The amount of information involved in many decision making situations has increased dramatically in recent years and support of some kind is often needed. Consequently, fields like Business Intelligence (BI) and Decision Support Systems (DSS) have advanced. Decision analysis applications belong to the latter category and aim to support decision making activities in businesses and organizations, and provide more clearly structured decision material to use as a basis for decisions. In spite of a belief in their potential, their employment is still limited in practice, which could partly be attributed to the fact that they are incomplete to support decision processes sufficiently in real settings. At present, e.g., the specification and execution of the elicitation of input data is often left to the discretion of the user. Yet, this involves quite a few problematic elements and is of importance for the quality of the process as a whole. This thesis focuses on more practically useful elicitation of information in decision analysis applications than what is offered today. A process model emphasizing the importance of structured elicitation of adequate input data throughout decision processes is also suggested. In order to further define the problematic aspects of elicitation, three empirical studies were conducted. The problems with eliciting precise decision data suggests that using imprecise values within elicitation is a more realistic and useful approach to strive for. Based on theory and the findings of the studies, a weight elicitation method for imprecise statements and noisy input was formalized into the Cardinal Rank Ordering of Criteria (CROC) method. This method is both compatible with an adapted prescriptive decision making model, focused on a more structured elicitation component, as well as algorithms for dealing with such data. The CROC method was employed and validated in two real-life cases, which is not so common within decision analysis research. / Mängden information i många beslutssituationer har ökat markant under senare år och det finns ofta behov av någon form av stöd. Följaktligen har områden som Business Intelligence (BI) och Beslutsstödssystem (BSS) avancerat. Beslutsanalysverktyg tillhör den senare kategorin och syftar till att fungera som stöd vid beslutsfattande inom företag och organisationer och tillhandahålla mer strukturerat underlag för beslut. Trots en tro på deras potential, så är deras användande begränsat i praktiken, vilket delvis kan tillskrivas det faktum att de är inkompletta för att stödja beslutsprocesser i tillräcklig utsträckning i verkligheten. För närvarande förutsätts, t.ex. ofta att användaren själv klarar av att specificera och utföra utvinningen (eliciteringen) av input data. Detta involverar dock ett antal problematiska delar och dess kvalité är av vikt för hela processen. Denna avhandling fokuserar på mer praktiskt användbar elicitering av information i beslutsanalys-applikationer än vad som finns att tillgå idag. En processmodell som betonar vikten av strukturerad elicitering av adekvata indata genom hela beslutsprocessen föreslås också. För att ytterligare definiera de problematiska aspekterna av elicitering utfördes tre empiriska studier. Problemen med att utvinna precisa beslutsdata antyder att användandet av oprecisa värden inom elicitering är en mer realistisk och användbar ansats att sträva efter. Baserat på teori och resultaten av studierna formaliserades en vikteliciterings-metod för oprecisa utlåtanden och osäkra indata i Cardinal Rank Ordering of Criteria (CROC) metoden. Metoden är både kompatibel med en anpassad preskriptiv beslutsmodell fokuserad på en mer strukturerad eliciteringskomponent samt algoritmer för att hantera denna typ av data. CROC-metoden användes och validerades i två riktiga fall, vilket inte är så vanligt inom beslutsanalys forskning. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Accepted. Paper 7: Submitted. </p>

The question of how to measure and classify people's risk preferences is of substantial importance in the field of economics. Inspired by the multitude of ways used to elicit risk preferences, we conduct a holistic investigation of the most prevalent method, the multiple price list (MPL) and its derivations. In our experiment, we find that revealed preferences differ under various versions of MPLs as well as yield unstable results within a 30-minute time frame. We determine the most stable elicitation method with the highest forecast accuracy by using multiple measures of within-method consistency and by using behavior in two economically relevant games as benchmarks. A derivation of the well-known method by Holt and Laury (American Economic Review 92(5):1644-1655, 2002), where the highest payoff is varied instead of probabilities, emerges as the best MPL method in both dimensions. As we pinpoint each MPL characteristic's effect on the revealed preference and its consistency, our results have implications for preference elicitation procedures in general.

Actualmente existe un gran número de técnicas y herramientas para realizar estimaciones en los procesos de software, pero muchas de ellas requieren de gran volumen de información del proyecto que se está analizando, dificultando una estimación temprana del esfuerzo requerido para desarrollar dicho proyecto. Aquellos analistas que trabajan con el Léxico Extendido del Lenguaje, al contar con este modelo en etapas tempranas del software, pueden inferir ciertas características del proyecto, como pueden ser los Casos de Uso, las clases y entidades de base de datos que formaran parte del diseño del proyecto. Por otro lado, existen técnicas de estimación de esfuerzo ampliamente utilizadas y estandarizadas que se valen de estas características, como por ejemplo Puntos Caso de Uso, pero que en una etapa temprana de elicitación de requerimientos no son aplicables por falta de información. Este trabajo pretende brindar a los usuarios que utilizan Léxico Extendido del Lenguaje en su proceso de elicitación de requerimientos, una herramienta que, a partir de la información recabada en las etapas tempranas de dicho proceso, proporcione una estimación del esfuerzo necesario para realizar el proyecto, basada en un método ampliamente utilizado y estandarizado.

Requirement elicitation is a critical activity in the requirement development process and it explores the requirements of stakeholders. The success or failure of this process is based on identifying the relevant stakeholders and discovering their needs as well as the quality of requirements. The quality of the requirements is greatly influenced by methods applied during requirements elicitation process. Only complete and structured requirements make these projects more reliable. The common challenges that analysts face during elicitation process are to ensure effective communication between stakeholders as well as the acquisition of tacit knowledge. Mostly errors in the systems are due to poor communication between user and analyst, and these errors require more resources (time and money) to correct them. The understandability problems during elicitation process of large web projects can lead to requirements ambiguous, inconsistent, incorrect and unusable. Different methods (Conversational, Observational, Analytical and Synthetic) are available to deal with the problems during requirement elicitation process. The challenge for analysts is to select an appropriate method or set of methods and apply them for the clear, consistent and correct requirement gathering. This study based on the results of interviews conducted to the professionals, who have industrial experience in development of web systems. The elicitation problems that are identified in literature and interview along with applicability of elicitation methods for requirement gathering in large web projects development are documented in this report. / Umar Sajjad Charhoi, Kotli, Azad Kashmir, Pakistan Muhammad Qaisar Hanif Bhimber, Azad Kashmir, Pakistan

Decision‐makers in organisations and businesses make numerous decisions every day, and these decisions are expected to be based on facts and carried out in a rational manner. However, most decisions are not based on precise information or careful analysis due to several reasons. People are, e.g., unable to behave rationally as a result of their experiences, socialisation, and additionally, because humans possess fairly limited capacities for processing information in an objective manner. In order to circumvent this human incapacity to handle decision situations in a rational manner, especially those involving risk and uncertainty, a widespread suggestion, at least in managerial decision making, is to take advantage of support in the form of decision support systems. One possibility involves decision analytical tools, but they are, almost without exception, not efficiently employed in organisations and businesses. It appears that one reason for this is the high demands the tools place on the decision‐maker in a variety of ways, e.g., by presupposing that reliable input data is obtainable by an exogenous process. Even though the reliability of current decision analytic tools is highly dependent on the quality of the input data, they rarely contain methods for eliciting data from the users. The problem focused on in this thesis is the unavailability and inefficiency of methods for eliciting decision information from the users. The aim is to identify problem areas regarding the elicitation of decision data in real decision making processes, and to propose elicitation methods that take people’s natural choice strategies and natural behaviour into account. In this effort, we have identified a conceptual gap between the decision‐makers, the decision models, and the decision analytical tools, consisting of seven gap components. The gap components are of three main categories (of which elicitation is one). In order to study elicitation problems, a number of empirical studies, involving more than 400 subjects in total, have been carried out in Sweden and Brazil. An iterative research approach has been adopted and a combination of quantitative and qualitative methods has been used. Findings made in this thesis include the fact that decision‐makers have serious problems in many decision situations due to not having access to accurate and relevant data in the first place, and secondly, not having the means for retrieving such data in a proper manner, i.e. lacking elicitation methods for this purpose. Employing traditional elicitation methods in this realm yield results that reveal an inertia gap, i.e. an intrinsic inertia in people’s natural behaviour to shift between differently framed prospects, and different groups of decisionmakers displaying different choice patterns. Since existing elicitation methods are unable to deal with the inertia, we propose a class of methods to take advantage of this natural behaviour, and also suggest a representation for the elicited information. An important element in the proposed class of methods is also that we must be able to fine‐tune methods and measuring instruments in order to fit into different types of decision situations, user groups, and choice behaviours.

Cette thèse porte sur la façon dont les individus prennent des décisions en présence de risque et d'incertitude. Elle est composée de quatre essais qui étudient théoriquement et expérimentalement la prise de décision.Les deux premiers essais étudient des situations où un décideur doit décider si un événement a eu lieu en utilisant des informations incertaines. Le fait d'identifier correctement que cet événement s'est produit est plus rémunéré que le fait d'identifier correctement qu'il ne s'est pas produit. Ce problème de décision induit une divergence entre deux qualités d'une décision : l'optimalité et l'exactitude. Les deux essais reproduisent de telles situations dans une expérience de laboratoire basée sur des tâches perceptuelles et analysent les décisions en utilisant la théorie de la détection du signal pour étudier l'arbitrage optimalité-exactitude. Le premier essai confirme l'existence d'un tel arbitrage avec un rôle dominant de la recherche de l'exactitude. Il explique l'existence de cet arbitrage par utilité non-monétaire associée au fait d'avoir raison. Le deuxième chapitre montre que présenter les informations perceptuelles en dernier contribue à l'existence de l'arbitrage optimalité-exactitude.Le troisième essai étudie comment les préférences vie-à-vie d'autrui interagissent avec l'attitude face à l'ambiguïté. Il présente les résultats d'une expérience où les sujets doivent faire des dons à des associations caritatives. Les dons peuvent avoir des coûts ou des bénéfices ambigus. Nous constatons que l'ambiguïté a pour effet de rendre les individus plus égoïstes. En d'autres termes, nous montrons que les individus utilisent l'ambiguïté comme une excuse pour ne pas donner. Ce comportement d’auto-justification est plus marqué pour les coûts ambigus que pour les avantages ambigus.Le quatrième essai examine la validité externe des mesures de préférence pour le risque en laboratoire en utilisant des décisions dans d'autres tâches expérimentales risquées et des décisions prisent sur en dehors du laboratoire. Nous constatons que les mesures de préférence pour le risque permettent d'expliquer les premières, mais qu'elles n'expliquent pas les secondes. / This thesis investigates how individuals make decisions under risk and uncertainty. It is composed of four essays that theoretically and experimentally investigate decision-making.The first two essays study situations where a decision maker has to decide whether an event has occurred using uncertain evidence. Accurately identifying that this event has occurred is more rewarded than accurately identifying that it has not occurred. This decision problem induces a divergence between two qualities of a decision: optimality and accuracy. Both essays reproduce such situations in a laboratory experiment based on perceptual tasks and analyze behavior using Signal Detection Theory to study the optimality-accuracy trade-off. The first essay confirms the existence of the trade-off with a leading role of accuracy. It explains the trade-off by the concern of individuals for being right. The second chapter finds that presenting perceptual evidence last contributes to the existence of the optimality-accuracy trade-off.The third essay studies how other-regarding preferences interact with attitude toward ambiguity. It reports the results of an experiment where subjects have to make donations to charities. Donations may have either ambiguous costs or ambiguous benefits. We find that other-regarding preferences are decreased under ambiguity. In other terms, we highlight that individual use ambiguity has an excuse not to give. This excuse-driven behavior is stronger for ambiguous costs than ambiguous benefits.The fourth essay challenges the external validity of laboratory risk preference measures using behavior in experimental risk tasks and naturally occurring behavior under risk. We find that risk preference measures are related with the former but that they fail to explain the latter.