In this thesis we are concerned with developing formal and representational mechanisms for reasoning with inconsistent information. Strictly speaking there are two conceptually distinct senses in which we are interested in reasoning with inconsistent information. In one sense, we are interested in using logical deduction to draw inferences in a symbolic system. More specifically, we are interested in mechanisms that can continue to perform deduction in a reasonable manner despite the threat of inconsistencies as a direct result of errors or misrepresentations. So in this sense we are interested in inconsistency-tolerant or paraconsistent deduction. ¶ However, not every case of inconsistent description is a case of misrepresentation. In many practical situations, logically inconsistent descriptions may be deployed as representations for problems that are inherently conflicting. The issue of error or misrepresentation is irrelevant in these cases. Rather the main concern in these cases is to provide meaningful analyses of the underlying structure and properties of our logical representation which in turn informs us about the salient features of the problem under consideration. So in this second sense, we are interested in deploying logic as a representation to model situations involving conflict. ¶ In this thesis we adopt a novel framework to unify both logic-as-deduction and logic-as-representation approaches to reasoning with inconsistent information. From a preservational view point, we take deduction as a process by which metalogical properties are preserved from premises to conclusions. Thus methodologically we may begin by identifying inconsistency-tolerant deduction mechanisms and then investigate what additional properties of inconsistent premises are preserved by these mechanisms; or alternatively we may begin by identifying properties of inconsistent logical descriptions and investigate which deductive mechanisms can preserve these properties. We view these as two aspects of the same investigation. A key assumption in this work is that adequate analyses of inconsistencies require provisions to quantitatively measure and compare inconsistent logical representations. While paraconsistent logics have enjoyed considerable success in recent years, proper quantitative analysis of inconsistencies seems to have lapsed behind to some extent. In this thesis we’ll explore different ways in which we can compare and measure inconsistencies. We hope to show that both inference and analysis can fruitfully be brought to bear on the issue of inconsistency handling under the same methodological scheme.

Upon interviewing a sample of youths, researchers who study exposure to violence are often faced with a substantial number of reports of exposure to violence and no way to confirm the veracity of these claims. The remarkably high levels of violence exposure reported by young preschool children (Richters & Martinez, 1990), paired with the low concordance between parents and children concerning what violence the child had witnessed and experienced (Howard, Cross, Li & Huang, 1999; Shahinfar, Fox & Leavitt, 2000), suggests that child self-reports of exposure to violence may not be entirely veridical. Since self-reports appear to be the only feasible method of measuring lifelong exposure to violence, determining the possible causes of false reports and investigating possible predictors should be a significant aspect of studying youth exposure to violence. With this thesis, I have investigated the veracity of child self-reports by comparing responses to a life-long exposure to violence questionnaire administered first between 1997 and 1999, and a second time two years later. Inconsistent reports, wherein an initial report of exposure to a particular violent instance was not confirmed by re-reporting at the second interview, were prevalent on a variety of violence-assessment items, including witnessing a murder and being the victim of a physical attack. For instance, when initially interviewed, approximately one in seven Chicago children aged 8 to 17 reported witnessing a shooting at least once during their lives. Alarmingly, 50% of these children did not confirm this instance of violence when interviewed again two years later. In an effort to identify self- or parent-reported characteristics and behaviours predictive of inconsistent responses concerning witnessing a shooting, I conducted several series of Binomial and Multinomial Logistic Regression analyses. Explanatory variables were selected to be representative of two main likely reasons for inconsistent self-reports: misremembering due to forgetting over time and the inaccuracy of children's memories, and misinformation due to an impulsive propensity towards lying or exaggeration. My findings suggest that, in comparison to individuals who confirmed their initial reports of having 'witnessed a shooting', individuals who retracted their initial claims were generally younger, from a higher socio-economic status level neighbourhood, admittedly lacked guilt after misbehaving, desired a lot of attention, and reported less impulsivity as measured by delinquency and behavioural impulsivity scales. / Thesis / Master of Science (MSc)

Studien behandlar elevers erfarenheter kring inkonsekventa textuppgifter i matematik. Syftet är att bilda kunskap om elevers syn på tillvägagångssätt vid lösandet av och svårig­heter med uppgifterna. Studiens teori utgår från fenomenologin då den stämmer över­ens med studiens syfte. Datamaterialet i stu­dien utgår från tio semistrukturerade inter­vjuer med elever i årskurs 6. Eleverna har under intervjuerna fått delge tillväga­gångs­sätt och vad de erfar var svårt och lätt med olika textuppgifter. Resultatet visar att problemmodellering var den vanligast förekommande strategin bland eleverna, men även direktöversättning förekom. Eleverna menade att svårigheten med textuppgifter var att beräkna och förstå vad de skulle göra. Eleverna hade olika preferenser av den omformulerade uppgiften men alla elever förutom en ansåg att standardversionen eller versionen där sambanden var tydligare var enklast att förstå. Slutsatser som kunde dras var att eleverna tenderade att koppla samman signalord med ett visst räknesätt, oavsett vilken strategi de använde sig av. Eleverna var även mer medvetna om problematiken kring beräkningen än de språkliga faktorerna. Deras olika preferenser av formuleringen av en uppgift kunde bero på deras tidigare erfarenheter av textuppgifter. / This study discusses pupils’ experiences of inconsistent word problems within mathe­matics. The aim is to form knowledge about pupils’ views of strategies and difficulties when solving inconsistent word problems. The theory in the study is based on phenomenology as it corresponds to the aim of the study. The material consisted of ten semi-structured interviews with pupils in grade 6. In the interviews the pupils described how they solved word problems and which difficulties they experienced with word problems. The result displayed the problem model approach as most common among the pupils, but direct translation also occurred. Complex aspects which the pupils mentioned were to calculate and perceive which rule of arithmetic to use. The pupils preferred different forms of rewording, but all except one pupil chose the standard version or the conceptual rewording. Conclusions drawn from the study was that pupils tended to associate relation terms with a rule of arithmetic, regardless of which strategy they used. The pupils were more aware of complications of the computing than of linguistic aspects. The difference in preferences of rewording could be due to differences in experiences of word problems.

Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle inconsistency of decision tables. We also analyze the time complexity of decision and inhibitory trees over arbitrary sets of attributes represented by information systems in the frameworks of local (when we can use in trees only attributes from problem description) and global (when we can use in trees arbitrary attributes from the information system) approaches.

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This report investigates inconsistencies in climate policy engagement of 16 major corporations in five industrial sectors and analyses which problem areas are significant in the inconsistency of their engagement. The purpose of this report was to scope the area of inconsistent climate policy engagement on behalf of the company GES International – Global Engagement Services, who offers advisory services in responsible investment. This report has been performed through a literature review and is partly based on a report on corporate carbon policy footprint by the independent organisation InfluenceMap.   All companies investigated in this report are to some extent supporting the climate agenda of the Paris Agreement, including limiting global warming to below two degrees. Nevertheless, 14 of the 16 companies are at the same time showing a negative climate policy engagement. The problem areas leading up to this inconsistency that have been detected in this report are; when companies are taking part in organisational relationships that are unsupportive of climate change mitigation strategies, when companies are against climate legislation, the unwillingness of companies to change their own industrial sector and companies showing mixed signals on the same policy topic.   The conclusions that can be drawn from this report is that there are substantial inconsistencies in the climate policy engagement of companies and it can be difficult to assess what the company’s true stance on climate engagement is. Some measures to improve on consistency is for companies to review their policy engagement and be transparent on what their actual stance climate engagement is, however this transition can take time. / Den här rapporten undersöker inkonsekvenser inom klimatpolicy engagemang hos 16 av världens största företag, inom fem olika sektorer, och analyser vilka problemområden som är betydande inom motsägande klimatengagemang hos företagen. Syftet med rapporten är att granska området kring inkonsekvent klimatpolicy engagemang på uppdrag av företaget GES International – Global Engagement Services, som erbjuder rådgivning inom hållbara investeringar. Rapporten har utförts genom en litteraturstudie som delvis är baserad på en rapport om företags påverkan på klimatet genom policys som är utgiven av den oberoende organisationen InfluenceMap.   Samtliga företag som undersöks i den här rapporten visar sig till viss del stödja klimatagendan i enlighet med Parisavtalet, vilket inkluderar en höjd global temperatur på max två grader. Trots detta visar sig 14 av de 16 företagen ha en distinkt negativ klimatpolicy engagemang som strider mot deras erkännande av Parisavtalet. De identifierade problem i den här rapporten som leder till motsägande klimatengagemang hos företagen är; när företagens organisatoriska relationer som arbetar strategiskt mot att förbättra klimatförändringar, när företagen strategiskt arbetar för att motverka lagstiftande om klimat, när företag är emot att inkludera sin egen sektor i klimatarbetet och när företagen visar olika åsikter inom samma ämne.   Slutsatsen som kan dras från den här rapporten är att det finns substantiella motsägelser i företagens klimatpolicy engagemang och det kan vara svårt att avgöra vilken som är den sanna inställningen hos företagen. För att för att göra företagens klimatengagemang mer konsekvent kan företagen till exempel granska sina egna engagemang och vara transparenta med vad deras egentliga inställning till klimatet är. Omställning till ett mer konsekvent klimatengagemang hos företagen kan ta lång tid.

no / When developing multivariate data classification and clustering methodologies for data mining, it is clear that most literature contributions only really consider data that contain consistently the same attributes. There are however many cases in current big data analytics applications where for same topic and even same source data sets there are differing attributes being measured, for a multitude of reasons (whether the specific design of an experiment or poor data quality and consistency). We define this class of data a dimensionally inconsistent multivariate data, a topic that can be considered a subclass of the Big Data Variety research. This paper explores some classification methodologies commonly used in multivariate classification and clustering tasks and considers how these traditional methodologies could be adapted to compare dimensionally inconsistent data sets. The study focuses on adapting two similarity measures: Robinson-Foulds tree distance metrics and Variation of Information; for comparing clustering of hierarchical cluster algorithms (such clusters are derived from the raw multivariate data). The results from experiments on engineering data highlight that adapting pairwise measures to exclude non-common attributes from the traditional distance metrics may not be the best method of classification. We suggest that more specialised metrics of similarity are required to address challenges presented by dimensionally inconsistent multivariate data, with specific applications for big engineering data analytics. / Jaguar Land-Rover

In the automotive sector, engine testing generates vast data volumes that are mainly beneficial to requesting engineers. However, these tests are often not revisited for further analysis due to inconsistent data quality and a lack of structured assessment methods. Moreover, the absence of a tailored knowledge discovery process hinders effective preprocessing, transformation, analytics, and visualization of data, restricting the potential for historical data insights. Another challenge arises from the heterogeneous nature of test structures, resulting in varying measurements, data types, and contextual requirements across different engine test datasets. This thesis aims to overcome these obstacles by introducing a specialized knowledge discovery approach for the distinctive Multiple Inconsistent Heterogeneous Data (MIHData) format characteristic of engine testing. The proposed methods include adapting data quality assessment and reporting, classifying engine types through compositional features, employing modified dendrogram similarity measures for classification, performing customized feature extraction, transformation, and structuring, generating and manipulating synthetic images to enhance data visualization, and applying adapted list-based indexing for multivariate engine test summary data searches. The thesis demonstrates how these techniques enable exploratory analysis, visualization, and classification, presenting a practical framework to extract meaningful insights from historical data within the engineering domain. The ultimate objective is to facilitate the reuse of past data resources, contributing to informed decision-making processes and enhancing comprehension within the automotive industry. Through its focus on data quality, heterogeneity, and knowledge discovery, this research establishes a foundation for optimized utilization of historical Engine Test Data (ETD) for improved insights. / Soroptimist International Bradford

Cognitive dissonance theory and selective information exposure literature postulate that individuals ignore preference-inconsistent information and selectively process new information. Previous studies on selective information exposure have shown that preference-inconsistent information is not persuasive for consumer decision making. Given the limited amount of past research about the effect of preference-inconsistent information on decision-making in broad domains of consumer behavior studies, the current study investigated how preference-inconsistent information can persuade consumers to switch to a sustainable product alternative. The purpose of this study is to investigate the process how preference-inconsistent sustainability-related information can be considered as important, consequently changing consumers' initial preference to green alternatives. A series of online experiments was conducted using a shampoo product category. Study 1 tested a baseline effect on whether consumers in the preference-inconsistent condition were persuaded to change their initial choice significantly compared to those in the preference-consistent condition. Study 2 tested the effect of preference-inconsistent sustainability-related information in the acceptance process, focusing on the role of brand commitment and information quality. Study 3 examined the effect of preference-inconsistent sustainability-related information in the evaluation process, investigating the impact of consumer environmental concern and PCE. Findings of Study 1 revealed that consumers in the preference-inconsistent condition were significantly persuaded to change choice to a sustainable alternative, which is not consistent with selective exposure literature. However, Study 1 findings were not sufficient to determine what specific factors influenced respondents to be persuaded, which provides justifications for Study 2 and Study 3. Findings in Study 2 and Study 3 conclusively demonstrated the importance of the credibility of preference-inconsistent information in the acceptance process. Also, findings suggested that the effect of credibility is stronger than that of brand commitment in the acceptance process. Regarding brand commitment, the results have shown that high commitment consumers had a higher acceptance of inconsistent information which is opposite to expectations. Further, the findings demonstrated the importance of environmental concern and the conditional effect of PCE in the evaluation process. Moreover, results supported that the relative weighting of sustainability attributes is driving the effects of environmental concern and PCE as a mediator on persuasion outcomes in the evaluation process. The current study contributes to understanding the process in which the preference-inconsistent information can be effective in influencing consumer choice. Moreover, findings from this research can provide implications for selective exposure literature and sustainable consumption literature. Practically, the results of the study provide implications to guide marketers and information providers in establishing effective ways to change consumers' behavior in sustainable consumption context.