The first two chapters of this thesis analyze the response of blood donors to several features of the blood market in Portugal. The first provides estimates of the blood supply elasticity using changes in a benefit scheme for regular blood donors. In Portugal, starting from 2003, the government strictly enforced the collection of medical user fees, but with regular donors receiving a waiver. Using within-county variations in the value of the benefit, measured as the user fee for a visit to the Emergency Department, I find that the benefit increases the number of donations, both unconditionally and conditional on the number of blood drives. I estimate a one euro increase in the user fee to increase blood donations by 1.8%, on average. I also estimate a negative elasticity of blood drives with respect to the user fees. This indicates that benefits and blood drives are substitutes in eliciting blood donations. The second chapter analyzes how waiting to donate blood affects donor retention. I use a panel of new blood donors, in Portugal, between 2008 and 2012. I find that higher waiting times make it less likely for first-time donors to donate again, controlling for donor and donation site-specific variables. A 10-minute increase in waiting time until triage results in a 0.6% decrease in donor retention, on average. Donors in the upper tail of the waiting time distribution are driving the effect. New donors at blood drives react more negatively than new donors at blood donor centers. The third chapter (joint with Matteo Galizzi and Raffaele Miniaci) reports experimental evidence on risk preferences measures, from two waves of a representative sample of the UK Household Longitudinal Survey. The subjects responded to three tests: two incentive-compatible lottery tests and a survey test measuring self-reported willingness to take risk. We find significant but low correlations between the responses to the three tests across time. Furthermore, we find that at least two thirds of the subjects made inconsistent choices across lottery tests, when controlling for individual-specific levels of background income. Finally, we find mixed evidence concerning the external validity of these tests.

According to the American Psychological Association (2006), three components should be equally considered in treatment decision-making: empirical research, clinical judgment, and the client’s values and preference. Swift, Callahan, and Vollmer (2011) defined client preferences as specific attributes that are desired in a therapeutic setting and are divided into three categories: role, therapist, and treatment-type. Currently, there is no treatment orientation scale that measures treatment type and magnitude of the relationship. For this initial phase of development, 5 treatment orientations are being used as the basis of the Client Treatment Orientation Scale (CTOS): psychodynamic, existential, cognitive-behavioral therapy, acceptance and commitment therapy, and multicultural. The purpose of this study is to begin development of a treatment orientation scale with 5-7 questions per subscale domain. A total sample of 651 participants completed the survey, was English speaking, and aged 18 or over, with the majority being male (n = 334, 51.3%). The mean age of participants was 31.91 (SD = 8.23), with an equal distribution of degree type (e.g. psychiatrist, clinical psychology, counseling psychology, and school psychology) with psychiatry the most endorsed at 26.6% (n = 173). Overall, results did not support the use of the CTOS in applied or research settings. Reliability analyses for the 5 subscales were: psychodynamic (α = .52), existential (α = .32), cognitive-behavioral therapy (α = .64), acceptance and commitment therapy (α = .46), and multicultural (α = .63). There were various limitations of the study, such as being self-report and the possibility of not being representative of the particular orientations being measured. Future research could re-examine items for latent variables or refine the current items for another factor analysis study.

Thesis (M. Phil.)--University of Hong Kong, 1999. / Includes bibliographical references (leaves 132-140).

Thesis (M.B.A.)--University of Hong Kong, 1992.

Thesis (M.B.A.)--University of Hong Kong, 1992. / Also available in print.

Thesis (Ph. D.)--University of Washington, 1986. / Vita. Bibliography: leaves [129]-138.

L’aide multicritère à la décision (AMCD) vise à faciliter et améliorer la qualité du processus de prise de décision. Les méthodes d’AMCD permettent de traiter les problèmes de choix, rangement et classification. Ces méthodes impliquent généralement la construction d’un modèle. Déterminer les valeurs des paramètres de ces modèles n’est pas aisé. Les méthodes d’apprentissage indirectes permettent de simplifier cette tâche en apprenant les paramètres du modèle de décision à partir de jugements émis par un décideur tels que “l’alternative a est préférée à l’alternative b” ou “l’alternative a doit être classifiée dans la meilleure catégorie”. Les informations données par le décideur sont généralement parcimonieuses. Le modèle d’AMCD est appris au cours d’un processus interactif entre le décideur et l’analyste. L’analyste aide le décideur à formuler et revoir ses jugements si nécessaire. Le processus s’arrête une fois qu’un modèle satisfaisant les préférences du décideur a été trouvé. Le “preference learning” (PL) est un sous domaine du “machine learning” qui s’intéresse à l’apprentissage des préférences. Les algorithmes de ce domaine sont capables de traiter de grands jeux de données et sont validés au moyen de jeux de données artificiels et réels. Les jeux de données traités en PL sont généralement collectés de différentes sources et sont entachés de bruit.Contrairement à l’AMCD, il existe peu ou pas d’interaction avec l’utilisateur en PL. Le jeu de données fourni en entrée à l’algorithme est considéré comme un échantillon éventuellement bruité d’une “réalité” ou “vérité de terrain”. Les algorithmes utilisés dans ce domaine ont des propriétés statistiques fortes leur permettant de s’affranchir du bruit dans ces jeux de données. Dans cette thèse, nous développons des algorithmes d’apprentissage permettant d’apprendre lesparamètres de modèles d’AMCD. Plus précisément, nous développons une métaheuristique afin d’apprendre les paramètres d’un modèle appelé MR-Sort (“majority rule sorting”). Cette métaheuristique est testée sur des jeux de donnéesartificiels et réels utilisés dans le domaine du PL. Nous utilisons cet algorithme afin de traiter un problème concret dans le domaine médical. Ensuite nous modifions la métaheuristique afin d’apprendre les paramètres d’un modèle plus expressif appelé NCS (“non-compensatory sorting”). Finalement, nous développons un nouveau type de règle de veto pour les modèles MR-Sort et NCS qui permet de prendre les coalitions de critères en compte. La dernière partie de la thèse introduit les méthodes d’optimisation semi-définie positive (SDP) dans le contexte de l’aide multicritère à la décision. Précisément, nous utilisons l’optimisation SDP afin d’apprendre les paramètres d’un modèle de fonction de valeur additive. / Multiple-criteria decision analysis (MCDA) aims at providing support in order to make a decision. MCDA methods allow to handle choice, ranking and sorting problems. These methods usually involve the elicitation of models. Eliciting the parameters of these models is not trivial. Indirect elicitation methods simplify this task by learning the parameters of the decision model from preference statements issued by the decision maker (DM) such as “alternative a is preferred to alternative b” or “alternative a should be classified in the best category”. The information provided by the decision maker are usually parsimonious. The MCDA model is learned through an interactive process between the DM and the decision analyst. The analyst helps the DM to modify and revise his/her statements if needed. The process ends once a model satisfying the preferences of the DM is found. Preference learning (PL) is a subfield of machine learning which focuses on the elicitation of preferences. Algorithms in this subfield are able to deal with large data sets and are validated withartificial and real data sets. Data sets used in PL are usually collected from different sources and aresubject to noise. Unlike in MCDA, there is little or no interaction with the user in PL. The input data set is considered as a noisy sample of a “ground truth”. Algorithms used in this field have strong statistical properties that allow them to filter noise in the data sets.In this thesis, we develop learning algorithms to infer the parameters of MCDA models. Precisely, we develop a metaheuristic designed for learning the parameters of a MCDA sorting model called majority rule sorting (MR-Sort) model. This metaheuristic is assessed with artificial and real data sets issued from the PL field. We use the algorithm to deal with a real application in the medical domain. Then we modify the metaheuristic to learn the parameters of a more expressive model called the non-compensatory sorting (NCS) model. After that, we develop a new type of veto rule for MR-Sort and NCS models which allows to take criteria coalitions into account. The last part of the thesis introduces semidefinite programming (SDP) in the context of multiple-criteria decision analysis. We use SDP to learn the parameters of an additive value function model.

This thesis hypothesizes that a method for selecting instructional strategies (specifically media) based in part on a relationship between learning style preference and personality preference provides more relevant and understandable feedback to students and thereby higher learning effectiveness. This research investigates whether personality preferences are valid predictors of learning style preferences. Since learning style preferences are a key consideration in instructional strategies and instructional strategies are a key consideration in learning effectiveness, this thesis contributes to a greater understanding of the relationship between personality preferences and effective learning in intelligent tutoring systems (ITS). This research attempts to contribute to the goal of a "truly adaptive ITS" by first examining relationships between personality preferences and learning style preferences; and then by modeling the influences of personality on learning strategies to optimize feedback for each student. This thesis explores the general question "what can personality preferences contribute to learning in intelligent tutoring systems?" So, why is it important to evaluate the relationship between personality preferences and learning strategies in ITS? "While one-on-one human tutoring is still superior to ITS in general, this approach is idiosyncratic and not feasible to deliver to [any large population] in any cost-effective manner." (Loftin, 2004). Given the need for ITS in large, distributed populations (i.e. the United States Army), it is important to explore methods of increasing ITS performance and adaptability. Findings of this research include that the null hypothesis that "there is no dependency between personality preference variables and learning style preference variables" was partly rejected. Highly significant correlations between the personality preferences, openness and extraversion, were established for both the active-reflective and sensing-intuitive learning style preferences. Discussion of other relationships is provided.

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published_or_final_version / Business Administration / Master / Master of Business Administration