirce took it that “next after the passion to learn there is no quality so indispensable to the successful prosecution of science as imagination”. In this paper we investigate aspects of scientific reasoning and discovery that seem irreplaceably dependent on a Peircean understanding of imagination, abductive reasoning and diagrammatic representations.

The Abductive Theory of Method: Scientific Inquiry and Clinical Practice

Abstract: Clinical reasoning is one of the central components of psychological assessment. The identification of a client's psychological difficulties and the subsequent depiction of their onset, development, and interrelationships enables clinicians to plan treatment in a systematic and effective manner. In an article (Ward, Vertue, & Haig, 1999), we outlined the abductive theory of method (ATOM) and argued that it offered a useful framework for highlighting and integrating the major phases of psychological assessment. These phases involve detecting clinical phenomena, postulating psychological mechanisms, developing a case formulation, and evaluating a case formulation. In this article we present a revised version of the adaptation of ATOM and elaborate on the related clinical dimensions of assessment.

Reasoning And The Logic Of Things

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Bringing Formal and Informal Reasoning Together-A New Era of Assessment?

Abstract: Scientific reasoning represents a set of skills students need to acquire in order to successfully participate in scientific practices. Hence, educational research has focused on developing and validating assessments of student learning that capture the two different components of the construct, namely formal and informal reasoning. In this opinion paper, we explain why we believe that it is time for a new era of scientific reasoning assessments that bring these components together, and how computer-based assessments (CBAs) might accomplish this. Reasoning is a mental process that enables people to construct new representations from existing knowledge (Rips, 2004). It includes cognitive processing that is directed at finding solutions to problems by drawing conclusions based on logical rules or rational procedures (Mayer and Wittrock, 2006). When people reason, they attempt to go “beyond the information given” to create a new representation that is assumed to be true (Bruner, 1957). The process of scientific reasoning comprises formal and informal reasoning (Galotti, 1989; Kuhn, 1993). Formal reasoning is characterized by rules of logic and mathematics, with fixed and unchanging premises (Perkins et al., 1991; Sadler, 2004). It encompasses the ability to formulate a problem, design scientific investigations, evaluate experimental outcomes, and make causal inferences in order to form and modify theories related to the phenomenon under investigation (Zimmerman, 2007). Formal scientific reasoning can be applied not only within the context of science, but in almost every other domain of society (Han, 2013). It can be used to make informed decisions regarding everyday life problems (Amsterlaw, 2006); for example, individuals use proportional reasoning to decide the fastest way to travel from one place to another. In informal reasoning, students draw inferences from uncertain premises as they ponder ill-structured, open-ended, and debatable problems without definitive solutions (Kuhn, 1991). When students reason formally, they work with the given premises in belief mode, which concerns arriving at true and warranted conclusions whereas informal reasoning is carried out in design mode, which focuses on identifying relevant premises that can be used to establish a strong argument (Bereiter and Scardamalia, 2006). Since a premise of informal reasoning is uncertain and can be questioned, its conclusion can be withdrawn in the light of new evidence (Evans, 2005). This process involves weighing the pros and cons of a particular decision (Voss et al., 1991). Learners engage in informal reasoning when they deal with socio-scientific issues—controversial issues that are influenced by social norms and conceptually related to science, such as whether or not to consume genetically modified food or support government's plan for a car-free city (Sadler and Zeidler, 2005). Both types of reasoning are used to manipulate existing information and share the same goal of generating new knowledge. While formal reasoning is judged by whether or not conclusions are valid, informal reasoning is assessed based on the quality of premises and their potential for strengthening conclusions. The manipulation of existing information in formal and informal reasoning processes can be described with dual-process theories of reasoning (Evans, 2007; Glockner and Witteman, 2010). According to these theories, there are two distinct processing modes: Type 1 processes are autonomous and intuitive processes that do not heavily rely on individuals' working memory, whereas Type 2 processes involve using mental simulation or thought experiments to support hypothetical thinking and reflective processes that require working memory (Evans and Stanovich, 2013). An individual's first response to a problem tends to be processed automatically and refers to their past experiences and personal beliefs (i.e., Type 1 process: Evans, 2008). For example, when using formal reasoning to decide the fastest way to travel from A to B, an individual's first thought might be to take a plane since it is commonly considered the fastest means of transport. However, the individual might change his or her mind after processing all necessary information, such as the travel time to and from the airport. Not every individual is able to progress after the first stage and produce a rational decision. Those who are confined to Type 1 processes make intuitive decisions, whereas more experienced individuals utilize Type 2 processes to construct a well-informed choice (Wu and Tsai, 2011). In the example of using informal reasoning to decide whether or not to support a government's plan for a car-free city, intuitive thought might lead individuals to support the plan based on their experiences with pollution. However, with the purpose of generating new representations, only those who can (a) elaborate on their intuitive decision with acceptable justifications; (b) address opposite arguments; and (c) think about how the plan can be further improved are utilizing Type 2 processes. In this regard, there is a strong connection between formal and informal reasoning, in which both types of reasoning share the common goal of generating new knowledge by processing available information through the dual stages. Activity in belief mode covers a broad range of scientific practices in school science (Bereiter and Scardamalia, 2006). Outside the classroom, however, students need to make decisions regarding problems with uncertain premises by working in design mode. Teachers should have ways to assess how students improve on their existing ideas by searching beyond what they already know rather than simply making sure their ideas align with accepted theories. It is therefore important to build a scientific reasoning assessment that incorporates both formal and informal reasoning skills in order to better measure the constructs underlying scientific reasoning. In the following, we argue that these complex skills can be best assessed using computer-based testing.

A Dialogue System for Evaluating Explanations

Abstract: This chapter presents a theory of explanation by building a dialectical system that has speech act rules that define the kinds of moves allowed, such as putting forward an argument, requesting an explanation and offering an explanation. Pre and post-condition rules for the speech acts determine when a particular speech act can be put forward as a move in the dialogue, and what type of move or moves must follow it. This chapter offers a dialogue structure with three stages, an opening stage, an explanation stage and a closing stage, and shows how an explanation dialogue can shift to other types of dialogue known in argumentation studies such as persuasion dialogue and deliberation dialogue. Such shifts can go from argumentation to explanation and back again. The problem of evaluating explanations is solved by extending the hybrid system of (Bex, Arguments, stories and criminal evidence: a formal hybrid theory. Springer, Dordrecht, 2011) which combines explanations and arguments to include a method of testing stories called examination dialogue. In this type of dialogue an explanation can be probed and tested by arguments. The result is a method of evaluating explanations.

Taking Advantage of Emergence: Productively Innovating in Complex Innovation Systems

Abstract: Our most pressing societal problems such as enhancing health care, developing alternate energy, revitalizing cities, and advancing the economy are complex innovation systems. Leveraging the enormous potential of sciences and technologies into better resolutions for these complex challenges requires a transformation in the social technologies we use to tap this potential. The thesis of this book is that we can grapple with complex innovation systems only by taking advantage of emergence. This book creates a theoretical framework of three new social technologies for taking advantage of emergence in infrastructures of complex innovation systems. The central social technology is abduction, the logic of discovery, for figuring out solutions to complex problems. Abductive reasoning differs significantly from deductive confirmation and simple rationality. The book details three abductive learning routines that enable innovators to grab up noisy and fragmented information, synthesize it into hypothesized configurations that capture the inherent ambiguity, evaluate these configurations by exploring consequences and contingencies, and reframe to accumulate the learning. The second social technology divides the infrastructure into four distinct but entangled subsystems of interpersonal action: the project, knowledge system, strategic, and institutional subsystems. Each subsystem is a vast multi-organizational network that must address its distinct problem if the infrastructure overall is to productively innovate. The author shows how cycling through abductive learning routines overcomes problems in each subsystem that conventional approaches cannot deal with. The third social technology is a new way of organizing based on heterarchy, not hierarchy, with roles and relations defined through heedful interrelating.

How Can Students Generalize the Chain Rule? The Roles of Abduction in Mathematical Modeling.

Abstract: The purpose of this study is to design a modeling task to facilitate students’ inquiries into the chain rule in calculus and to analyze the results after implementation of the task. Researchers have pointed out that students may encounter difficulties in understanding the chain rule due to the formal approaches used to teach and learn this concept. In this study, we take a modeling approach to the teaching and learning of the chain rule by facilitating the generalization of students’ models and modeling activities. We assumed abductive reasoning to be one of the key factors which can support the generalization of students’ models and modeling activities. We believe that analogical reasoning and diagrammatic reasoning are key factors in fostering students’ use of abduction. As a result, we determined that the students’ models and modeling activities were generalized to the chain rule by their use of abductive reasoning, and the students found the chain rule to be a generalized rule for describing changes of various quantities.

Context as Relevance-Driven Abduction and Charitable Satisficing

Abstract: It has been widely assumed that the full meaning of a linguistic expression can be grasped only within a situation, the context of the utterance. There is even agreement that certain factors within the situation are particularly significant, including gestures and facial expressions of the participants, their social roles, the setting of the exchange, the objects surrounding the participants, the linguistic, cultural and educational backgrounds of the participants, their beliefs, including those concerning the situation, the social procedures and conventions that regulate the situation. Finally, there is some agreement that context is dynamic, reflexive (the speakers are mutually aware of their beliefs), not limited to linguistics actions, and last but not least, a psychological construct. This definition of context is not (very) controversial, but it leaves out two major problems, which will be addressed in this paper: how is context arrived at? And, since a perfectly natural interpretation of the above definition could be that the context of each utterance is the entire universe, how is the relevant context delimited? Four related concepts will provide the answer to both questions: abductive reasoning, driven by relevance and cooperation, and bounded rationality and the principle of charity. Simply put, context is derived abductively by the speakers assuming that for the speakers to behave the way they behave and do so rationally, a given context must be available to them. The context is bounded by the simple requirement that speakers not try to optimize their interpretation/calculation, but rather satisfice, i.e., find the first acceptable solution and by the need to follow the principle of charity, which forces intersubjective agreement. Thus, abductive reasoning and bounded rationality will be shown to be sufficient to calculate the relevant context of utterances (or other rationality-driven interactions) and to effectively delimit the potentially infinite search space that must be explored to do so.

Argument-Based Logic Programming for Analogical Reasoning

Abstract: Analogical reasoning can be understood as a kind of resemblance of one thing to another, thus assigning properties from one context to another. The key idea is to use similarity information to support an inference which cannot be deductively inferred. In this paper, we present a formal and intuitive framework of this phenomena using an argument-based logic-programming-like language. A proof theory of our system is stated in the dialectical style, where a proof takes the form of dialogue between a proponent and an opponent of an argument. We also discuss how the proposed framework can be fine tuned for optimistic analogical reasoning and pessimistic analogical reasoning. Finally, we discuss a design sketch of our proposed analogical reasoner called Analogist.

Meaning in Dialogue: An Interactive Approach to Logic and Reasoning

Abstract: This book argues for a view in which processes of dialogue and interaction are taken to be foundational to reasoning, logic, and meaning. This is both a continuation, and a substantial modification, of an inferentialist approach to logic. As such, the book not only provides a critical introduction to the inferentialist view, but it also provides an argument that this shift in perspective has deep and foundational consequences for how we understand the nature of logic and its relationship with meaning and reasoning. This has been upheld by several technical results, including, for example a novel approach to logical paradox and logical revision, and an account of the internal justification of logical rules. The book shows that inferentialism is greatly strengthened, such that it can answer the most stringent criticisms of the view. This leads to a view of logic that emphasizes the dynamics of reasoning, provides a novel account of the justification and normativity of logical rules, thus leading to a new, attractive approach to the foundations of logic. The book addresses readers interested in philosophy of language, philosophical and mathematical logic, theories of reasoning, and also those who actively engage in current debates involving, for example, logical revision, and the relationship between logic and reasoning, from advanced undergraduates, to professional philosophers, mathematicians, and linguists.

Taking Advantage of Emergence: Productively Innovating in Complex Innovation Systems

Abstract: Our most pressing societal problems such as enhancing health care, developing alternate energy, revitalizing cities, and advancing the economy are complex innovation systems Leveraging the enormous potential of sciences and technologies into better resolutions for these complex challenges requires a transformation in the social technologies we use to tap this potential The thesis of this book is that we can grapple with complex innovation systems only by taking advantage of emergence This book creates a theoretical framework of three new social technologies for taking advantage of emergence in infrastructures of complex innovation systems The central social technology is abduction, the logic of discovery, for figuring out solutions to complex problems Abductive reasoning differs significantly from deductive confirmation and simple rationality The book details three abductive learning routines that enable innovators to grab up noisy and fragmented information, synthesize it into hypothesized configurations that capture the inherent ambiguity, evaluate these configurations by exploring consequences and contingencies, and reframe to accumulate the learning The second social technology divides the infrastructure into four distinct but entangled subsystems of interpersonal action: the project, knowledge system, strategic, and institutional subsystems Each subsystem is a vast multi-organizational network that must address its distinct problem if the infrastructure overall is to productively innovate The author shows how cycling through abductive learning routines overcomes problems in each subsystem that conventional approaches cannot deal with The third social technology is a new way of organizing based on heterarchy, not hierarchy, with roles and relations defined through heedful interrelating

On Structural Properties to Improve FMEA-Based Abductive Diagnosis

Abstract: Abductive Model-Based Diagnosis (MBD) provides an intuitive approach to fault identification by reasoning on a description of the system to be diagnosed. Nevertheless, its computational complexity hinders a vast adoption and thus motivates further evaluation of efficient methods. In this paper, we investigate the structural metrics inherent to models and diagnosis problems generated on the basis of Failure Mode Effect Analysis (FMEA). Proceeding on the metrics developed, we investigate their potential as classification features to identify the most suitable diagnosis algorithm for a particular diagnosis problem. Evaluated on artificial and practical samples, our approach shows that the classifier trained on the described metrics is able to indicate the most efficient method in case of a specific diagnosis scenario.

Autistic Learning and Cognition

Abstract: The focus of this chapter is autistic learning and cognition. We explore the difficullties humans and machines share in learning the external world and focus on how it happens in case of autism. Firstly, the framework of active learning is introduced which is the basis of our model for autistic adaptation. We start with a hypersensitivity of an autistic learning system and explain how it leads to repetitive patterns, stereotypy and ignorance behavior. We then introduce hybrid deductive, inductive and abductive reasoning system Jasmine and reproduce the scenarios of autistic learning.

A Bayesian Network for the Cognitive Diagnosis of Deductive Reasoning

Abstract: In our previous works, we presented Logic-Muse as an ITS that helps improve logical reasoning skills in multiple contexts. All its three main components (the learner, tutor and expert models) have been developed while relying on the help of experts and on important work in the field of reasoning and computer science. The main purpose of this paper is to present and assess the Bayesian Network (that allows real time diagnosis and modeling of the learner’s state of knowledge) implemented in the learner component. We demonstrate the prediction and the adaptive capabilities for our learner model by using data mining techniques on data from 71 students. We believe this work will help the research community in building and assessing a BN in an ITS that teach logical reasoning.

Abduction, Complex Inferences, and Emergent Heuristics of Scientific Inquiry

Abstract: The roles of abductive inference in dynamic heuristics allows scientific methodologies to test novel explanations for the world’s ways Deliberate reasoning often follows abductive patterns, as well as patterns dominated by deduction and induction, but complex mixtures of these three modes of inference are crucial for scientific explanation All possible mixed inferences are formulated and categorized using a novel typology and nomenclature Twenty five possible combinations among abduction, induction, and deduction are assembled and analyzed in order of complexity There are five primary categories for sorting these inferential procedures: fallacies, non-scientific procedures, quasi-scientific procedures, scientific procedures, and scientific heuristics

Explanatory Judgment, Probability, and Abductive Inference

Abstract: Abductive reasoning assigns special status to the explanatory power of a hypothesis But how do people make explanatory judgments? Our study clarifies this issue by asking: (i) How does the explanatory power of a hypothesis cohere with other cognitive factors? (ii) How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues In the responses, we isolated three constructs: Explanatory Value, Rational Acceptability, and Entailment Explanatory judgments strongly cohered with judgments of causal relevance and with a sense of understanding Furthermore, we found that Explanatory Value was sensitive to manipulations of statistical relevance relations between hypothesis and evidence, but not to explicit information about the prior probability of the hypothesis These results indicate that probabilistic information about statistical relevance is a strong determinant of Explanatory Value More generally, our study suggests that abductive and probabilistic reasoning are two distinct modes of inference

Applications of the Lacanian theory of discourses in the field of Communication

Abstract: This article discusses the application of Lacan’s discourse theory in the field of Communication. Based on bibliographical research, which includes texts of the author illustrating the proposed appli -cation, its purpose is to examine modes of use of this tool and the recourse to it as an instrument of interdisciplinary articulation. It is shown that the discourse theory serves as factor of rapprochement and differentiation, pivot for abductive reasoning, framework for historical evolution, criterion for systematization and underlying method, and that the interface between Communication and Psy-choanalysis brought about by the application of this theory involves the notion of Psychoanalysis in extension and operations of conceptual import, export and contextualization. It follows that this application is useful for research not only in Communication but even in Psychoanalysis, in addition to contributing to the understanding of the broader social context. Keywords : Discourses. Lacan. Communication. Media. Epistemology.