“So the big rocks we put in the jar”: using situated reasoning to coordinate design activity

Ways in which situated reasoning featured in the course of action in the setting of a pair-programming software design exercise is examined, and how interactionally design was accomplished as a coordinated activity in situ.

A holistic method to assess building energy efficiency combining D-S theory and the evidential reasoning approach

The assessment of building energy efficiency is one of the most effective measures for reducing building energy consumption. This paper proposes a holistic method (HMEEB) for assessing and certifying building energy efficiency based on the D-S (Dempster-Shafer) theory of evidence and the Evidential Reasoning (ER) approach. HMEEB has three main features: (i) it provides both a method to assess and certify building energy efficiency, and exists as an analytical tool to identify improvement opportunities; (ii) it combines a wealth of information on building energy efficiency assessment, including identification of indicators and a weighting mechanism; and (iii) it provides a method to identify and deal with inherent uncertainties within the assessment procedure. This paper demonstrates the robustness, flexibility and effectiveness of the proposed method, using two examples to assess the energy efficiency of two residential buildings, both located in the ‘Hot Summer and Cold Winter’ zone in China. The proposed certification method provides detailed recommendations for policymakers in the context of carbon emission reduction targets and promoting energy efficiency in the built environment. The method is transferable to other countries and regions, using an indicator weighting system to modify local climatic, economic and social factors.

Emotional reasoning and anxiety sensitivity: associations with social anxiety disorder in childhood

Background Two specific cognitive constructs that have been implicated in the development and maintenance of anxiety symptoms are anxiety sensitivity and emotional reasoning, both of which relate to the experience and meaning of physical symptoms of arousal or anxiety. The interpretation of physical symptoms has been particularly implicated in theories of social anxiety disorder, where internal physical symptoms are hypothesized to influence the individual's appraisals of the self as a social object. Method The current study compared 75 children on measures of anxiety sensitivity and emotional reasoning: 25 with social anxiety disorder, 25 with other anxiety disorders, and 25 nonanxious children (aged 7–12 years). Results Children with social anxiety disorder reported higher levels of anxiety sensitivity and were more likely than both other groups to view ambiguous situations as anxiety provoking, whether physical information was present or not. There were no group differences in the extent to which physical information altered children's interpretation of hypothetical scenarios. Limitations This study is the first to investigate emotional reasoning in clinically anxious children and therefore replication is needed. In addition, those in both anxious groups commonly had comorbid conditions and, consequently, specific conclusions about social anxiety disorder need to be treated with caution. Conclusion The findings highlight cognitive characteristics that may be particularly pertinent in the context of social anxiety disorder in childhood and which may be potential targets for treatment. Furthermore, the findings suggest that strategies to modify these particular cognitive constructs may not be necessary in treatments of some other childhood anxiety disorders.

National interest to global reform: patterns of reasoning in British foreign policy discourse

Discussion of the national interest often focuses on how Britain's influence can be maximized, rather than on the goals that influence serves. Yet what gives content to claims about the national interest is the means-ends reasoning which links interests to deeper goals. In ideal-typical terms, this can take two forms. The first, and more common, approach is conservative: it infers national interests and the goals they advance from existing policies and commitments. The second is reformist: it starts by specifying national goals and then asks how they are best advanced under particular conditions. New Labour's foreign policy discourse is notable for its explicit use of a reformist approach. Indeed, Gordon Brown's vision of a 'new global society' not only identifies global reform as a key means of fulfilling national goals, but also thereby extends the concept of the national interest well beyond a narrow concern with national security.

A semiotic approach to critical reasoning

Information can be interpreted as in-formation, which refers to the potential of the form for a mediation of meaning. In this paper we focus on reasoning information and consider the question how form involved in reasoning can be used for an analysis of accounting narratives in corporate disclosures. An evaluation of experimental results is included.

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.