Showing papers on "Abductive reasoning published in 2021"

Active Inference and Abduction

Abstract: The background target of the research going into the present article is to forge an intellectual alliance between, on the one hand, active inference and the free-energy principle (FEP), and on the other, Charles S. Peirce’s theory of semiotics and pragmatism. In the present paper, the focus is on the allegiance between the nomenclatures of active and abductive inferences as the proper place to begin reaching at that wider target. The paper outlines the key conceptual elements involved in a naturalistic rendering of Peirce’s late semiotic and logical notion of abductive reasoning. The target is a cognitive-biological model of abduction which preserves the functional integrity of an organism and fulfils the existential imperative for living beings’ evidence of existence. Such a model is an adaptation of Peirce’s late logical schema of abduction proposed in his largely unpublished works during the early 20th century. The proposed model is argued to be a feasible sketch also of recent breakthroughs in computational (sensu Bayesian) cognitive science.

Modeling as Scientific Reasoning—The Role of Abductive Reasoning for Modeling Competence

Abstract: While the hypothetico-deductive approach, which includes inductive and deductive reasoning, is largely recognized in scientific reasoning, there is not much focus on abductive reasoning. Abductive reasoning describes the theory-based attempt of explaining a phenomenon by a cause. By integrating abductive reasoning into a framework for modeling competence, we strengthen the idea of modeling being a key practice of science. The framework for modeling competence theoretically describes competence levels structuring the modeling process into model construction and model application. The aim of this theoretical paper is to extend the framework for modeling competence by including abductive reasoning, with impact on the whole modeling process. Abductive reasoning can be understood as knowledge expanding in the process of model construction. In combination with deductive reasoning in model application, such inferences might enrich modeling processes. Abductive reasoning to explain a phenomenon from the best fitting guess is important for model construction and may foster the deduction of hypotheses from the model and further testing them empirically. Recent studies and examples of learners’ performance in modeling processes support abductive reasoning being a part of modeling competence within scientific reasoning. The extended framework can be used for teaching and learning to foster scientific reasoning competences within modeling processes.

Threshold concepts, obstacles or scientific dead ends?

Abstract: Educational researchers have concluded that there are threshold concepts in a large number of disciplines. Yet, these researchers have not paid enough attention to the objection to the theory. It i...

Abduction and styles of scientific thinking

Abstract: In philosophy of science, the literature on abduction and the literature on styles of thinking have existed almost totally in parallel. Here, for the first time, we bring them together and explore their mutual relevance. What is the consequence of the existence of several styles of scientific thinking for abduction? Can abduction, as a general creative mode of inference, have distinct characteristic forms within each style? To investigate this, firstly, we present the concept of abduction; secondly we analyze what is understood by styles of thinking; thirdly, we give some comments on abduction and styles of thinking by analyzing examples of scientific discovery or innovation within each style. We develop a case-based comparative investigation of creative aspects of abductive reasoning with examples drawn from different styles of scientific thinking and doing as understood by the Crombie/Hacking tradition. We argue that abduction, as a general mode of reasoning, can have a variety of specific expressions enabled and constrained by the styles of scientific thinking. Finally, we draw some conclusions on the relationship between abduction and styles of thinking suggesting that scientific discovery is a dynamical goal-directed activity within the scientific community that benefits from distinct styles of thinking and doing research.

Integrating Theory in Co-design: An Abductive Approach:

Abstract: Co-design empowers people, giving them a voice in social marketing program design; however, approaches have mostly excluded expert knowledge. An abductive approach to co-design allows for inclusion...

Creative leaps in theory: the might of abduction.

Abstract: This paper argues that abductive reasoning has a central place in theorizing Health Professions Education. At the root of abduction lies a fundamental debate: How do we connect practice, which is always singular and unique, with theory, which describes the world in terms of rules, generalizations, and universals? While abduction was initially seen as the ‘poor cousin’ of deduction and induction, ultimately it has something important to tell us about the role of imagination and humility in theorizing Health Professions Education. It is that which makes theory possible, because it allows us to ask what might be the case and calls attention to the role of creative leaps in theory. Becoming aware of the abductive reasoning we already perform in our research allows us to take the role of imagination—something rarely associated with theory—seriously.

From da Vinci's Flying Machines to a Theory of the Creative Process.

Abstract: Steps involved in the creative process have been described in previous research, yet the exact nature of the process still remains unclear. In the current study, we take this investigation further,...

Abductive reasoning in nursing: Challenges and possibilities.

Abstract: Abduction, deduction and induction are different forms of inference in science. However, only a few attempts have been made to introduce the idea of abductive reasoning as an extended way of thinking about clinical practice in nursing research. The aim of this paper was to encourage critical reflections about abductive reasoning based on three empirical examples from nursing research and includes three research questions on what abductive reasoning is, how the process has taken place, and how knowledge about abductive reasoning based on the examples can inform nursing research and clinical practice. The study has a descriptive and explorative approach using a convenience sample of three empirical studies from nursing research. The three studies illustrate different ways to enter the abductive reasoning process in steps. They represent new caring models, which offer visual and cognitive maps for expanding nursing research, education and thus informing care. Therefore, we suggest that abductive reasoning may be beneficial for different ways of knowing and demonstrates scientific innovation to shed new light on health phenomena, which can help researchers and practitioners to gain a broader and deeper understanding of nursing care inquiry. However, more studies are needed to broaden this scope.

Abductive Reasoning as the Integrating Mechanism between First- Second- and Third-Person Practice in Action Research

Abstract: Action research has long adopted an integrative approach to research as incorporating three inquiries and voices: the first-person voice of individuals inquiring into their own thinking and learning, the second- person inquiry into the collaborative engagements between the actors as co-researchers and the third-person contribution to knowledge for a wider audience. Third-person theory seeks to integrate among the first- and second-person practices, linking the subjective dynamics of action and inquiry (within the first-person), the intersubjective collaborative dynamics of action and inquiry (between second-persons engaged with one another) and the outcome of actionable knowledge (among a collection of third-persons-and-things at a distance from and often anonymous-to-one another). Drawing on Peirce’s articulation of abductive reasoning this article explores how abductive reasoning serves as the integrating mechanism between first-, second- and third-person practice and informs both the theory of how theory is generated through first- and second-person practices.

Automated activity and progress analysis based on non-monotonic reasoning of construction operations

Abstract: PurposeReal-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.Design/methodology/approachThe presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.FindingsThis work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.Research limitations/implicationsThe KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.Practical implicationsThe presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.Originality/valueWhile automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.

An Abductive Inference Approach to Assess the Performance-Enhancing Effects of Drugs Included on the World Anti-Doping Agency Prohibited List

Abstract: Some have questioned the evidence for performance-enhancing effects of several substances included on the World Anti-Doping Agency's Prohibited List due to the divergent or inconclusive findings in randomized controlled trials (RCTs). However, inductive statistical inference based on RCTs-only may result in biased conclusions because of the scarcity of studies, inter-study heterogeneity, too few outcome events, or insufficient power. An abductive inference approach, where the body of evidence is evaluated beyond considerations of statistical significance, may serve as a tool to assess the plausibility of performance-enhancing effects of substances by also considering observations and facts not solely obtained from RCTs. Herein, we explored the applicability of an abductive inference approach as a tool to assess the performance-enhancing effects of substances included on the Prohibited List. We applied an abductive inference approach to make inferences on debated issues pertaining to the ergogenic effects of recombinant human erythropoietin (rHuEPO), beta2-agonists and anabolic androgenic steroids (AAS), and extended the approach to more controversial drug classes where RCTs are limited. We report that an abductive inference approach is a useful tool to assess the ergogenic effect of substances included on the Prohibited List-particularly for substances where inductive inference is inconclusive. Specifically, a systematic abductive inference approach can aid researchers in assessing the effects of doping substances, either by leading to suggestions of causal relationships or identifying the need for additional research.

An Ethnomethodological Study of Abductive Reasoning While Tinkering

Abstract: Tinkering is often viewed as arbitrary practice that should be avoided. However, tinkering can be performed as part of a sound reasoning process. In this ethnomethodological study, we investigated ...

Competing hypotheses and abductive inference

Abstract: This paper explores the nature of competition between hypotheses and the effect of failing to model this relationship correctly when performing abductive inference. In terms of the nature of competition, the importance of the interplay between direct and indirect pathways, where the latter depends on the evidence under consideration, is investigated. Experimental results show that models which treat hypotheses as mutually exclusive or independent perform well in an abduction problem that requires identifying the most probable hypothesis, provided there is at least some positive degree of competition between the hypotheses. However, even in such cases a significant limitation of these models is their inability to identify a second hypothesis that may well also be true.

Why the Data Revolution Needs Qualitative Thinking

Abstract: This essay draws on qualitative social science to propose a critical intellectual infrastructure for data science of social phenomena. Qualitative sensibilities—interpretivism, abductive reasoning, and reflexivity in particular—could address methodological problems that have emerged in data science and help extend the frontiers of social knowledge. First, an interpretivist lens—which is concerned with the construction of meaning in a given context—can enable the deeper insights that are requisite to understanding high-level behavioral patterns from digital trace data. Without such contextual insights, researchers often misinterpret what they find in large-scale analysis. Second, abductive reasoning—which is the process of using observations to generate a new explanation, grounded in prior assumptions about the world—is common in data science, but its application often is not systematized. Incorporating norms and practices from qualitative traditions for executing, describing, and evaluating the application of abduction would allow for greater transparency and accountability. Finally, data scientists would benefit from increased reflexivity—which is the process of evaluating how researchers’ own assumptions, experiences, and relationships influence their research. Studies demonstrate such aspects of a researcher’s experience that typically are unmentioned in quantitative traditions can influence research findings. Qualitative researchers have long faced these same concerns, and their training in how to deconstruct and document personal and intellectual starting points could prove instructive for data scientists. We believe these and other qualitative sensibilities have tremendous potential to facilitate the production of data science research that is more meaningful, reliable, and ethical.

Generating mathematical knowledge in the classroom through proof, refutation, and abductive reasoning

Abstract: Proving and refuting are fundamental aspects of mathematical practice that are intertwined in mathematical activity in which conjectures and proofs are often produced and improved through the back-and-forth transition between attempts to prove and disprove. One aspect underexplored in the education literature is the connection between this activity and the construction by students of knowledge, such as mathematical concepts and theorems, that is new to them. This issue is significant to seeking a better integration of mathematical practice and content, emphasised in curricula in several countries. In this paper, we address this issue by exploring how students generate mathematical knowledge through discovering and handling refutations. We first explicate a model depicting the generation of mathematical knowledge through heuristic refutation (revising conjectures/proofs through discovering and addressing counterexamples) and draw on a model representing different types of abductive reasoning. We employed both models, together with the literature on the teachers’ role in orchestrating whole-class discussion, to analyse a series of classroom lessons involving secondary school students (aged 14–15 years, Grade 9). Our analysis uncovers the process by which the students discovered a counterexample invalidating their proof and then worked via creative abduction where a certain theorem was produced to cope with the counterexample. The paper highlights the roles played by the teacher in supporting the students’ work and the importance of careful task design. One implication is better insight into the form of activity in which students learn mathematical content while engaging in mathematical practice.

From LSAT: The Progress and Challenges of Complex Reasoning.

Abstract: Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark of human intelligence, it involves a degree of explicit reading comprehension, interpretation of logical knowledge and complex rule application. In this paper, we take a step forward in complex reasoning by systematically studying the three challenging and domain-general tasks of the Law School Admission Test (LSAT), including analytical reasoning, logical reasoning and reading comprehension. We propose a hybrid reasoning system to integrate these three tasks and achieve impressive overall performance on the LSAT tests. The experimental results demonstrate that our system endows itself a certain complex reasoning ability, especially the fundamental reading comprehension and challenging logical reasoning capacities. Further analysis also shows the effectiveness of combining the pre-trained models with the task-specific reasoning module, and integrating symbolic knowledge into discrete interpretable reasoning steps in complex reasoning. We further shed a light on the potential future directions, like unsupervised symbolic knowledge extraction, model interpretability, few-shot learning and comprehensive benchmark for complex reasoning.

Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize? (Forthcoming in Organization Science)

Abstract: Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g. through data reduction and automation of data coding, or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this Organization Science Perspective-paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part due to scholars’ inherent distaste for “predictions without explanations” that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate “algorithm supported induction,” yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.

Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming

Abstract: Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

Creativity and successful product concept selection for innovation

Abstract: Selecting novel product concepts for further development into successful innovations entails decision making under conditions of high uncertainty. The literature discusses several influencing facto...

Nature Chose Abduction: Support from Brain Research for Lipton’s Theory of Inference to the Best Explanation

Abstract: This paper presents arguments and evidence from psychology and neuroscience supporting Lipton’s 2004 claim that scientists create knowledge through an abductive process that he calls “Inference to the Best Explanation”. The paper develops two conclusions. Conclusion 1 is that without conscious effort on our part, our brains use a process very similar to abduction as a powerful way of interpreting sensory information. To support Conclusion 1, evidence from psychology and neuroscience is presented that suggests that what we humans perceive through our senses is not reality, but rather, our ‘brain’s “best guess” of the causes of its sensory input. The implication of this best guessing is that our brains use a process very similar to abduction throughout our lives to inform us of what is happening in the world around us. In addition, an argument based on Darwinian evolution is presented claiming that our brains do an excellent job of interpreting sensory information from the outside world. (If they did not, we, as a species, could hardly have survived.) Combining these two claims leads to Conclusion 1. Building on Conclusion 1, Conclusion 2 is that Lipton and others are correct in claiming that scientists use abduction when creating scientific theories. Abduction must be strong, because Nature chose abduction for its own sensemaking purposes. This paper’s contribution to knowledge is in pointing out that recent psychological and neuroscientific research has major implications for the philosophical world’s confidence in the probable validity of abductive inference. The punchline is simple: Nature chose abduction!

Data-driven abductive inference of library specifications

Abstract: Programmers often leverage data structure libraries that provide useful and reusable abstractions. Modular verification of programs that make use of these libraries naturally rely on specifications that capture important properties about how the library expects these data structures to be accessed and manipulated. However, these specifications are often missing or incomplete, making it hard for clients to be confident they are using the library safely. When library source code is also unavailable, as is often the case, the challenge to infer meaningful specifications is further exacerbated. In this paper, we present a novel data-driven abductive inference mechanism that infers specifications for library methods sufficient to enable verification of the library's clients. Our technique combines a data-driven learning-based framework to postulate candidate specifications, along with SMT-provided counterexamples to refine these candidates, taking special care to prevent generating specifications that overfit to sampled tests. The resulting specifications form a minimal set of requirements on the behavior of library implementations that ensures safety of a particular client program. Our solution thus provides a new multi-abduction procedure for precise specification inference of data structure libraries guided by client-side verification tasks. Experimental results on a wide range of realistic OCaml data structure programs demonstrate the effectiveness of the approach.

Enhancing clinical judgement in virtual care for complex chronic disease.

Abstract: The COVID-19 pandemic has transformed traditional in-person care into a new reality of virtual care for patients with complex chronic disease (CCD), but how has this transformation impacted clinical judgement? I argue that virtual specialist-patient interaction challenges clinical reasoning and clinical judgement (clinical reasoning combined with statistical reasoning). However, clinical reasoning can improve by recognising the abductive, deductive, and inductive methods that the clinician employs. Abductive reasoning leading to an inference to the best explanation or invention of an explanatory hypothesis is the default response to unfamiliar or confusing situations. Deductive reasoning supports a previously established goal, but deductive accuracy requires sound premises leading to a valid conclusion. Inductive reasoning uses efficient data sorting, data interpretation, and plan creation without a previously established goal, and allows assessing inferential accuracy over time. In all cases, communication remains the backbone of the clinical encounter. Virtual care for CCD challenges clinical judgement by reducing available information, so even experienced specialists who use induction might default to deduction or abduction. The visit might shorten, decreasing narrative competence and in-turn management quality. Clinical judgement in virtual encounters can be enhanced by allowing sufficient time, employing allied health staff, using an advance script, avoiding dogmatic commitment to either virtual or in-person encounters, special training in virtual care, and conscious awareness of abductive, deductive, and inductive reasoning processes. Clinical judgement in virtual encounters especially calls for Gestalt cognition to assess a situational pattern irreducible to its parts and independent of its particulars, so that efficient data interpretation and self-reflection are enabled. Gestalt cognition integrates abduction, deduction, and induction, appropriately divides the time and effort spent on each, and can compensate for reduced available information. Evaluating one's clinical judgement for those components especially vulnerable to compromise can help optimize the delivery of virtual care for patients with CCD.

Students’ Thinking Process When Using Abductive Reasoning in Problem Solving

Abstract: Background : Abductive reasoning is the process of making conjectures to explain surprising observations. Although this conjecture is not certain to be true, in solving a problem, this reasoning is very helpful to determine the best solution strategy. Objectives : The study aims to investigate whether all types of abductive reasoning lead to the formation of new schemes. Design : This research used a qualitative approach with a descriptive exploratory design. Setting and Participants : A total of 41 students of the bachelor of mathematics education program were involved in solving a task and then 8 persons were chosen to be deeply interviewed which representing the types of undercoded and overcoded abductive reasoning. Data collection and analysis : The collected data were the results of the students’ works and task-based interviews. Piaget's schema theory was used to analyze students' thinking processes using abductive reasoning. The analysis was carried out at all steps of problem solving, namely understanding the problem, devising a plan, carrying out the plan, and looking back. Results : Those who carried out overcoded abductive reasoning used this reasoning to determine problem solving strategies. Meanwhile, those who carried out undercoded abductive reasoning used it to determine problem solving strategies as well as to form new schemes. Conclusions : The results showed that students who did abductive reasoning did not always produce new schemes. This study also notes that the truth value of answers from the application of abductive reasoning in problem solving was open and the importance of the look back step to perform accommodation.

Arrested Development: On Instinct and Reasoning in C. S. Peirce’s Philosophy

Abstract: According to the founder of pragmatism Charles S. Peirce, instinct and reasoning complement each other as cognitive tools. Peirce’s idea of instinct shows close affinity to other key components in his architectonic (such as habit and symbol), for it contains generality and is determined to a quasi-purpose. However, it is often studied in relation to il lume naturale, musement, and abductive inference as well, because it provides compressed meanings to serve as premises for further thought and action and in definite respects it escapes all control. Moreover, Peirce conceived science as a development of instinct and proposed an elaborate classification of human instincts. This paper aims to delve into the interaction between instinct and reasoning as conceived by Peirce by elaborating on his hypothesis that the rational mind evolved out of the instinctive mind by a process of arrested development, in which the encounter with error seems to have a major role. The proposed continuum of development from instinct to reasoning, however, seems contradictory to the established interpretation of Peirce’s evolutionary cosmogony and his semiotic doctrine as tracing significant relations in the order of ascending generality. Are our refined cognitive abilities of ratiocination infantile compared with the unerring capacity of instinct? Could the rational mind be the result of a lucky digress in the evolution of semiosis? Is our capacity for intellectual blundering a curse or a blessing? Problems such as those need to be tackled when considering the interplay between—as well as the very possibility for—instinctive and inferential cognition.

Learning Event Graph Knowledge for Abductive Reasoning

Abstract: Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering. To facilitate this task, a narrative text based abductive reasoning task 𝛼NLI is proposed, together with explorations about building reasoning framework using pretrained language models. However, abundant event commonsense knowledge is not well exploited for this task. To fill this gap, we propose a variational autoencoder based model ege-RoBERTa, which employs a latent variable to capture the necessary commonsense knowledge from event graph for guiding the abductive reasoning task. Experimental results show that through learning the external event graph knowledge, our approach outperforms the baseline methods on the 𝛼NLI task.

Abductive Spaces: Modeling Concept Framework Revision with Category Theory

Abstract: A formal model of abductive inference is provided in which abduction is conceived as expansive and contractive movements through a topological space of theoretical and practical commitments. A pair of presheaves over the (Heyting algebra) space of commitments corresponds to communities sharing commitments on the one hand and possible obstructions to commitments on the other. In this framework, abductive inference is modeled by the dynamics of redistributed communities of commitment made in response to obstructive encounters. This semantic-pragmatic model shows how elementary category theory tools can be used to formalize abductive inference while hewing close to ordinary intuitions about collective agency and reasoning.

Generating Hypothetical Events for Abductive Inference.

Abstract: Abductive reasoning starts from some observations and aims at finding the most plausible explanation for these observations. To perform abduction, humans often make use of temporal and causal inferences, and knowledge about how some hypothetical situation can result in different outcomes. This work offers the first study of how such knowledge impacts the Abductive NLI task – which consists in choosing the more likely explanation for given observations. We train a specialized language model LMI that is tasked to generate what could happen next from a hypothetical scenario that evolves from a given event. We then propose a multi-task model MTL to solve the Abductive NLI task, which predicts a plausible explanation by a) considering different possible events emerging from candidate hypotheses – events generated by LMI – and b) selecting the one that is most similar to the observed outcome. We show that our MTL model improves over prior vanilla pre-trained LMs fine-tuned on Abductive NLI. Our manual evaluation and analysis suggest that learning about possible next events from different hypothetical scenarios supports abductive inference.