Fuzzy Markovian Bonus-Malus Systems in Non-Life Insurance

Markov chains (MCs) are widely used to model a great deal of financial and actuarial problems. Likewise, they are also used in many other fields ranging from economics, management, agricultural sciences, engineering or informatics to medicine. This paper focuses on the use of MCs for the design of non-life bonus-malus systems (BMSs). It proposes quantifying the uncertainty of transition probabilities in BMSs by using fuzzy numbers (FNs). To do so, Fuzzy MCs (FMCs) as defined by Buckley and Eslami in 2002 are used, thus giving rise to the concept of Fuzzy BMSs (FBMSs). More concretely, we describe in detail the common BMS where the number of claims follows a Poisson distribution under the hypothesis that its characteristic parameter is not a real but a triangular FN (TFN). Moreover, we reflect on how to fit that parameter by using several fuzzy data analysis tools and discuss the goodness of triangular approximates to fuzzy transition probabilities, the fuzzy stationary state, and the fuzzy mean asymptotic premium. The use of FMCs in a BMS allows obtaining not only point estimates of all these variables, but also a structured set of their possible values whose reliability is given by means of a possibility measure. Although our analysis is circumscribed to non-life insurance, all of its findings can easily be extended to any of the abovementioned fields with slight modifications.University of Barcelon

Forgetting as a way to avoid deception in a repeated imitation game

Adversarial decision making is aimed at determining optimal decision strategies to deal with an adaptive opponent. A clear example of such situation is the repeated imitation game presented here. Two agents compete in an adversarial model where one agent wants to learn how to imitate the actions taken by the other agent by means of the observation and memorization of the past actions. One defense against this adversary is to make decisions that are intended to confuse him. To achieve this, randomized strategies that change along time for one of the agents are proposed and their performance is analysed from both a theoretical and empirical point of view. We also study the ability of the imitator to avoid deception and adapt to a new behaviour by forgetting the oldest observations. The results confirm that wrong assumptions about the imitator’s behaviour lead to dramatic losses due to a failure in causing deception.Grupo de investigación TIC-169: Modelos de Decisión y Optimización (MODO

Adversarial decision and optimization-based models

Decision making is all around us. Everyone makes choices everyday, from the moment we open our eyes in the morning. Some of them do not have very important consequences in our life and these consequences are easy to take into account. However, in the business world, managers make decisions that have important consequences on the future of their own firm (in terms of revenues, market position, business policy) and their employees. In these cases, it is difficult to account for all the possible alternatives and consequences and to quantify them. Decision making tools such as Decision Analysis are required in order to determine the optimal decision. Furthermore, when several competing agents are involved in a decision making situation and their combination of actions affect each other’s revenues, the problem becomes even more complicated. The way an agent makes a decision and the tools required to determine the optimal decision change. When we are aware of someone observing and reacting to our behavior, one might occasionally prefer a sub-optimal choice aimed at causing confusion on the adversary, so that it will be more difficult for him to guess our decision in future encounters, which may report us a larger benefit. This situation arises in counter-terrorist combat, terrorism prevention, military domains, homeland security, computer games, intelligent training systems, economic adversarial domains, and more. In simple terms, we define an adversary as an entity whose aims are somehow inversely related to ours, and who may influence the profits we obtain from our decisions by taking his/her own actions. This kind of competitive interaction between two agents fits a variety of complex situations which can be analyzed with a number of techniques ranging from Knowledge Engineering and Artificial Intelligence (agent-based modeling, tree exploration, machine learning) to Operational Research, with an emphasis on Game Theory. The objective of this thesis is the analysis and design of adversarial decision and optimization-based models which are able to represent adversarial situations. We are going to conduct theoretical studies and propose practical applications including imitation games, security games and patrolling domains. More precisely, we first study a two-agent imitation game in which one of the agents does not know the motivation of the other, and tries to predict his decisions when repeatedly engaging in a conflict situation, by observing and annotating the past decisions. We propose randomized strategies for the agents and study their performance from a theoretical and empirical point of view. Several variants of this situation are analyzed. Then we move on to practical applications. We address the problem of extracting more useful information from observations of a randomized Markovian strategy that arises when solving a patrolling model, and propose a mathematical procedure based on fuzzy sets and fuzzy numbers for which we provide a ready-to-use implementation in an R package. Finally, we develop an application of adversarial reasoning to the problem of patrolling an area using an autonomous aerial vehicle to protect it against terrestrial intruders, and solve it using a mix of game-theoretic techniques and metaheuristics.Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia ArtificialEsta tesis ha sido financiada parcialmente por los proyectos P07-TIC-02970 y P11-TIC-8001 de la Junta de Andalucía, TIN2011-27696-C02-01 del Ministerio de Economía y Competitividad, GENIL-PYR-2014-9 del CEI-BioTIC (Universidad de Granada), TIN2008-01948 y TIN2008-06872-C04-04 del Ministerio de Ciencia e Innovación, y por la beca FPU referencia AP-2010-4738 del Ministerio de Educación

SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R

The problem of comparing a new solution method against existing ones to find statistically significant differences arises very often in sciences and engineering. When the problem instance being solved is defined by several parameters, assessing a number of methods with respect to many problem configurations simultaneously becomes a hard task. Some visualization technique is required for presenting a large number of statistical significance results in an easily interpretable way. Here we review an existing color-based approach called Statistical Ranking Color Scheme (SRCS) for displaying the results of multiple pairwise statistical comparisons between several methods assessed separately on a number of problem configurations. We introduce an R package implementing SRCS, which performs all the pairwise statistical tests from user data and generates customizable plots. We demonstrate its applicability on two examples from the areas of dynamic optimization and machine learning, in which several algorithms are compared on many problem instances, each defined by a combination of parametersDepartment of Computer Science and Artificial Intelligence, Universidad de Granad

Formin Homology 2 Domain Containing 3 (FHOD3) Is a Genetic Basis for Hypertrophic Cardiomyopathy.

The genetic cause of hypertrophic cardiomyopathy remains unexplained in a substantial proportion of cases. Formin homology 2 domain containing 3 (FHOD3) may have a role in the pathogenesis of cardiac hypertrophy but has not been implicated in hypertrophic cardiomyopathy. This study sought to investigate the relation between FHOD3 mutations and the development of hypertrophic cardiomyopathy. FHOD3 was sequenced by massive parallel sequencing in 3,189 hypertrophic cardiomyopathy unrelated probands and 2,777 patients with no evidence of cardiomyopathy (disease control subjects). The authors evaluated protein-altering candidate variants in FHOD3 for cosegregation, clinical characteristics, and outcomes. The authors identified 94 candidate variants in 132 probands. The variants' frequencies were significantly higher in patients with hypertrophic cardiomyopathy (74 of 3,189 [2.32%]) than in disease control subjects (18 of 2,777 [0.65%]; p  FHOD3 is a novel disease gene in hypertrophic cardiomyopathy, accounting for approximately 1% to 2% of cases. The phenotype and the rate of cardiovascular events are similar to those reported in unselected cohorts. The FHOD3 gene should be routinely included in hypertrophic cardiomyopathy genetic testing panels