Model-Based and Manipulative Abduction in Science

Magnani, Lorenzo

doi:10.1023/b:foda.0000042841.18507.22

Cited by 58 publications

(37 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The best explanation is the one that maximizes the probabilityj9(configuration | evidence). Abduction in this case stands for the most plausible explanatory diagnostic criteria, in line with the model-based abduction concept defined by Magnani (2004).…”

Section: Probabilistic Distributions On the Propagation When Decidingmentioning

confidence: 76%

“…Assessing the most probable explanation in the data relations. Magnani (2004) has claimed that abduction can be used to study cognitive activity in many areas of model-based reasoning. Abduction, or abduction inference, can be defined as "the process of obtaining the most plausible explanations for a sequence of observed facts."…”

Section: Probabilistic Distributions On the Propagation When Decidingmentioning

confidence: 99%

See 1 more Smart Citation

What is behind a summary-evaluation decision?

Zipitria

Larrañaga

Armañanzas

et al. 2008

Behavior Research Methods

View full text Add to dashboard Cite

Research in psychology has reported that, among the variety of possibilities for assessment methodologies, summary evaluation offers a particularly adequate context for inferring text comprehension and topic understanding. However, grades obtained in this methodology are hard to quantify objectively. Therefore, we carried out an empirical study to analyze the decisions underlying human summary-grading behavior. The task consisted of expert evaluation of summaries produced in critically relevant contexts of summarization development, and the resulting data were modeled by means of Bayesian networks using an application called Elvira, which allows for graphically observing the predictive power (if any) of the resultant variables. Thus, in this article, we analyzed summary-evaluation decision making in a computational framework.

show abstract

Section: Probabilistic Distributions On the Propagation When Decidingmentioning

confidence: 76%

Section: Probabilistic Distributions On the Propagation When Decidingmentioning

confidence: 99%

What is behind a summary-evaluation decision?

Zipitria

Larrañaga

Armañanzas

et al. 2008

Behavior Research Methods

View full text Add to dashboard Cite

show abstract

“…The weak form of inference uses distributed cognition according to three perspectives: 1) physically distributed (use of external objects, affordances, artefacts and tools to support intelligent activity); 2) socially distributed cognition (social interaction, collaboration and social practices enhancing the activity); 3) temporally distributed cognition (use of representations, tools, methods, etc.). However, the aspects of distributed cognition can be extended to involve embodied aspects, which were discussed by Magnani (2004). For Magnani (2004), manipulative abduction brings forward "information" about the senses and implicit meanings that derive from the body proper (i.e., tacit knowledge).…”

Section: Weak Form Of Inferencementioning

confidence: 99%

“…However, the aspects of distributed cognition can be extended to involve embodied aspects, which were discussed by Magnani (2004). For Magnani (2004), manipulative abduction brings forward "information" about the senses and implicit meanings that derive from the body proper (i.e., tacit knowledge). The model of manipulative abduction emphasises thinking "through doing", in which external representations (mediators/signs) are sensed and experienced by the body (see Magnani, 2004, p. 229, 233-242;Magnani, 2001).…”

Section: Weak Form Of Inferencementioning

confidence: 99%

Experience in Design and Learning Approaches – Enhancing the Framework for Experience

Bauters¹

2017

Ro. J. Comm. & PR

View full text Add to dashboard Cite

In design and learning studies, an increasing amount of attention has been paid to experience. Many design approaches relate experience to embodiment and phenomenology. The growth in the number of applications that use the Internet of Things (IoT) has shifted human interactions from mobile devices and computers to tangible, material things. In education, the pressure to learn and update skills and knowledge, especially in work environments, has underlined the challenge of understanding how workers learn from reflection while working. These directions have been fuelled by research findings in the neurosciences, embodied cognition, the extended phenomenological-cognitive system and the role of emotions in decision-making and meaning making. The perspective on experience in different disciplines varies, and the aim is often to categorise experience. These approaches provide a worthwhile view of the importance of experience in learning and design, such as the recent emphasis on conceptual and epistemological knowledge creation. In pragmatism, experience plays a considerable role in research, art, communication and reflection. Therefore, I rely on Peirce's communicative theory of signs and Dewey's philosophy of experience to examine how experience is connected to reflection and therefore how it is necessarily tangible.

show abstract

“…If we see scientific discovery like a kind of opportunistic ability of integrating information from many kinds of simultaneous constraints to produce explanatory hypotheses that account for them all, then manipulative abduction will play the role of eliciting possible hidden constraints by building external suitable experimental structures. Magnani [9] Constructive research method implies building of an artifact (practical, theoretical or both) that solves a domain specific problem in order to create knowledge about how the problem can be solved (or understood, explained or modeled) in principle. Constructive research gives results which can have both practical and theoretical relevance.…”

Section: Characteristics Of Constructive Researchmentioning

confidence: 99%

Constructive Research and Info-computational Knowledge Generation

Dodig-Crnković

2010

Model-Based Reasoning in Science and Technology

View full text Add to dashboard Cite

Abstract.It is usual when writing on research methodology in dissertations and thesis work within Software Engineering to refer to Empirical Methods, Grounded Theory and Action Research. Analysis of Constructive Research Methods which are fundamental for all knowledge production and especially for concept formation, modeling and the use of artifacts is seldom given, so the relevant first-hand knowledge is missing. This article argues for introducing of the analysis of Constructive Research Methods, as crucial for understanding of research process and knowledge production. The paper provides characterization of the Constructive Research Method and its relations to Action Research and Grounded Theory. Illustrative example of Blue Brain Project is presented. Finally, foundations of Constructive Research are analyzed within the framework of Info-Computationalism which provides models of knowledge construction by information processing in a cognizing agent. Understanding of Research Methodology in Computing on the Background of Philosophy of Engineering and Philosophy of ScienceInformation age is fundamentally dependent on engineering whose knowledge largely rests on the research within Computing / Informatics. Demonstrably, artefacts built on research results of Computing work remarkably well and in that sense the research within Computing yields a reliable and trustworthy knowledge about the behavior of computational (engineered) systems. One of the relevant methodological and epistemological questions is what sort of knowledge is it and how it relates to our understanding of the world, including our understanding of humans. It is of interest to know what aspects of Computing could be labeled "Science" in a rigorous classical sense, and how knowledge produced within Computing enriches both our understanding of the world and our potential for further learning about the world and acting in the world, (see [1] and [2]).As we are building Knowledge Society, our understanding of knowledge production, both on the level of Cognitive Science and on the level of technological enhancements provided by ICT is becoming strategically important. In this context a very general view of Computing as Science and Computing as Engineering is needed wherefrom also necessity follows of understanding of Computing in both Philosophy of Science and Philosophy of Engineering contexts. In short, engineers have discovered the innovative potential of broad insights into the discipline, which gives tangible comparative advantage. Knowing about underlying mechanisms and structures for knowledge production helps us look out of the box and find novel approaches, which is especially important in constructive and design research.

show abstract

Model-Based and Manipulative Abduction in Science

Cited by 58 publications

References 26 publications

What is behind a summary-evaluation decision?

What is behind a summary-evaluation decision?

Experience in Design and Learning Approaches – Enhancing the Framework for Experience

Constructive Research and Info-computational Knowledge Generation

Contact Info

Product

Resources

About