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Creative Imagining
Published in Lisa Grocott, Design for Transformative Learning, 2022
Following neither a bottom up- nor top-down logic, the process of abductive reasoning can be generally understood as working from an incomplete set of observations to sense-make from the data at hand. The designer brings this orientation to the synthesising of different kinds of data in a distinctly creative and generative direction. Consider, for example, how a private investigator might work with gaps in information compared to a designer. Whereas the investigator seeks to disclose something unseen, the designer iteratively engages with information to see something anew. The practice of sensing and wondering in design is more than simply abductive; it is performative. It is grounded in a creative practice that embraces the ambiguity of not-knowing (3) and explores hunches through making a move (4). The relevance of design abduction in a world of multifaceted, interdependent problems lies in simultaneously proposing problems and solutions by co-creating the what and how of a situation (5).
From Observations to Insights: The Hilly Road to Value Creation
Published in Bo T. Christensen, Linden J. Ball, Kim Halskov, Analysing Design Thinking: Studies of Cross-Cultural Co-Creation, 2017
A key task of the analysis is finding instances of abductive reasoning and, in particular, the abductive hypothesis. Abductive reasoning occurs when individuals encounter a surprising observation or an intended value that is not currently satisfied by current knowledge. We follow rule-based guidelines for the identification of abductive reasoning in verbalizations (Dong et al., 2016a; Dong et al., 2015). Computational approaches based upon extracting adverbs of manner, referring to process and actions, can also be used to identify instances of abductive reasoning (Bedford et al., 2017). An example of abductive reasoning from the session on clustering insights from the first co-creation workshop is: (v09, 239) if you actually also buy these accessories you have become even: more, eh: responsibleThe rule is: p → q: IF buy these accessories THEN responsibleAlternatively, the rule could be stated as, “People who buy these accessories are more responsible.”
Distributed Data and Information Fusion in Visual Sensor Networks
Published in David L. Hall, Chee-Yee Chong, James Llinas, Martin Liggins, Distributed Data Fusion for Network-Centric Operations, 2013
Federico Castanedo, Juan Gomez-Romero, Miguel A. Patricio, Jesus Garcia, Jose M. Molina
Scene interpretation is a paradigmatic case of abductive reasoning, in contrast to the Description Logics classical deductive reasoning. Abductive reasoning takes a set of facts as input and finds a suitable hypothesis that explains them (sometimes with an associated degree of confidence or probability). This is the case of scene interpretation: the objective is to figure out what is happening in the scene from the observations and the contextual facts. In terms of the fusion agent architecture, scene interpretation is an abductive transformation from instances of a lower-level ontology (representing perceived or contextual entities) to instances of a higher-level ontology. Abductive reasoning is not directly supported by ontologies (Elsenbroich et al. 2006), since monotonicity of ontology languages forbids adding new knowledge to the models while reasoning. Nevertheless, it can be simulated by using customized procedures or preferably by defining transformation rules in a suitable query language. The RACER inference engine, presented in Section 17.2.3, allows abductive reasoning, and therefore it may be a good choice to implement the reasoning procedures within the ontologies.
Organizing logistics to achieve strategic fit in building contractors: a configurations approach
Published in Construction Management and Economics, 2022
Petter Haglund, Martin Rudberg, Ahmet Anil Sezer
The research process was based on iterations between data collection and conceptual framework development, following the logic of abductive reasoning. A key concern in abductive reasoning is to identify deviations in the empirical material from prior theoretical knowledge to suggest hypotheses/propositions or to interpret existing phenomena through a new conceptual framework (Kovács and Spens 2005). The abductive research process in this study enabled the researchers to make meaningful interpretations of the empirical data from the case studies, while the definitions and interpretations of the variables within the conceptual framework could be refined based on the case study findings. This process resulted in the LCPT (Figure 2), which was developed by combining the definitions of logistics context and organizing variables in the conceptual framework with the insights gained from the case studies.
The regenerative supply chain: a framework for developing circular economy indicators
Published in International Journal of Production Research, 2019
Mickey Howard, Peter Hopkinson, Joe Miemczyk
We adopt an abductive approach in our research i.e. neither inductive nor deductive, which starts with a problem or phenomenon based on an incomplete set of facts (Dubois and Gadde 2002; Kovács and Spens 2005). Abductive reasoning uses systematised creativity or intuition to develop ‘new’ knowledge and to break out of the limitations of induction and deduction (Pettigrew 1997) which both delimit the phenomenon under investigation (CE indicators), towards already established constructs such as sustainable supply chain management. The abductive research builds on a concept or idea using multiple sources of empirical and theoretical data as part of a data matching or ‘systematic combining’ process (Dubois and Gadde 2002). Figure 2 illustrates our process of matching, directing and redirecting the multiple sources of data between the empirical world (company data), theory (literature review), phenomena (CE indicators) and cases. Abductive reasoning is most commonly used in conjunction with case study development, enabling the examination of plausible conclusions and whether the phenomenon may be related or not to generalisable rules or types of situations, thus providing new insights into the particularities of specific situations, experiences and settings (Dubois and Gadde 2002; Kovács and Spens 2005).
A novel approach for analysing evolutional motivation of empirical engineering knowledge
Published in International Journal of Production Research, 2018
Xinyu Li, Zuhua Jiang, Lijun Liu, Bo Song
Since the conclusions of abductive reasoning are not always tenable, it’s necessary to examine the motivations by domain experts to ensure their reasonability. Thus, the costs in the evaluation are supposed to be discussed. Greater width and depth in the process of abductive reasoning (corresponding to the M events in a turn and the Δ backtracked time intervals) means better completeness and persuasiveness of evolution analysis results. However, considering the workload of experts in the worst case, when Relthreshold is 0, domain experts need long time and burdened effort to check all events obtained from abductive reasoning. The exponential growth brought by these parameters also occupies a huge proportion of RAM usage for the EEK-KEAS, which seriously impact the users’ experience. Therefore, parameters and thresholds in the abductive reasoning algorithm should be well tuned to reduce the manual workload and RAM usage. In this paper, we chose quite small M and Δ and a rather large Relthreshold. The connections among evolutional events were also constructed and saved before executing the abductive reasoning algorithm. Hence, the RAM usage for EEK-KEAS is little and the numbers of evolution events in checklist are acceptable. Besides, for a wider and deeper analysis case, more filtering rules could be added into the abductive reasoning algorithm as well, which can be investigated in the following research.