Generalizations and Applications of the Stochastic Block Model to Basketball Games and Variable Selection Problems

Over the past decade, there has been an explosion of network data in a vast number of circumstances, such as the World Wide Web, social networks, gene interactions, economic networks, etc. Scientific analysis of networks is of significant importance in revealing laws governing complex systems. Community detection, one of the fundamental problems in network analysis, discovers the underlying cluster structure of nodes in a network. The Stochastic Block Model (SBM) is an influential framework for model-based community detection. In this thesis, we first propose a Continuous-time Stochastic Block Model (CSBM). Furthermore, we develop a multistate CSBM and use it to analyze Basketball games. Finally, we propose a novel variable selection approach by constructing networks among variables and applying SBM techniques. Various Stochastic Block Models have been developed for static networks, such as a network of Facebook users. Theoretical properties of these models have been studied recently. However, for transactional networks, for example, a network of email users who frequently send emails to each other, research is relatively limited. Most existing works either do not take time into account or treat time in a discrete manner (as in discrete-time Markov chains). In contrast, we propose a Continuous-time Stochastic Block Model (CSBM) for transactional networks. Transactions between pairs of nodes are modeled as inhomogeneous Poisson processes, where the rate function of each Poisson process is characterized by the underlying cluster labels of the corresponding pair of nodes. The CSBM is capable of not only detecting communities but also capturing how transaction patterns evolve among communities. As an important application, a multistate CSBM is developed and applied to basketball games. Basketball data analysis has gained enormous attention from enthusiasts and professionals from various fields. We advocate that basketball games can be analyzed as transactional networks. The multistate CSBM models basketball plays as continuous-time Markov chains. The model clusters players according to their playing styles and performance. It also provides cluster-specific estimates of the effectiveness of players at scoring, rebounding, stealing, etc, and also captures player interaction patterns within and between clusters. Moreover, the model reveals subtle differences in the offensive strategies of different teams. Applications to NBA basketball games illustrate the performance of the multistate CSBM. The SBM framework can also be employed for variable selection. In the past two decades, variable selection has become one of the central topics in statistical learning and high-dimensional data analysis. Numerous methods have been successfully developed. In general, there are mainly three types of approaches: penalized likelihood methods, variable screening methods and Bayesian variable selection methods. Nevertheless, in this thesis, we propose a novel variable selection method: Variable Selection Networks, which is in a new framework --- Variable Selection Ensembles. Given a regression model with p covariates, we consider the ensemble of all p(p-1)/2 submodels with two covariates. We construct networks with nodes being the p variables and each edge being a measure of the importance of the pair of variables to which it connects. We show that such networks have block structures. Variable selection is conducted by applying SBM techniques to these networks

Cognitive finance: Behavioural strategies of spending, saving, and investing.

Research in economics is increasingly open to empirical results. The advances in behavioural approaches are expanded here by applying cognitive methods to financial questions. The field of "cognitive finance" is approached by the exploration of decision strategies in the financial settings of spending, saving, and investing. Individual strategies in these different domains are searched for and elaborated to derive explanations for observed irregularities in financial decision making. Strong context-dependency and adaptive learning form the basis for this cognition-based approach to finance. Experiments, ratings, and real world data analysis are carried out in specific financial settings, combining different research methods to improve the understanding of natural financial behaviour. People use various strategies in the domains of spending, saving, and investing. Specific spending profiles can be elaborated for a better understanding of individual spending differences. It was found that people differ along four dimensions of spending, which can be labelled: General Leisure, Regular Maintenance, Risk Orientation, and Future Orientation. Saving behaviour is strongly dependent on how people mentally structure their finance and on their self-control attitude towards decision space restrictions, environmental cues, and contingency structures. Investment strategies depend on how companies, in which investments are placed, are evaluated on factors such as Honesty, Prestige, Innovation, and Power. Further on, different information integration strategies can be learned in decision situations with direct feedback. The mapping of cognitive processes in financial decision making is discussed and adaptive learning mechanisms are proposed for the observed behavioural differences. The construal of a "financial personality" is proposed in accordance with other dimensions of personality measures, to better acknowledge and predict variations in financial behaviour. This perspective enriches economic theories and provides a useful ground for improving individual financial services

Markov and Semi-markov Chains, Processes, Systems and Emerging Related Fields

This book covers a broad range of research results in the field of Markov and Semi-Markov chains, processes, systems and related emerging fields. The authors of the included research papers are well-known researchers in their field. The book presents the state-of-the-art and ideas for further research for theorists in the fields. Nonetheless, it also provides straightforwardly applicable results for diverse areas of practitioners

Multi-agent network games with applications in smart electric mobility

The growing complexity and globalization of modern society brought to light novel problems and challenges for researchers that aim to model real-life phenomena. Nowadays communities and even single individuals cannot be considered as a closed system, since one's actions create a ripple effect that ends up influencing the action of others. Therefore, the study of decision-making processes over networks became a pivotal topic in the research community. The possible applications are virtually endless and span into many different fields. Two of the most relevant examples are smart mobility and energy management in highly populated cities, where a collection of (partially) noncooperative individuals interact over a network trying to reach an efficient equilibrium point, in the sense of Nash, and share limited resources due to the environment in which they operate. In this work, we approach these problems through the lens of game theory. We use different declinations of this powerful mathematical tool to study several aspects of these themes. We design decentralized iterative algorithms solving generalized network games that generate behavioral rules for the players that, if followed, ensure global convergence. Then, we question the classical assumption of perfect players’ rationality by introducing novel dynamics to model partial rationality and analyzing their properties. We conclude by focusing on the design of optimal policies to regulate smart mobility and energy management. In this case, we create a detailed and more realistic description of the problem and use a nudging mechanism, implemented by means of a semi-decentralized algorithm, to align the users' behavior with the one desired by the policymaker

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic