This thesis studies the problem of measuring freedom of choice. It analyzes the concept of freedom of choice, discusses conditions that a measure should satisfy, and introduces a new class of measures that uniquely satisfy ten proposed conditions. The study uses a decision-theoretical model to represent situations of choice and a metric space model to represent differences between options. The first part of the thesis analyzes the concept of freedom of choice. Different conceptions of freedom of choice are categorized into evaluative and non-evaluative, as well as preference-dependent and preference-independent kinds. The main focus is on the three conceptions of freedom of choice as cardinality of choice sets, representativeness of the universal set, and diversity of options, as well as the three conceptions of freedom of rational choice, freedom of eligible choice, and freedom of evaluated choice. The second part discusses the conceptions, together with conditions for a measure and a variety of measures proposed in the literature. The discussion mostly focuses on preference-independent conceptions of freedom of choice, in particular the diversity conception. Different conceptions of diversity are discussed, as well as properties that could affect diversity, such as the cardinality of options, the differences between the options, and the distribution of differences between the options. As a result, the diversity conception is accepted as the proper explication of the concept of freedom of choice. In addition, eight conditions for a measure are accepted. The conditions concern domain-insensitivity, strict monotonicity, no-choice situations, dominance of differences, evenness, symmetry, spread of options, and limited function growth. None of the previously proposed measures satisfy all of these conditions. The third part concerns the construction of a ratio-scale measure that satisfies the accepted conditions. Two conditions are added regarding scale-independence and function growth proportional to cardinality. Lastly, it is shown that only one class of measures satisfy all ten conditions, given an additional assumption that the measures should be analytic functions with non-zero partial derivatives with respect to some function of the differences. These measures are introduced as the Ratio root measures.

Decision makers have long relied on experts to inform their decision making. Expert judgment analysis is a way to elicit and combine the opinions of a group of experts to facilitate decision making. The use of expert judgment is most appropriate when there is a lack of data for obtaining reasonable statistical results. The experts are asked for advice by one or more decision makers who face a specific real decision problem. The decision makers are outside the group of experts and are jointly responsible and accountable for the decision and committed to finding solutions that everyone can live with. The emphasis is on the decision makers learning from the experts. The focus of this thesis is an overview and comparison of the various elicitation and combination methods available. These include the traditional committee method, the Delphi method, the paired comparisons method, the negative exponential model, Cooke’s classical model, the histogram technique, using the Dirichlet distribution in the case of a set of uncertain proportions which must sum to one, and the employment of overfitting. The supra Bayes approach, the determination of weights for the experts, and combining the opinions of experts where each opinion is associated with a confidence level that represents the expert’s conviction of his own judgment are also considered.

Dans cette thèse, nous proposons une perspective méthodologique sur le double rôle du langage dans la rationalité économique, les utilisations de langage par les économistes pour la théoriser et les utilisations de langage par les agents économiques pour l’exprimer, pour clarifier trois principales questions (et leurs connexions) qui sous-tendent les débats entre économie comportementale et économie standard : le problème de l’unification théorique vis-à-vis des trois dimensions de la rationalité économique, la question de l’interdisciplinarité entre économie et Psychologie, et le problème du positif/normatif dans les modèles de comportements individuels. Concernant le problème du positif/normatif et le rôle du langage dans les comportements des agents économiques, notre intention est de fournir, au-delà de la simple clarification, une critique constructive des contributions de l’économie standard comme de l’économie comportementale. Suivant la position de l’enchevêtrement du philosophe Hilary Putnam et des philosophes-économistes Vivian Walsh et Amartya Sen, il est soutenu que l’économie tant standard que comportementale propose une articulation insatisfaisante des dimensions positive et normative dans les modèles de comportements individuels; et que la reconnaissance de l’enchevêtrement de faits, de valeurs et de conventions peut être théoriquement et empiriquement fructueuse. Prêter attention au rôle du langage dans les comportements des agents économiques montre parfois qu’un comportement apparemment irrationnel peut en fait être défendu comme rationnel; c’est pourquoi nous soutenons que, et montrons comment, l'axiome implicite - connu sous le nom d’invariance à la description - dans les modèles standards de comportements individuels empêchant l’influence du langage doit être affaibli (mais pas complètement supprimé), contrairement aux positions de la plupart des économistes standards et comportementaux. / In this dissertation, we propose a methodological perspective on the twofold role of language in economic rationality, economists’ uses of language to theorize it and economic agent’s uses of language to express it, can clarify three main issues (and their connections), underlying the behavioral versus standard economics debates: the issue of the theoretical unification regarding the three dimensions of economic rationality, the issue of interdisciplinarity between economics and Psychology and the positive/normative issue within models of individual behaviors. Regarding the positive/normative issue and the role of language in the behaviors of economic agents, the intention is to provide a constructive criticism of contributions from behavioral as well as standard economists. Following the entanglement thesis of philosopher Hilary Puntam and philosophers-economists Vivian Walsh and Amartya Sen, it is argued that both standard and behavioral economists propose an unsatisfying articulation between the positive and normative dimensions of models of individual behaviors; and that recognizing the entanglement of facts, values and conventions can actually be theoretically and empirically fruitful. Paying some attention to the role of language in the behaviors of economic agents may sometimes show that a seemingly irrational behavior can in fact be defended as rational; hence we argue that, and show how, the implicit axiom -- known as ‘description invariance’ -- in standard models of individual behaviors preventing the influence of language needs to be weakened (though not dropped entirely), contrary to the positions of most behavioral and standard economists.

The objective of this thesis is to apply alternative multi-objective optimization techniques to the portfolio selection problem. Theoretical part starts with detailed analysis of the classical Markowitz model and its assumptions. Following that, introduction of multi-criterion optimization techniques available for finding non-dominated portfolios is given. One of these techniques, the genetic algorithm, is presented in great detail. Some of the basic methods useful for predicting stock prices and its risks are presented at the end of the theoretical part. Practical part presents an application of the theory to the problem of constructing efficient portfolios of 11 selected stocks traded on Prague Stock Exchange. Results achieved by different approaches are compared and interpreted.

This thesis is concerned with the problem of how to make decisions in an uncertain world. We use a model of uncertainty based on Markov decision problems, and develop a number of algorithms for decision-making both for the planning problem, in which the model is known in advance, and for the reinforcement learning problem in which the decision-making agent does not know the model and must learn to make good decisions by trial and error. The basis for much of this work is the use of structural representations of problems. If a problem is represented in a structured way we can compute or learn plans that take advantage of this structure for computational gains. This is because the structure allows us to perform abstraction. Rather than reasoning about each situation in which a decision must be made individually, abstraction allows us to group situations together and reason about a whole set of them in a single step. Our approach to abstraction has the additional advantage that we can dynamically change the level of abstraction, splitting a group of situations in two if they need to be reasoned about separately to find an acceptable plan, or merging two groups together if they no longer need to be distinguished. We present two planning algorithms and one learning algorithm that use this approach. A second idea we present in this thesis is a novel approach to the exploration problem in reinforcement learning. The problem is to select actions to perform given that we would like good performance now and in the future. We can select the current best action to perform, but this may prevent us from discovering that another action is better, or we can take an exploratory action, but we risk performing poorly now as a result. Our Bayesian approach makes this tradeoff explicit by representing our uncertainty about the values of states and using this measure of uncertainty to estimate the value of the information we could gain by performing each action. We present both model-free and model-based reinforcement learning algorithms that make use of this exploration technique. Finally, we show how these ideas fit together to produce a reinforcement learning algorithm that uses structure to represent both the problem being solved and the plan it learns, and that selects actions to perform in order to learn using our Bayesian approach to exploration. / Science, Faculty of / Computer Science, Department of / Graduate

A transformative decision rule alters the representation of a decisionproblem, either by changing the sets of acts and states taken intoconsideration, or by modifying the probability or value assignments.Examples of decision rules belonging to this class are the principleof insufficient reason, Isaac Levi’s condition of E-admissibility, Luceand Raiffa’s merger of states-rule, and the de minimis principle. Inthis doctoral thesis transformative decision rules are analyzed froma foundational point of view, and applied to two decision theoreticalproblems: (i) How should a rational decision maker model a decisionproblem in a formal representation (‘problem specification’, ‘formaldescription’)? (ii) What role can transformative decision rules play inthe justification of the principle of maximizing expected utility?The thesis consists of a summary and seven papers. In Papers Iand II certain foundational issues concerning transformative decisionrules are investigated, and a number of formal properties of this classof rules are proved: convergence, iterativity, and permutability. InPaper III it is argued that there is in general no unique representationof a decision problem that is strictly better than all alternative representations.In Paper IV it is shown that the principle of maximizingexpected utility can be decomposed into a sequence of transformativedecision rules. A set of axioms is proposed that together justify theprinciple of maximizing expected utility. It is shown that the suggestedaxiomatization provides a resolution of Allais’ paradox that cannot beobtained by Savage-style, nor by von Neumann and Morgenstern-styleaxiomatizations. In Paper V the axiomatization from Paper IV is furtherelaborated, and compared to the axiomatizations proposed byvon Neumann and Morgenstern, and Savage. The main results in PaperVI are two impossibility theorems for catastrophe averse decisionrules, demonstrating that given a few reasonable desiderata for suchrules, there is no rule that can fulfill the proposed desiderata. In PaperVII transformative decision rules are applied to extreme risks, i.e.to a potential outcome of an act for which the probability is low, butwhose (negative) value is high. / <p>QC 20100622</p>

This thesis presents a methodology for obtaining the optimal design capacity for sediment yield in multipurpose reservoir design. A stochastic model is presented for the prediction of sediment yield in a semi -arid watershed based on rainfall data and watershed characteristics. Uncertainty stems from each of the random variables used in the model, namely, rainfall amount, storm duration, runoff, peak flow rate, and number of events per season. Using the stochastic sediment yield model for N- seasons, a Bayesian decision analysis is carried out for a dam site in southern Arizona. Extensive numerical analyses and simplifying assumptions are made to facilitate finding the optimal solution. The model has applications in the planning of reservoirs and dams where the effective lifetime of the facility may be evaluated in terms of storage capacity and of the effects of land management on the watershed. Experimental data from the Atterbury watershed are used to calibrate the model and to evaluate uncertainties associated with our knowledge of the parameters of the joint distribution of rainfall and storm duration used in calculating the sediment yield amount.

Orientadores: Clarice Weis Arns, Márcia Bianchi dos Santos / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-27T11:13:27Z (GMT). No. of bitstreams: 1 Carvalho_RicardoDuraesde_D.pdf: 3518273 bytes, checksum: 7b6f8b159eb057429823e23f6852c29b (MD5) Previous issue date: 2015 / Resumo: A evolução e a dinâmica populacional dos Coronavírus (CoVs) ainda permanecem pouco exploradas. No presente estudo, análises filogenéticas e de filogeografia foram conduzidas para investigar a dinâmica evolutiva dos CoVs detectados em aves silvestres e sinantrópicas. Um total de 500 amostras, que inclui os suabes traqueais e cloacais coletados de 312 aves silvestres pertencentes a 42 espécies, foram analisadas através da RT-qPCR. Sessenta e cinco amostras (13%) provenientes de 23 espécies foram positivas para o Coronavírus aviário (AvCoV). Trezentos e duas amostras foram investigadas para a pesquisa do Pan-Coronavírus (Pan-CoV) através do nPCR, destas, 17 (5,6%) foram positivas, sendo que 11 foram detectadas em espécies diferentes. Análises filogenéticas dos AvCoVs revelaram que as sequências de DNA das amostras coletadas no Brasil não agruparam com nenhuma das sequências do gene Spike (S1) dos AvCoVs depositados no banco de dados GenBank. Análise Bayesiana estimou uma variante do AvCoV proveniente da Suécia (1999) como o ancestral comum mais recente dos AvCoVs detectados neste estudo. Além disso, as análises realizadas através do "Bayesian Skyline Plot" (BSP) inferiram um aumento na dinâmica da população demográfica do AvCoV em diferentes espécies de aves silvestres e sinantrópicas. As análises filogenéticas do Pan-CoV mostrou que a maioria das amostras se agruparam com o Vírus da Hepatite Murina A59 (MHV A59), CoV pertencente ao grupo dos Beta-CoVs. Uma amostra [CoV detectado em Amazona vinacea(Papagaio-de-peito-roxo)] se agrupou com um CoV de Suínos, o PCoV HKU15, que pertence ao gênero Delta-CoV, ainda não relatado na América do Sul. Nossos achados sugerem que as aves podem ser novos potenciais hospedeiros responsáveis pela propagação e disseminação de diferentes CoVs para diferentes espécies de animais / Abstract: The evolution and population dynamics of Coronaviruses (CoVs) still remain underexplored. In the present study, phylogenetic and phylogeographic analyseswere conducted to investigate the evolutionary dynamics of CoV detected in wild and synanthropic birds. A total of 500 samples, including tracheal and cloacal swabs collected from 312 wild birds belonging to 42species, were analysed by RT-qPCR. A total of 65 samples from 23bird species were positive for Avian Coronaviruses (AvCoVs).Three hundred and two samples were screened for the Pan-Coronavirus (Pan-CoV) through the nPCR, 17 (5.6%) were positive, being that 11 were detected in different species. AvCoVs phylogenetic analyses revealed that the DNA sequences from samples collected in Brazil did not cluster with any of the AvCoV S1 gene sequences deposited in the GenBank database. Bayesian framework analysis estimated an AvCoV strain from Sweden (1999) as the most recent common ancestor of the AvCoVs detected in this study. Furthermore, Bayesian Skyline Plot (BSP) analysis inferred an increase in the AvCoV dynamic demographic population in different wild and synanthropic bird species. Phylogenetic analysis of the Pan-CoV showed that most of the samples clustered with the Murine Hepatitis Virus A59 strain (MHV A59) belong to the BetaCoV group. Besides, one of our samples [CoV detected in Amazona vinacea (parrot-breasted-purple)] clustered with a CoV isolated from pigs, PCoV HKU15, belonging to the DeltaCoV genus, still not reported in South America. Our findings suggest that birds may be potential new hosts responsible for spreading of different CoVs for different species of animals / Doutorado / Microbiologia / Doutor em Genetica e Biologia Molecular

This thesis explores the use of unstructured data, and specifically textual data, in providing consumer insights and improving business decisions. The thesis consists of two essays. In essay I, I examine how the linguistic similarity between the language used by reviewers of a product and a prospective customer’s own writing style can be leveraged to assess the match between customers and products. Applying tools from machine learning, Bayesian statistics, and computational linguistics to a large-scale dataset from Yelp, I find that the closer the writing style of a restaurant’s past reviews are to a prospective customer’s writing style, the more likely that customer is to write a review for that restaurant. This effect holds across restaurant types and is driven by the linguistic similarity between the customer’s own reviews and positive past reviews for the restaurant. Further, I find that similarity with respect to words related to leisure (e.g., family, wine, beer, weekend), biology (e.g., eat, life, love), as well as swear words are most influential in creating a match between customers and restaurants. In essay II, I examine whether borrowers consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. I find that this textual information has a substantial and significant ability to predict whether borrowers will pay back the loan above and beyond the financial and demographic variables commonly used in models predicting default. Using text-mining and machine-learning tools to automatically process and analyze the raw text in over 120 thousand loan requests from Prosper.com, an online crowdfunding platform, I find that including the textual information in the loan significantly helps predict loan default and can have substantial financial implications. I find that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of God, the borrower’s financial and general hardship, pleading lenders for help, and short-term focused words. I further observe that defaulting loan requests are written in a manner consistent with the writing style of extroverts and liars.

Thesis advisor: Uzi Segal / This dissertation consists of three chapters analyzing preferences for randomization in social choice problems. The first two chapters are related and in the fields of distributive justice and social choice. They concern allocation of an indivisible good in social choice problems where efficiency is at odds with equality. The last chapter addresses a social choice problem from an individual's perspective using decision theoretical analysis. In this dissertation I demonstrate why randomization may be an attractive policy in social choice problems and demonstrate how individuals may have preferences over the precise method of randomization. The first chapter is titled "Live and Let Die." This paper discusses how to allocate an indivisible good by social lottery when agents have asymmetric claims. Intuition suggests that there may exist agents who should receive zero probability in the optimal social lottery. In such a case, I say that these agents have weak claims to the good. This paper uses a running example of allocating an indivisible medical treatment to individuals with different survival rates and reactions to the treatment in order to provide conditions for consistency of weak claims. As such, I develop two related assumptions on a social planner's preferences over lotteries. The first -- survival rate scaling -- states that if an individual has a weak claim, then his claim is also weak when survival rates increase proportionally. The second -- independence of weak claims -- states that if an individual has a weak claim, then his removal does not affect others' probabilities of receiving the treatment. These assumptions imply that a compatible social welfare function must exhibit constant elasticity of substitution, which results in potentially-degenerate weighted lotteries. The second chapter is titled "Why is Six Afraid of Seven? Bringing the "Numbers" to Economics." This chapter discusses the numbers problem: the question of if the numbers of people involved should be used to determine whether to help certain people or to help certain other people. I discuss the main solutions that have been proposed: flipping a coin, saving the greater number, and proportionally weighted lotteries. Using the economic tools of social choice, I then show how the model of the previous chapter, "Live and Let Die," can be extended to address numbers problems and compare the implications of prominent social welfare functions for numbers problems. I argue that potentially-degenerate weighted lotteries can assuage the main concerns discussed in the literature and I show that both the Nash product social welfare function as well as constant elasticity of substitution (CES) social welfare functions are compatible with this solution. Finally, I discuss a related problem known as "probability cases," in which individuals differ in survival chances rather than numbers of individuals at risk. When the model is extended to allow for both asymmetries in survival chances and numbers of individuals in groups, CES results in potentially-degenerate weighted lotteries whereas Nash product does not. The third chapter is titled "All Probabilities are Equal, but Some Probabilities are More Equal than Others," which is joint work with Professor Uzi Segal of the Economics Department at Boston College and Professor Shlomo Naeh of the Departments of Talmud and Jewish Thought at The Hebrew University of Jerusalem. In this chapter we compare preferences for different procedures of selecting people randomly. A common procedure for selecting people is to have them draw balls from an urn in turn. Modern and ancient stories (for example, by Graham Greene and the Talmud) suggest that such a lottery may not be viewed by the individuals as "fair.'' In this paper, we compare this procedure with several alternatives. These procedures give all individuals equal chance of being selected, but have different structures. We analyze these procedures as multi-stage lotteries. In line with previous literature, our analysis is based on the observation that multi-stage lotteries are not considered indifferent to their probabilistic one-stage representations. As such, we use a non-expected utility model to understand the preferences of risk-averse individuals over these procedures and show that they may be not indifferent between them. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.