The Nature and Development of Reasoning Strategies

Author(s):  
Ruth M. J. Byrne ◽  
Simon J. Handley
Keyword(s):  
Author(s):  
J.D. Trout

In early epistemology, philosophers set standards on how to reason and on what counts as knowledge. These normative standards still form a core of work in contemporary epistemology, but much objectively excellent reasoning still doesn’t meet these epistemological standards, and sometimes these standards lead reasoning astray. Improving decisions about health and happiness may require developing even better reasoning strategies than are now available through contemporary epistemology. One naturalistic theory of good reasoning—Strategic Reliabilism—holds that excellent reasoning efficiently allocates cognitive resources to robustly reliable reasoning strategies, all applied to significant problems. This contrasts with the traditional normative theories in epistemology that drew their inspiration from intuitions.


Author(s):  
Nicola A. Kiernan ◽  
Andrew Manches ◽  
Michael K. Seery

Visuospatial thinking is considered crucial for understanding of three-dimensional spatial concepts in STEM disciplines. Despite their importance, little is known about the underlying cognitive processing required to spatially reason and the varied strategies students may employ to solve visuospatial problems. This study seeks to identify and describe how and when students use imagistic or analytical reasoning when making pen-on-paper predictions about molecular geometry and if particular reasoning strategies are linked to greater accuracy of responses. Student reasoning was evidenced through pen-on-paper responses generated by high attaining, high school students (N = 10) studying Valence Shell Electron Pair Repulsion (VSEPR) Theory in their final year of chemistry. Through analysis and coding of students’ open-ended paper-based responses to an introductory task, results revealed that students employed multiple reasoning strategies, including analytical heuristics and the spontaneous construction of external diagrammatic representations to predict molecular geometry. Importantly, it was observed that despite being instructed on the use of VSEPR theory to find analytical solutions, some students exhibited preference for alternative reasoning strategies drawing on prior knowledge and imagistic reasoning; showing greater accuracy with 3D diagrammatic representations than students who used the algorithmic method of instruction. This has implications for both research and practice as use of specific reasoning strategies are not readily promoted as a pedagogical approach nor are they given credit for in national examinations at school level.


Author(s):  
Pieter F. de Vries Robbé ◽  
Pieter E. Zanstra ◽  
Steven F. Hartkamp ◽  
Wim P. A. Beckers
Keyword(s):  

2019 ◽  
Vol 37 (5) ◽  
pp. 638-661 ◽  
Author(s):  
Abdul-Rasheed Amidu ◽  
David Boyd ◽  
Fernand Gobet

Purpose Behavioural studies of valuers have suggested that valuers rely on a number of cognitive strategies involving reasoning and intuition when undertaking a valuation task. However, there are few studies of the actual reasoning mechanisms in valuation. In other fields, much attention has been paid to forward and backward reasoning, as this shows the choices and decisions that are made in undertaking a complex task. This paper studied this during a valuation task. The purpose of this paper is twofold: first, to develop a methodological approach for empirical research on valuers’ reasoning, and, second, to report expert-novice differences on valuers’ use of forward and backward reasoning during a valuation problem solving. Design/methodology/approach The study utilised a verbal protocol analysis (VPA) to elicit think-aloud data from a purposive sample of a group of valuers of different levels of expertise undertaking a commercial-valuation task. Through a content analysis interpretive strategy, the transcripts were analysed into different cognitive segments identifying the forward and backward reasoning strategies. Findings The findings showed that valuers accomplished the valuation task by dividing the overall problem into sub-problems. These sub-problems are thereafter solved by integrating available data with existing knowledge by relying more on forward reasoning than backward reasoning. However, there were effects associated with the level of expertise in the way the processes of forward and backward reasoning are used, with the expert and intermediate valuers being more thorough and comprehensive in their reasoning process than the novices. Research limitations/implications This study explores the possibility that forward and backward reasoning play an important role in commercial valuation problem solving using a limited sample of valuers. Given this, data cannot be generalised to all valuation practice settings but may motivate future research that examines the effectiveness of forward and backward reasoning in diverse valuation practice settings and develops a holistic model of valuation reasoning. Practical implications The findings of this study are applicable to valuation practice. Future training efforts need to evaluate the usefulness of teaching problem solving and explicitly recognise forward and backward reasoning, along with other problem-solving strategies uncovered in this study, as standard training strategies for influencing the quality of valuation decisions. Originality/value By adopting VPA, this study employs an insightful and rich dataset which allows an interpretation of thoughts of valuers into cognitive reasoning strategies that provide a deeper level of understanding of how valuers solve valuation problem; this has not been possible in previous related valuation studies.


2011 ◽  
pp. 104-112 ◽  
Author(s):  
Mahesh S. Raisinghani ◽  
Christopher Klassen ◽  
Lawrence L. Schkade

Although there is no firm consensus on what constitutes an intelligent agent (or software agent), an intelligent agent, when a new task is delegated by the user, should determine precisely what its goal is, evaluate how the goal can be reached in an effective manner, and perform the necessary actions by learning from past experience and responding to unforeseen situations with its adaptive, self-starting, and temporal continuous reasoning strategies. It needs to be not only cooperative and mobile in order to perform its tasks by interacting with other agents but also reactive and autonomous to sense the status quo and act independently to make progress towards its goals (Baek et al., 1999; Wang, 1999). Software agents are goal-directed and possess abilities such as autonomy, collaborative behavior, and inferential capability. Intelligent agents can take different forms, but an intelligent agent can initiate and make decisions without human intervention and have the capability to infer appropriate high-level goals from user actions and requests and take actions to achieve these goals (Huang, 1999; Nardi et al., 1998; Wang, 1999). The intelligent software agent is a computational entity than can adapt to the environment, making it capable of interacting with other agents and transporting itself across different systems in a network.


1984 ◽  
Vol 23 (01) ◽  
pp. 9-14 ◽  
Author(s):  
R. A. Miller

SummaryINTERNIST-1 is an experimental computer program for consultation in general internal medicine. On a series of test cases, its performance has been shown to be similar to that of staff physicians at a university hospital. Despite INTERNIST-1’s apparent success in dealing with complex cases involving multiple diagnoses in the same patient, many shortcomings in both its knowledge representation schemes and its diagnostic algorithms still remain. Among the known problems are lack of anatomical and temporal reasoning, inadequate representation of degrees of severity of findings and illnesses, and failure to reason properly about causality. These drawbacks must be corrected before INTERNIST-1’s successor program, CADUCEUS, can be used. It is estimated that CADUCEUS will not be ready for release to the general medical community for five to ten years.Broader problems faced by all medical diagnostic consultant systems are: design of an efficient human interface; development and completion of medical knowledge bases; expansion of diagnostic algorithms from simple heuristic rules to include a range of complex reasoning strategies, and development of a method for validating computer programs for clinical use.


1992 ◽  
Vol 13 (2) ◽  
pp. 111-124 ◽  
Author(s):  
Ruth M.J. Byrne ◽  
Simon. J. Handley
Keyword(s):  

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