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Estudo da incerteza de medição em análises toxicológicas de substâncias psicoativas em urina; Study of the measurement uncertainty in toxicological analysis of psychoactive substances in urine

Eller, Sarah Carobini Werner de Souza
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 16/04/2014 Português
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Nenhuma medição é realizada com perfeição absoluta, uma vez que todos os valores encontrados são aproximações do valor real e todas as medidas, independente de sua finalidade ou qualidade, possuem uma incerteza. A incerteza de medição é um parâmetro associado ao resultado, que caracteriza a dispersão em torno dos seus valores. O conceito de incerteza de medição já é adotado em laboratórios de calibração e também muito aplicado na área de engenharia; no entanto em análises toxicológicas esta abordagem ainda é recente e há poucos relatos na literatura científica. Portanto, este trabalho teve como objetivo o estudo da incerteza de medição em análises toxicológicas confirmatórias de substâncias psicoativas - anfetaminas (anfetamina e metanfetamina), ácido 11-nor-Δ9-tetraidrocanabinol carboxílico (THC-COOH) e benzoilecgonina - em urina, detectados pela técnica de cromatografia em fase gasosa acoplada à espectrometria de massas (GC-MS). A microextração em fase líquida (LPME) mostrou-se eficaz na determinação de THC-COOH, e após a completa validação, o método desenvolvido foi aplicado na quantificação de amostras de urina de referência provenientes do National Institute of Standards and Technology (NIST) dos Estados Unidos da América (SRM1507b - NIST). As principais contribuições para a incerteza do método foram a concentração do analito...

Using fuzzy logic to characterize uncertainty during the design and use stages of performance measurement

Sousa, Sérgio; Nunes, Eusébio P.; Lopes, Isabel da Silva
Fonte: IAENG - International Association of Engineers Publicador: IAENG - International Association of Engineers
Tipo: Conferência ou Objeto de Conferência
Publicado em //2014 Português
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The process of performance measurement encompasses the activities required for data collection (use stage), which was previously designed (design stage) and contribute to decision-making after data analysis (analysis stage). The lack of quality of performance measures (PMs) may influence decision-making. Since the process of performance measurement involves generally several actors, the decisionmaker may not be aware of the level of uncertainty associated with performance measures. In this paper, fuzzy sets are used to represent the uncertainty generated in performance measures during its design, use and analysis stages. The uncertainty sources are arranged on three cause–and-effect diagrams representing controllable factors that can lead to imperfect design, use and analysis, impacting on PMs uncertainty. This degree of imperfection will be labelled deficiency (at a given stage) and a methodology is presented to infer its effect on the PM uncertainty. The identification of uncertainty sources and the determination of an Uncertainty Index support actions to improve performance measures’ quality. An application example is provided to show the usefulness of the proposed methodology.; Fundação para a Ciência e a Tecnologia (FCT)

Uncertainty assessment of performance indicators

Cavallare, Marcello; Sousa, Sérgio; Nunes, Eusébio P.
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Conferência ou Objeto de Conferência
Publicado em //2014 Português
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Purpose: This paper defines a model to evaluate the uncertainty in performance indicators (PIs) based on Uncertainty Components (UCs). Methodology: The proposed work consists, in a first stage, of an assessment of the level of influence that each UC has in a given PI. Based on the questionnaire responses a matrix of UCs vs PIs is presented to show the relevance of the contribution of each UC to the uncertainty associated with a PI. The second stage of the methodology consists on the development of a model to infer the uncertainty level on a PI based on the uncertainty level of the identified UCs. Findings: A questionnaire referring to the assessment of PIs was applied, and the results provide evidence that UCs influence the PI. A model was developed based on logical relations between the UCs and the overall PI uncertainty, and the number of empirical analyses contribute to validate it. Originality/value: This paper presents a model to infer the uncertainty level of a PI based on UCs. The model can also be applied to propagate uncertainty among multiple related PIs. UCs definitions can guide the development of actions to reduce uncertainty in PIs, thus reducing the risk in the decision making process.

Tolerance analysis approach based on the classification of uncertainty (aleatory / epistemic)

DANTAN, Jean-Yves; GAYTON, N.; QURESHI, Ahmed Jawad; LEMAIRE, Maurice; ETIENNE, Alain
Fonte: Elsevier Publicador: Elsevier
Português
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Uncertainty is ubiquitous in tolerance analysis problem. This paper deals with tolerance analysis formulation, more particularly, with the uncertainty which is necessary to take into account into the foundation of this formulation. It presents: a brief view of the uncertainty classification: Aleatory uncertainty comes from the inherent uncertain nature and phenomena, and epistemic uncertainty comes from the lack of knowledge, a formulation of the tolerance analysis problem based on this classification, its development: Aleatory uncertainty is modeled by probability distributions while epistemic uncertainty is modeled by intervals; Monte Carlo simulation is employed for probabilistic analysis while nonlinear optimization is used for interval analysis.; “AHTOLA” project (ANR-11- MONU-013)

Innocent Bystanders : How Foreign Uncertainty Shocks Harm Exporters

Taglioni, Daria; Zavacka, Veronika
Fonte: World Bank, Washington, DC Publicador: World Bank, Washington, DC
Português
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The failure of trade economists to anticipate the extreme drop in trade post Lehman Brothers bankruptcy suggests that the behavior of trade in exceptional circumstances may still be poorly understood. This paper explores whether uncertainty shocks have explanatory power for movements in trade. VAR estimations on United States data suggest that domestic uncertainty is a strong predictor of movements in imports, but has little effect on exports. Guided by these results, the paper estimates a bilateral model with focus on the impact of importer uncertainty on foreign suppliers. It finds that there is a strong negative relationship between uncertainty and trade and that this relationship is non-linear. Uncertainty matters most when its levels are exceptionally high. The paper does not find evidence of learning from past turmoils, suggesting that prior experience with major uncertainty shocks does not reduce the effect on trade. In line with the expectations, the negative effect of uncertainty shocks on trade is higher for trade relationships more intensive in durable goods. Surprisingly...

Uncertainty analysis methods for multi-criteria decision analysis.

Hyde, Kylie Marie
Fonte: Universidade de Adelaide Publicador: Universidade de Adelaide
Tipo: Tese de Doutorado
Publicado em //2006 Português
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Planning, design and operational decisions are made under complex circumstances of multiple objectives, conflicting interests and participation of multiple stakeholders. Selection of alternatives can be performed by means of traditional economics-based methods, such as benefit-cost analysis. Alternatively, analyses of decision problems, including water resource allocation problems, which involve trade-offs among multiple criteria, can be undertaken using multi-criteria decision analysis (MCDA). MCDA is used to assist decision makers (DMs) in prioritising or selecting one or more alternatives from a finite set of available alternatives with respect to multiple, usually conflicting, criteria. In the majority of decision problems, MCDA is complicated by input parameters that are uncertain and evaluation methods that involve different assumptions. Consequently, one of the main difficulties in applying MCDA and analysing the resultant ranking of the alternatives is the uncertainty in the input parameter values (i.e. criteria weights (CWs) and criteria performance values (PVs)). Analysing the sensitivity of decisions to various input parameter values is, therefore, an integral requirement of the decision analysis process. However, existing sensitivity analysis methods have numerous limitations when applied to MCDA...

Analytical measurement uncertainty of food carotenoid determination

Dias, M. Graça; Camões, M. Filomena; Oliveira, Luísa
Fonte: Instituto Nacional de Saúde Doutor Ricardo Jorge, IP Publicador: Instituto Nacional de Saúde Doutor Ricardo Jorge, IP
Tipo: Conferência ou Objeto de Conferência
Publicado em 06/06/2011 Português
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Resumo publicado em: EURACHEM/CITAC 2011 workshop: Recent Developments in Measurement Uncertainty, 6‐7 June 2011: book of abstrats, p. P - 25 [Disponível em: http://eurachem2011.fc.ul.pt/pdf/Eurachem_CITAC2011-BookofAbstracts.pdf]; INTRODUCTION. Carotenoid determination in food is a complex analytical process involving several mass transfer steps (extraction, evaporation, saponification, etc.). For consistent interpretation of an analytical method result it is necessary to evaluate the confidence that can be placed in it; this can be provided by the quantification of its accuracy (trueness and precision) in the form of a measurement uncertainty estimate. The Guide to the expression of Uncertainty in Measurement issued by the International Organization for Standardization1 establishes rules for evaluating and expressing uncertainty. Although it is a very powerful tool2, it is even more complex when analytical methods include mass transfer steps that lack descriptive models for the behaviour of the analyte in the analytical system. The guide was interpreted for analytical chemistry by EURACHEM, whose second edition3 already includes the possibility of using interlaboratory information and also the use of information obtained from analytical methods inhouse validation. MATERIAL AND METHODS. Analytical measurement uncertainty was estimated based on intralaboratory data...

Three essays on fair division and decision making under uncertainty

Xue, Jingyi
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Thesis; Text Formato: application/pdf
Português
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The first chapter is based on a paper with Jin Li in fair division. It was recently discovered that on the domain of Leontief preferences, Hurwicz (1972)'s classic impossibility result does not hold; that is, one can find efficient, strategy-proof and individually rational rules to divide resources among agents. Here we consider the problem of dividing l divisible goods among n agents with the generalized Leontief preferences. We propose and characterize the class of generalized egalitarian rules which satisfy efficiency, group strategy-proofness, anonymity, resource monotonicity, population monotonicity, envy-freeness and consistency. On the Leontief domain, our rules generalize the egalitarian-equivalent rules with reference bundles. We also extend our rules to agent-specific and endowment-specific egalitarian rules. The former is a larger class of rules satisfying all the previous properties except anonymity and envy-freeness. The latter is a class of efficient, group strategy-proof, anonymous and individually rational rules when the resources are assumed to be privately owned. The second and third chapters are based on two working papers of mine in decision making under uncertainty. In the second chapter, I study the wealth effect under uncertainty --- how the wealth level impacts a decision maker's degree of uncertainty aversion. I axiomatize a class of preferences displaying decreasing absolute uncertainty aversion...

Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6

Briggs, A.H.; Weinstein, M.C.; Fenwick, E.A.L.; Karnon, J.; Sculpher, M.J.; Paltiel, A.D.
Fonte: SAGE Publications Publicador: SAGE Publications
Tipo: Artigo de Revista Científica
Publicado em //2012 Português
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A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique...

Model parameter estimation and uncertainty: A report of the ISPOR-SMDM modeling good research practices task force-6

Briggs, A.; Weinstein, M.; Fenwick, E.; Karnon, J.; Sculpher, M.; Paltiel, A.
Fonte: Wiley-Blackwell Publishing, Inc. Publicador: Wiley-Blackwell Publishing, Inc.
Tipo: Artigo de Revista Científica
Publicado em //2012 Português
Relevância na Pesquisa
36.6129%
A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value of information analysis. The article also makes extensive recommendations around the reporting of uncertainty, in terms of both deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique...

Commodity Price Uncertainty in Developing Countries

Dehn, Jan
Fonte: World Bank, Washington, DC Publicador: World Bank, Washington, DC
Tipo: Publications & Research :: Policy Research Working Paper; Publications & Research
Português
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Uncertainty about commodity export prices is important to developing countries -- both governments and producers -- that export primary commodities. Commodity export price uncertainty is typically measured as the standard deviation in the terms of trade. There are three problems with this approach: 1) Terms of trade indices are unsuitable as proxies for commodity price movements per se. 2) The shortness of terms of trade time series makes them inappropriate as a base for constructing time-varying uncertainty measures. 3) Simple standard deviation measures ignore the distinction between predictable and unpredictable elements in the price process, so they risk overstating uncertainty. 4) The author examines commodity price uncertainty in developing countries using new data for quarterly aggregate commodity price indices for 113 developing countries for the period 1957-97. Each index is a geometrically weighted index of 57 commodity prices. He constructs six different measures of uncertainty. The uncertainty measures confirm the importance of distinguishing between predictable and unpredictable components in the price process. But there is a positive...

Speculative requirements: automatic detection of uncertainty in natural language requirement

Yang, Hui; De Roeck, Anne; Gervasi, Vincenzo; Willis, Alistair; Nuseibeh, Bashar
Fonte: IEEE Computer Society Publicador: IEEE Computer Society
Tipo: info:eu-repo/semantics/conferenceObject; all_ul_research; ul_published_reviewed
Português
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peer-reviewed; Stakeholders frequently use speculative language when they need to convey their requirements with some degree of uncertainty. Due to the intrinsic vagueness of speculative language, speculative requirements risk being misunderstood, and related uncertainty overlooked, and may benefit from careful treatment in the requirements engineering process. In this paper, we present a linguistically-oriented approach to automatic detection of uncertainty in natural language (NL) requirements. Our approach comprises two stages. First we identify speculative sentences by applying a machine learning algorithm called Conditional Random Fields (CRFs) to identify uncertainty cues. The algorithm exploits a rich set of lexical and syntactic features extracted from requirements sentences. Second, we try to determine the scope of uncertainty. We use a rule-based approach that draws on a set of hand-crafted linguistic heuristics to determine the uncertainty scope with the help of dependency structures present in the sentence parse tree. We report on a series of experiments we conducted to evaluate the performance and usefulness of our system.

Perceptions of Family Medicine Residents on the Role of Clinical Uncertainty in Learning to Become Competent Family Physicians

Rich, Jessica
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
Português
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As the first point of patient care, Family physicians are known to experience greater levels of uncertainty in decision-making. Little is known about how Family Medicine residents perceive and experience uncertainty in their dual roles as learners and healthcare providers. While there is research to suggest that uncertainty can be productive for learning medical practice, no study has examined the role of uncertainty in resident learning. The purpose of this study was to explore how Family Medicine residents view, experience, and manage uncertainty while learning through practice. A qualitative design was used to explore residents’ emotions, thoughts, opinions, and attitudes regarding their lived experiences of uncertainty. Over a two-week period, nine Family Medicine residents from one academic institution participated in individual, semi-structured interviews. Following verbatim transcription and member checking of interview summaries, the data was thematically analyzed to identify patterns in participants’ views and experiences of uncertainty. Results showed that uncertainty was a common and potentially uncomfortable experience for participating residents. Over time, the residents recalled their uncertainty subsiding and changing in character as they gained confidence and comfort with uncertainty in their decision-making. Despite viewing uncertainty as integral to lifelong professional learning...

The Computation and Visualization of Uncertainty in Surgical Navigation

Simpson, AMBER
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 38939302 bytes; application/pdf
Português
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The subject of this dissertation is the calculation and visualization of intraoperative measurement uncertainty in computer-assisted surgical procedures. Error is the difference between the observed or measured value and the true value (called ground-truth) of a quantity. Uncertainty is the unknown difference between the measured and true values, and exists in the absence of knowledge of ground truth. If one has an algorithm for computing the ground truth then one can get an accurate estimate of error. However, in computer-assisted surgery, the ground truth is often unknown. The introduction of error to surgical procedures is inevitable: it cannot be avoided by simply taking very careful measurements, providing more accurate algorithms, or by improving instrument calibration. One can only reduce errors as much as reasonably possible, calculate a reliable estimate of the uncertainty, and provide a meaningful way to convey this uncertainty information to clinicians. In this dissertation, I demonstrate that the visualization of registration uncertainty improves surgical navigation and that real-time computation of intraoperative measurement uncertainty is possible. In an extensive user study of surgeons and surgical residents, I compare methods of visualizing intraoperative uncertainty and determine that there are several methods of effectively conveying uncertainty in surgical navigation.; Thesis (Ph.D...

A New Robust Scenario Approach to Supply Chain Optimization under Bounded Uncertainty

Chowdhury, NIAZ
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
36.58849%
Supply Chain Optimization (SCO) problem under uncertainty can be modeled as two-stage optimization problem where first-stage decisions are associated with design and development of facilities and second-stage decisions are associated with operation of the supply chain network. Recently, a robust scenario approach combing the traditional scenario or robust approach has been developed to better address uncertainties in SCO problems, and it can ensure solution feasibility and better expected objective value. But this approach can only address uncertainties bounded with the infinity-norm. This thesis proposes a modified robust scenario approach, which can be used to address uncertainty region bounded with the p-norm in SCO. In this case, after the normalization of the uncertainty region, the smallest box uncertainty region, that covers the normalized uncertainty region, can be partitioned into a number of box uncertainty subregions. Following some screening criteria, two subsets of the subregions that over-estimates and under-estimates the original uncertainty region can be selected. When the number of scenarios increases, the optimal objective values of the two robust scenario formulations converge to a constant, which is a good estimate of the true optimal value. This new robust scenario approach is then extended for any bounded uncertainty regions...

Causal Uncertainty in Social Interactions: The Impact of Interpersonal Expectations and Uncertainty Reduction on Liking

Boucher, ELIANE
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 1092305 bytes; application/pdf
Português
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High causally uncertain (CU) individuals experience lingering doubts about their ability to determine the causes of social events (Weary & Edwards, 1994). Furthermore, these people tend to perceive their interactions and conversational partners more negatively (Boucher & Jacobson, 2009). However, the reasons for these negative reactions remain unclear. Therefore, the purpose of the current set of studies was to explore two possible explanations for these reactions. Specifically, in three studies, I examined if insufficient uncertainty reduction or negative interpersonal expectations mediate the relationship between causal uncertainty and liking for a recent acquaintance. In Study 1 (N = 114), participants engaged in a brief unstructured dyadic conversation, whereas in Study 2 (N = 176), they engaged in three conversations with different partners. Finally, in Study 3 (N = 220), I examined the effects of temporarily activating causal uncertainty beliefs during initial interactions. As predicted, causal uncertainty was negatively associated with liking and uncertainty reduction. In Studies 1 and 2, high CU participants reported more uncertainty about themselves and their partner, and less liking than did low CU participants. Although chronic levels of causal uncertainty in Study 3 were not associated with liking or uncertainty reduction...

CAUSAL UNCERTAINTY AND SELF-REGULATION ABILITIES

PASSEY, JENNIFER
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 491196 bytes; application/pdf
Português
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Causal uncertainty refers to the lack of confidence in one’s ability to understand causal relations in the social world (Weary & Edwards, 1994). Relative to people with low causal uncertainty, individuals with high causal uncertainty exhibit enhanced self-regulation performance following a social interaction (Jacobson, Papile, Passey, & Boucher, 2006). The current studies investigated the potential mechanisms underlying this relationship, and the role of self-esteem. Study 1 investigated whether the social or nonsocial nature of the depleting task and expectations about the need for future self-control could account for the relationship between causal uncertainty and self-regulation (N = 181). For the social task, high causally uncertain participants’ self-regulation performance was consistent across expectations for future self-control regardless of participant self-esteem. In contrast, low causally uncertain participants’ performance improved with increasing instructions to conserve energy for future tasks but only for participants with lower self-esteem. For low causally uncertain participants with higher self-esteem, self-regulation performance decreased with increased expectations for future self-control. In the nonsocial condition...

Uncertainty estimation of anions and cations measured by ion chromatography in fine urban ambient particles (PM2.5)

Alvarado, Ana María; Seguel, Rodrigo J.; Araya, Ma. Consuelo; Leiva Guzmán, Manuel Andrés
Fonte: Springer Publicador: Springer
Tipo: Artículo de revista
Português
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The present work presents a measurement uncertainty evaluation according to Guide to the Expression of Uncertainty in Measurement (GUM) of the concentration of the cations K? and Li? and anions NO3 -2 and SO4 -2 in fine airborne particulate matter, refers to particles less than 2.5 lm in diameter (PM2.5), as measured by ion chromatography (US-EPA 300 method). The GUM method is not typically used to report uncertainty. In general, the analytical results only report the measurement’s standard deviation under repetition as an uncertainty; thus, not all sources of uncertainty are considered. In this work, the major sources of uncertainty regarding the measurements were identified as contributions to linear least square regression lines, repeatability, precision, and trueness. The expanded uncertainty was approximately 20% for anions and cations. The largest contribution to uncertainty was found to be repeatability.

An investigation into the treatment of uncertainty and risk in roadmapping: a framework and a practical process

Ilevbare, Imohiosen Michael
Fonte: University of Cambridge; Department of Engineering; Institute for Manufacturing Publicador: University of Cambridge; Department of Engineering; Institute for Manufacturing
Tipo: Thesis; doctoral; PhD
Português
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This thesis investigates roadmapping in the context of its application to strategic early-stage innovation planning. It is concerned with providing an understanding of how uncertainty and risk are manifested in roadmapping in this application, and with developing and testing a roadmapping process that supports appropriate treatment of uncertainty and risk. Roadmapping is an approach to early-stage innovation planning, which is strategic in nature. It is seeing increasing application in practice and receiving growing attention in management literature. There has, however, been a noticeable lack of attention to uncertainty and risk in roadmapping theory and practice (and generally in strategic planning and at innovation?s early-stages). This is despite the awareness that uncertainty and risk are fundamental to strategy and innovation (i.e. application domains of roadmapping), and that roadmapping is meant to deliver, as part of its benefits, the identification, resolution and communication of uncertainties and risks. There is very limited theoretical or practical direction on what this entails. It is this gap that the research reported in thesis addresses. The research is divided into two phases. The first phase explains the manifestations and mechanisms of uncertainty and risk in roadmapping. It also introduces ?risk-aware roadmapping?...

A Toolkit for uncertainty reasoning and representation using fuzzy set theory in PROLOG expert systems

Bicker, Marcelle M.
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Português
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This thesis examines the issue of uncertainty reasoning and representation in expert systems. Uncertainty and expert systems are defined. The value of uncertainty in expert systems as an approximation of human reasoning is stressed. Five alternative methods of dealing with uncertainty are explored. These include Bayesian probabilities, Mycin confirmation theory, fuzzy set theory, Dempster-Shafer's theory of evidence and a theory of endorsements. A toolkit to apply uncertainty processing in PROLOG expert systems is developed using fuzzy set theory as the basis for uncertainty reasoning and representation. The concepts of fuzzy logic and approximate reasoning are utilized in the implementation. The toolkit is written in C-PROLOG for the PYRAMID UNIX system at the Rochester Institute of Technology.