Private Real Estate Investment Analysis within a One-Shot Decision Framework

Land development is a typical one-shot decision for private investors due to the huge investment expense and the fear of substantial loss. In this paper, a private real estate investment problem is analyzed within a one-shot decision framework, which is used for a situation where a decision is made only once. The one-shot decision framework involves two steps. The first is to identify which state of nature should be focused for each alternative. The second is to evaluate alternatives by using the focused states of nature. In a one-shot decision framework, the behavior of different types of private investors, such as normal, active, passive and more easily satisfied ones, are examined. The analysis provides insights into personal real estate investment and important policy implications in the regulation of urban land development.Private real estate investment; Possibility theory; One-shot decision; Focus points

Beliefs and Dynamic Consistency,

In this chapter, we adopt the decision theoretic approach to the representation and updating of beliefs. We take up this issue and propose a reconsideration of Hammond's argument. After reviewing the argument more formally, we propose a weaker notion of dynamic consistency. We observe that this notion does not imply the full fledged sure thing principle thus leaving some room for models that are not based on expected utility maximization. However, these models still do not account for ''imprecision averse" behavior such as the one exhibited in Ellsberg experiment and that is captured by non-Bayesian models such as the multiple prior model. We therefore go on with the argument and establish that such non-Bayesian models possess the weak form of dynamic consistency when the information considered consists of a reduction in imprecision (in the Ellsberg example, some information about the proportion of Black and Yellow balls)R. Arena and A. Festré

MAMA BEAR CONVERSATIONS: A TEMPLATE ANALYSIS OF MOTHERS MESSAGE BOARD DISCUSSIONS OF HPV VACCINATION AND WEIGHT MANAGEMENT FOR THEIR PRE-ADOLESCENT AND ADOLESCENT CHILDREN

The growth of the Internet has allowed users to gather in online spaces to share thought processes and information about any number of topics, and mothers in particular have found value in these communities as they seek to navigate the rough waters of motherhood. The aim of this research is to examine three message board forum threads at Cafemom.com where mothers gather to discuss two specific health care concerns for their pre-adolescent and adolescent children: human papillomavirus (HPV) vaccination and weight management. In an attempt to understand how mothers gather in these spaces to discuss these important pre-adolescent and adolescent health issues among themselves, I utilized computer-mediated discourse analysis to identify the forums as a community of information sharing and template analysis to identify themes relating to these specific health concerns. I focused on how mothers expressed their understanding of the complexities of HPV vaccination and weight management, how they shared their information with one another in an online message board setting, how they framed their posts in possibilistic and probabilistic frameworks, and how they established credibility among themselves. Next, within the structures of template analysis, I identified six Major Themes: impetus for discussion; framework for discussion; decision making statements; issues of knowledge; issues of agency; and power roles. The themes identified from three threads posted on CafeMom indicate three characteristics of note: (1) there is a gap in knowledge for many of these decision-makers that must be bridged if effective health care decisions are to be made, regardless of what that decision ultimately is, (2) the health community at large needs to deepen its understanding of how parents, mothers in particular, share health information about their children in unmonitored online settings, and (3) the health community needs to equip parents and patients to understand how to interpret information given by different sources and introduce basic statistical numeracy to allow for better understanding of measures involving percentiles of populations and other statistical information. It is through the organically created online conversations among mothers that these issues can continue to be explored and expanded to include other health care concerns

Beliefs and Dynamic Consistency,

In this chapter, we adopt the decision theoretic approach to the representation and updating of beliefs. We take up this issue and propose a reconsideration of Hammond's argument. After reviewing the argument more formally, we propose a weaker notion of dynamic consistency. We observe that this notion does not imply the full fledged sure thing principle thus leaving some room for models that are not based on expected utility maximization. However, these models still do not account for ''imprecision averse" behavior such as the one exhibited in Ellsberg experiment and that is captured by non-Bayesian models such as the multiple prior model. We therefore go on with the argument and establish that such non-Bayesian models possess the weak form of dynamic consistency when the information considered consists of a reduction in imprecision (in the Ellsberg example, some information about the proportion of Black and Yellow balls

Classification of pneumonia from X-ray images using siamese convolutional network

Pneumonia is one of the highest global causes of deaths especially for children under 5 years old. This happened mainly because of the difficulties in identifying the cause of pneumonia. As a result, the treatment given may not be suitable for each pneumonia case. Recent studies have used deep learning approaches to obtain better classification within the cause of pneumonia. In this research, we used siamese convolutional network (SCN) to classify chest x-ray pneumonia image into 3 classes, namely normal conditions, bacterial pneumonia, and viral pneumonia. Siamese convolutional network is a neural network architecture that learns similarity knowledge between pairs of image inputs based on the differences between its features. One of the important benefits of classifying data with SCN is the availability of comparable images that can be used as a reference when determining class. Using SCN, our best model achieved 80.03% accuracy, 79.59% f1 score, and an improved result reasoning by providing the comparable images

Learning Possibilistic Logic Theories

Vi tar opp problemet med å lære tolkbare maskinlæringsmodeller fra usikker og manglende informasjon. Vi utvikler først en ny dyplæringsarkitektur, RIDDLE: Rule InDuction with Deep LEarning (regelinduksjon med dyp læring), basert på egenskapene til mulighetsteori. Med eksperimentelle resultater og sammenligning med FURIA, en eksisterende moderne metode for regelinduksjon, er RIDDLE en lovende regelinduksjonsalgoritme for å finne regler fra data. Deretter undersøker vi læringsoppgaven formelt ved å identifisere regler med konfidensgrad knyttet til dem i exact learning-modellen. Vi definerer formelt teoretiske rammer og viser forhold som må holde for å garantere at en læringsalgoritme vil identifisere reglene som holder i et domene. Til slutt utvikler vi en algoritme som lærer regler med tilhørende konfidensverdier i exact learning-modellen. Vi foreslår også en teknikk for å simulere spørringer i exact learning-modellen fra data. Eksperimenter viser oppmuntrende resultater for å lære et sett med regler som tilnærmer reglene som er kodet i data.We address the problem of learning interpretable machine learning models from uncertain and missing information. We first develop a novel deep learning architecture, named RIDDLE (Rule InDuction with Deep LEarning), based on properties of possibility theory. With experimental results and comparison with FURIA, a state of the art method, RIDDLE is a promising rule induction algorithm for finding rules from data. We then formally investigate the learning task of identifying rules with confidence degree associated to them in the exact learning model. We formally define theoretical frameworks and show conditions that must hold to guarantee that a learning algorithm will identify the rules that hold in a domain. Finally, we develop an algorithm that learns rules with associated confidence values in the exact learning model. We also propose a technique to simulate queries in the exact learning model from data. Experiments show encouraging results to learn a set of rules that approximate rules encoded in data.Doktorgradsavhandlin

"If Oswald had not killed Kennedy" – Spohn on Counterfactuals

Wolfgang Spohn's theory of ranking functions is an elegant and powerful theory of the structure and dynamics of doxastic states. In two recent papers, Spohn has applied it to the analysis of conditionals, claiming to have presented a unified account of indicative and subjunctive (counterfactual) conditionals. I argue that his analysis fails to account for counterfactuals that refer to indirect causes. The strategy of taking the transitive closure that Spohn employs in the theory of causation is not available for counterfactuals. I have a close look at Spohn's treatment of the famous Oswald-Kennedy case in order to illustrate my points. I sketch an alternative view that seems to avoid the problems