This textual analysis examines computer simulations as rhetorical objects and acts. In particular, this work examines scientific simulations from organic chemistry and astrophysics in order to expose how rhetorical and social aspects influence the ad hoc decisions (e.g., setting initial parameters, excluding and adding arbitrary elements, and making other choices) that comprise simulations. Prior works in philosophy, critical theory and technical communication underscore fictional and formal features of simulation. In contrast, this dissertation dissects multiple levels of documents surrounding actual simulations—not only drafts of published articles but also software and code interiors, e-mail and letter correspondence, newsletters and white paper reports—in order to discuss the relational (rather than purely formal) meaning of the simulations. This work also compares simulation to other modes of the scientific imagination—paradox, thought experiments and metaphor, in particular. My findings suggest that simulations hinge upon abductive (rather than deductive or inductive) reasoning and qualify as virtual evidence. Also, while published drafts of simulation articles tidy the ad hoc twists and turns necessary for creating simulations, prior drafts and peripheral documents attest to the fact that organizational affiliations, earlier projects, and rhetorical strategies help establish the scope and meaning of simulation projects. Further, meaning-making takes place well before and long after the article drafting process—in prior incarnations of the work for presentation, in correspondence between article writers and reviewers, and in citations in others’ writing. / text

Made available in DSpace on 2014-06-11T19:25:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-10-23Bitstream added on 2014-06-13T20:53:23Z : No. of bitstreams: 1 cocchieri_t_me_mar.pdf: 416905 bytes, checksum: 14bb78803d1bdc1da924acbe9d5ce33b (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O objetivo desta Dissertação é realizar uma pesquisa sobre a natureza da criatividade como processo, com possibilidade de ser explicado de modo sistematizado. Buscamos refutar uma hipótese contrária que compartimenta o processo de criação em uma aura subjetivada e inefável. Com intuito de fundamentar nossa argumentação, procuramos reconstruir os argumentos desenvolvidos por C. S. Peirce referentes a um tipo específico de raciocínio lógico que está associado ao conceito de criatividade, por ser de natureza gerativa de idéias novas chamado pelo filósofo de raciocínio abdutivo. Investigamos aspectos da filosofia de Peirce que estruturam e permeiam a análise desta inferência lógica. Ao longo do desenvolvimento de nossas argumentações, apresentamos o pensamento de filósofos contemporâneos que se debruçaram à análise deste tema. / This dissertation aims at realizing a research on the nature of creativity understood as a process, with the possibility of explaining it in a systematic manner. We refute the hypothesis which ascribes to creative process a subjective and ineffable aura. In order to settle our argumentation we reconstruct that hypotheses of C. S. Peirce referring to a specific sort of logical reasoning associated with the concept of creativity, called abductive reasoning. As we also consider aspects of Peirce's philosophy which organize and integrate the analysis of such logical inference. As our argumentation is developed, we present the theses of contemporary philosophers that have worked on the analysis of this subject.

This dissertation explores how urban leaders and stakeholders can leverage urban innovation to address complex challenges and the uncertainties come with them at the local level, specifically for marginalized communities. Through a series of three standalone articles, including a pilot study on tactical urbanism and two systematic reviews on urban innovation governance and just city implications, the research employs an abductive approach to reconceptualize urban innovation as a platform for collective action and self-governance. The pilot study examines tactical urbanism as a promising trend for addressing uncertainties at the hyper-local level during the COVID-19 pandemic, using a qualitative analysis of academic and grey literature, as well as case studies of tactical urbanism interventions. Building upon these findings, the first systematic review delves into the concept of "urban innovation governance," proposing a participatory, community-based governance conceptualization. This review employs a mixed method meta-synthesis research strategy and an umbrella review methodology to assess the available evidence on urban innovation governance from a multidisciplinary perspective. Through triangulating my theoretical lens, the second review explores urban innovation as a platform for active and inclusive citizenship, utilizing a scoping review methodology to synthesize the practical implications of just city research, and identifying strategies for promoting equitable and inclusive urban transformations. By synthesizing insights from these studies, this dissertation challenges technocratic and top-down perspectives, arguing that community-driven urban innovation is key to locally attuned, inclusive action. The findings contribute to debates on public governance, community development, and innovation, offering evidence-based principles to guide localized innovation governance regimes tailored to unique urban contexts. This research highlights the transformative potential of urban innovation when approached through a self-governing, community-level lens.

Resumo: O objetivo desta Dissertação é realizar uma pesquisa sobre a natureza da criatividade como processo, com possibilidade de ser explicado de modo sistematizado. Buscamos refutar uma hipótese contrária que compartimenta o processo de criação em uma aura subjetivada e inefável. Com intuito de fundamentar nossa argumentação, procuramos reconstruir os argumentos desenvolvidos por C. S. Peirce referentes a um tipo específico de raciocínio lógico que está associado ao conceito de criatividade, por ser de natureza gerativa de idéias novas chamado pelo filósofo de raciocínio abdutivo. Investigamos aspectos da filosofia de Peirce que estruturam e permeiam a análise desta inferência lógica. Ao longo do desenvolvimento de nossas argumentações, apresentamos o pensamento de filósofos contemporâneos que se debruçaram à análise deste tema. / Abstract: This dissertation aims at realizing a research on the nature of creativity understood as a process, with the possibility of explaining it in a systematic manner. We refute the hypothesis which ascribes to creative process a subjective and ineffable aura. In order to settle our argumentation we reconstruct that hypotheses of C. S. Peirce referring to a specific sort of logical reasoning associated with the concept of creativity, called abductive reasoning. As we also consider aspects of Peirce's philosophy which organize and integrate the analysis of such logical inference. As our argumentation is developed, we present the theses of contemporary philosophers that have worked on the analysis of this subject. / Orientador: Maria Eunice Quílici Gonzales / Coorientador: Lauro Frederico Barbosa da Silveira / Banca: Mariana Claudia Broens / Banca: Ivo Assad Ibri / Mestre

While there are efforts to establish a single international accounting standard, there are strong current and future needs to handle heterogeneous accounting methods and systems. We advocate a context-based approach to dealing with multiple accounting standards and equational ontological conflicts. In this paper we first define what we mean by equational ontological conflicts and then describe a new approach, using Constraint Logic Programming and abductive reasoning, to reconcile such conflicts among disparate information systems. In particular, we focus on the use of Constraint Handling Rules as a simultaneous symbolic equation solver, which is a powerful way to combine, invert and simplify multiple conversion functions that translate between different contexts. Finally, we demonstrate a sample application using our prototype implementation that demonstrates the viability of our approach. / Singapore-MIT Alliance (SMA)

There is much debate on the best way to educate students on how to generate hypotheses to enhance clinical reasoning in nursing education. To increase opportunities for nursing programs to promote the discovery of accurate and broad-level hypotheses, scholars recommend abductive reasoning which offers an alternative approach to hypothetico-deductive reasoning. This study explored the effects of abductive reasoning training on hypothesis generation abilities (accuracy, expertise, breadth) of first and second year baccalaureate nursing students in a problem-based learning curriculum. A quasi-experiment with 64 participants (29 control, 35 experimental) was conducted. Based on their allocation, study participants either took part in abductive reasoning training or informal group discussion. Three different test questionnaires, each with a unique care scenario, were used to assess participants’ hypothesis generation abilities at baseline, immediate post-test and one-week follow-up. Content validity for care scenarios and other study materials was obtained from content academic experts. Compared to control participants, experimental participants showed significant improvements at follow-up on hypothesis accuracy (p=0.05), expertise (p=0.006), and breadth (p=0.003). While control participants’ hypotheses displayed a superficial understanding of care situations, experimental participants’ hypotheses reflected increased accuracy, expertise and breadth. This study shows that abductive reasoning, as a scaffolding teaching and learning strategy, can allow nursing students to discover underlying salient patterns in order to better understand and explain the complex realities of care situations. Educating nursing students in abductive reasoning could enable them to adapt existing competencies when trying to accurately and holistically understand newer complex care situations. This could lead to a more holistic, person-based approach to care which will allow nursing students to see various health-related issues as integrated rather than separate. / Thesis / Doctor of Philosophy (PhD) / This study explored the effects of a training program on hypothesis generation abilities of nursing students. The training program aimed to teach students how to think more broadly about care situations. Student’s hypothesis generation abilities were measured through the use of three care scenarios, each of which was presented before, immediately after and one-week after the training program. Only first and second year nursing students were included in the study. About half of the students were provided with the training while the other half were provided with informal discussion about hypothesis generation. After one-week, it was discovered that students who received the training had improved significantly in their ability to generate broad hypotheses. These students also generated hypotheses that were more accurate than the other group of students who did not receive the training. Due to the training, students’ abilities in discovering the important aspects of the care situation also improved.

The aim of this study is to investigate how self-perceptions of expertise among sports coaches may develop, regress, and redevelop over time within the context of coaching, in light of recent reconceptualisations of expertise, expertise development, sports coaching, coach development, and adult learning. The developmental journeys of four expert-like sports coaches are explored using a life history/life course approach. Written life history accounts are gathered, and repeated semi-structured interviews undertaken (six per participant over two years), focussing upon critical incidents related to coach development and perceptions of expertise, to capture interpretations and feelings. Narrative inquiry is employed to investigate and represent participants' lived experiences, and how they create meaning and identity from them. Co-constructed storied accounts of expert-like coaches' developmental journeys are produced featuring local exemplary knowledge. Looking across the stories and their respective interconnections, to speculate on wider theoretical implications is a further aspect of the study. Theoretical standpoints from a new wave of literature across different subject domains, and a Bourdieusian perspective, are used as guiding interpretive frameworks. This study reveals a more nuanced and complex holistic portrayal of perceived expertise development in contrast to oversimplified conceptions that currently dominate in this field of inquiry. This uniquely longitudinal in-depth exploration of the lived developmental journey of expert-like coaches provides illuminating detail on the process, influences, and continuation of expertise development (that may inform the facilitation and flourishing of other practitioners); uncovering a more intricate conceptualisation of expertise development, encompassing the importance of change and adaptation upon ongoing and recursive (re)development.

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Abductive reasoning is the process of finding a best explanation for a set of observations. In many abductive problems, like medical diagnosis, scientific discovery, debugging or troubleshooting, an amount of information far beyond the capacity limits of working memory (WM) must be processed. Although WM plays a central role in theories of human cognition, theories of abductive reasoning do not specify WM processes during the generation of explanations. On the basis of a computational model of abductive reasoning and of theories of text comprehension a mechanism is proposed that reduces WM load during abductive reasoning. The computational model views abductive reasoning as the sequential comprehension and integration of observations into a situation model that represents the current best explanation for the observations. The proposed WM mechanism assumes that the situation model is only partly kept in WM, whereas other pieces are stored in long-term memory. These long-term representation part can be reliably accessed through retrieval structures to reinstatiate information in WM during abductive reasoning. It is assumed that unexplained observations are actively maintained in WM until an explanation for them could be generated. Thereafter their representation is lost from WM. But these explained observations can be recalled from long-term memory via their integration into the situation model. This mechanism makes predictions about the availability of the mental representation of explained and unexplained observations. These predictions were tested in four experiments, using different memory tests for observations. In Experiments 1 and 2 a recognition test was used, in Experiment 3 an implicit menory test was used and in Experiment 4 the participants had to perform an unexpected recall after task interruption. The results show that unexplained observations are accessed faster than explained ones during abductive reasoning. This confirms the mechanism's assumption that unexplained observations are kept in WM and explained ones not. But explained observations seem not to be represented in long-term memory. Rather, it seems that observations are rapidly forgotten afer they are explained. Different possible reasons for this pattern of result are discussed.

Several real world tasks involve data that is uncertain and relational in nature. Traditional approaches like first-order logic and probabilistic models either deal with structured data or uncertainty, but not both. To address these limitations, statistical relational learning (SRL), a new area in machine learning integrating both first-order logic and probabilistic graphical models, has emerged in the recent past. The advantage of SRL models is that they can handle both uncertainty and structured/relational data. As a result, they are widely used in domains like social network analysis, biological data analysis, and natural language processing. Bayesian Logic Programs (BLPs), which integrate both first-order logic and Bayesian net- works are a powerful SRL formalism developed in the recent past. In this dissertation, we develop approaches using BLPs to solve two real world tasks – plan recognition and machine reading. Plan recognition is the task of predicting an agent’s top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. In the first part of the dissertation, we develop an approach to abductive plan recognition using BLPs. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for abductive plan recognition as is. Therefore, we extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). In the second part of the dissertation, we apply BLPs to the task of machine reading, which involves automatic extraction of knowledge from natural language text. Most information extraction (IE) systems identify facts that are explicitly stated in text. However, much of the information conveyed in text must be inferred from what is explicitly stated since easily inferable facts are rarely mentioned. Human readers naturally use common sense knowledge and “read between the lines” to infer such implicit information from the explicitly stated facts. Since IE systems do not have access to common sense knowledge, they cannot perform deeper reasoning to infer implicitly stated facts. Here, we first develop an approach using BLPs to infer implicitly stated facts from natural language text. It involves learning uncertain common sense knowledge in the form of probabilistic first-order rules by mining a large corpus of automatically extracted facts using an existing rule learner. These rules are then used to derive additional facts from extracted information using BLP inference. We then develop an online rule learner that handles the concise, incomplete nature of natural-language text and learns first-order rules from noisy IE extractions. Finally, we develop a novel approach to calculate the weights of the rules using a curated lexical ontology like WordNet. Both tasks described above involve inference and learning from partially observed or incomplete data. In plan recognition, the underlying cause or the top-level plan that resulted in the observed actions is not known or observed. Further, only a subset of the executed actions can be observed by the plan recognition system resulting in partially observed data. Similarly, in machine reading, since some information is implicitly stated, they are rarely observed in the data. In this dissertation, we demonstrate the efficacy of BLPs for inference and learning from incomplete data. Experimental comparison on various benchmark data sets on both tasks demonstrate the superior performance of BLPs over state-of-the-art methods. / text