An instance data repository for the round-robin sports timetabling problem

The sports timetabling problem is a combinatorial optimization problem that consists of creating a timetable that defines against whom, when and where teams play games. This is a complex matter, since real-life sports timetabling applications are typically highly constrained. The vast amount and variety of constraints and the lack of generally accepted benchmark problem instances make that timetable algorithms proposed in the literature are often tested on just one or two specific seasons of the competition under consideration. This is problematic since only a few algorithmic insights are gained. To mitigate this issue, this article provides a problem instance repository containing over 40 different types of instances covering artificial and real-life problem instances. The construction of such a repository is not trivial, since there are dozens of constraints that need to be expressed in a standardized format. For this, our repository relies on RobinX, an XML-supported classification framework. The resulting repository provides a (non-exhaustive) overview of most real-life sports timetabling applications published over the last five decades. For every problem, a short description highlights the most distinguishing characteristics of the problem. The repository is publicly available and will be continuously updated as new instances or better solutions become available

Essays in Econometrics

This dissertation consists of three chapters and is inspired by current economic issues affecting the majority (if not all) of economic agents, such as recession tracking, matching algorithms and, economic inequality. In the first chapter, I measure the uncertainty affecting estimates of economic inequality in the US and investigate how accounting for properly estimated standard errors can affect the results of empirical and structural macroeconomic studies. While focusing on income and wealth shares within the top 10 percent, my results suggest that ignoring uncertainties in estimated shares can lead to statistically imprecise conclusions about the past and current levels of top income and wealth inequality, and therefore, lead to inaccurate predictions and potentially ineffective policy recommendations. In the second chapter, Professor Jean-Francois Richard and I propose a hybrid version of Dynamic Stochastic General Equilibrium models with emphasis on parameter invariance and tracking performance at times of rapid changes (recessions). Our methodology is illustrated by an application to a pilot Real Business Cycle model for the US economy from 1948 to 2019, where we highlight the model’s parameters invariance, tracking, and 1- to 3-step ahead forecasting performance, outperforming those of an unconstrained benchmark Vector AutoRegressive model. In the third and final chapter, Professor Alistair J. Wilson and I analyze an existing matching procedure designed to solve a complex constrained assignment problem for one of the most successful pan-European ventures: the UEFA Champions League. Relying upon a combination of theory, structural estimation, and simulation, we outline a quantitative methodology aimed at assessing a highly transparent (but combinatorically complex) tournament’s assignment procedure and provide evidence that the UEFA assignment rule is effectively a "constrained-best" in terms of pairwise independence

Friends, Cliques and Gifts: Social Proximity and Recognition in Peer-Based Tournament Rituals

Two main accounts of the effect of proximity between candidates competing for recognition and members of the evaluating audience in the underlying social structure can be extrapolated from extant literature on peer-based tournament rituals and cultural fields. Following a Bourdieusian tradition, one account – which we label self-reproduction – insists on the catalyzing effect of social proximity in shaping recognition along relational lines. Drawing from recent scholarship on social evaluation, a second account – which we label intellectual distance – suggests that social proximity deters recognition. We probe the influence of different articulations of social proximity (i.e., direct ties, cliquishness and reciprocity) on recognition by studying awarding decisions within the context of the Norwegian advertising industry. Interviews with key informants and econometric results suggest that, while self-reproduction tends to prevail over intellectual distance, these effects co-exist and their relative influence varies across levels of recognition. We gauge the relative saliency of the two accounts by using a mix-method approach. Important implications for research on social evaluation and recognition in peer-based tournament rituals are drawn

English Premier League scheduling using simulated annealing

This is the first known attempt at scheduling the English Premier League (EPL), which is a NP-hard problem, in the literature. In this research an initial schedule is created using a ‘polygon’ construction method, a method which originates in graph theory. Two distinct simulated annealing metaheuristic solving methodologies are then created to improve this initial schedule. One method is based on a temperature schedule, finite epoch length and reheats while the other is based on a gradually reducing temperature schedule and non-finite epoch length. These two methods were evaluated with respect to solution quality (total penalty), reliability (variation of solution quality over numerous trials) and speed. The official schedule used by the EPL organisers was used for comparison. It was found that the first method produced comparable results, while the second produced improved results. The second method was validated over three seasons and consistently performed well. The findings in this research can be used as the maiden real-world framework and benchmark for the unsolved EPL scheduling problem in the sports scheduling literature

Essays on Asymmetries in Contest

This thesis is concerned with the effects of asymmetries in ability and social preferences in contests and conflict networks. Standard models find that asymmetries monotonically decrease total and individual efforts. I demonstrate that this result does not necessarily hold when players are embedded in complex networks, have preferencesregardingthefairnessofthecontestortheoutcomesofothers, and when real subjects play these games in the lab. Chapter 1 formulates a network of bilateral contests in which locally unique equilibria always exist, and global uniqueness is possible. I find that an increase of one player’s ability can increase her effort and the effort of the entire network. If one player targets a specific opponent, other players follow. Chapter 2 imposes a budget constraint on this model. Most findings are robust to this modelling choice. This allows an investigation of topics like the effects of heterogeneity on team performance and the effect of asymmetries in the number of conflicts a player and her rivals are involved in. Chapter 3 documents that there exists no agreed way for implementing social preferences in contests. I derive four possible versions and critically assess their properties. When costs are considered, the magnitude of predicted overspreading and overbidding is reduced. Mild asymmetry can result in higher effort from the high ability player. In chapter 4, I present a pilot experiment in which social identities, with and without a hierarchy, are induced. We find that identities with such a hierarchy can trigger more aggressive play. To structure these findings, I suggest a foundational model of social preferences that relates them to social identity, where ‘close’ players are treated with altruism and ‘distant’ players are treated with spite

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

Tesis por compendio[ES] La argumentación computacional es el área de investigación que estudia y analiza el uso de distintas técnicas y algoritmos que aproximan el razonamiento argumentativo humano desde un punto de vista computacional. En esta tesis doctoral se estudia el uso de distintas técnicas propuestas bajo el marco de la argumentación computacional para realizar un análisis automático del discurso argumentativo, y para desarrollar técnicas de persuasión computacional basadas en argumentos. Con estos objetivos, en primer lugar se presenta una completa revisión del estado del arte y se propone una clasificación de los trabajos existentes en el área de la argumentación computacional. Esta revisión nos permite contextualizar y entender la investigación previa de forma más clara desde la perspectiva humana del razonamiento argumentativo, así como identificar las principales limitaciones y futuras tendencias de la investigación realizada en argumentación computacional. En segundo lugar, con el objetivo de solucionar algunas de estas limitaciones, se ha creado y descrito un nuevo conjunto de datos que permite abordar nuevos retos y investigar problemas previamente inabordables (e.g., evaluación automática de debates orales). Conjuntamente con estos datos, se propone un nuevo sistema para la extracción automática de argumentos y se realiza el análisis comparativo de distintas técnicas para esta misma tarea. Además, se propone un nuevo algoritmo para la evaluación automática de debates argumentativos y se prueba con debates humanos reales. Finalmente, en tercer lugar se presentan una serie de estudios y propuestas para mejorar la capacidad persuasiva de sistemas de argumentación computacionales en la interacción con usuarios humanos. De esta forma, en esta tesis se presentan avances en cada una de las partes principales del proceso de argumentación computacional (i.e., extracción automática de argumentos, representación del conocimiento y razonamiento basados en argumentos, e interacción humano-computador basada en argumentos), así como se proponen algunos de los cimientos esenciales para el análisis automático completo de discursos argumentativos en lenguaje natural.[CA] L'argumentació computacional és l'àrea de recerca que estudia i analitza l'ús de distintes tècniques i algoritmes que aproximen el raonament argumentatiu humà des d'un punt de vista computacional. En aquesta tesi doctoral s'estudia l'ús de distintes tècniques proposades sota el marc de l'argumentació computacional per a realitzar una anàlisi automàtic del discurs argumentatiu, i per a desenvolupar tècniques de persuasió computacional basades en arguments. Amb aquestos objectius, en primer lloc es presenta una completa revisió de l'estat de l'art i es proposa una classificació dels treballs existents en l'àrea de l'argumentació computacional. Aquesta revisió permet contextualitzar i entendre la investigació previa de forma més clara des de la perspectiva humana del raonament argumentatiu, així com identificar les principals limitacions i futures tendències de la investigació realitzada en argumentació computacional. En segon lloc, amb l'objectiu de sol$\cdot$lucionar algunes d'aquestes limitacions, hem creat i descrit un nou conjunt de dades que ens permet abordar nous reptes i investigar problemes prèviament inabordables (e.g., avaluació automàtica de debats orals). Conjuntament amb aquestes dades, es proposa un nou sistema per a l'extracció d'arguments i es realitza l'anàlisi comparativa de distintes tècniques per a aquesta mateixa tasca. A més a més, es proposa un nou algoritme per a l'avaluació automàtica de debats argumentatius i es prova amb debats humans reals. Finalment, en tercer lloc es presenten una sèrie d'estudis i propostes per a millorar la capacitat persuasiva de sistemes d'argumentació computacionals en la interacció amb usuaris humans. D'aquesta forma, en aquesta tesi es presenten avanços en cada una de les parts principals del procés d'argumentació computacional (i.e., l'extracció automàtica d'arguments, la representació del coneixement i raonament basats en arguments, i la interacció humà-computador basada en arguments), així com es proposen alguns dels fonaments essencials per a l'anàlisi automàtica completa de discursos argumentatius en llenguatge natural.[EN] Computational argumentation is the area of research that studies and analyses the use of different techniques and algorithms that approximate human argumentative reasoning from a computational viewpoint. In this doctoral thesis we study the use of different techniques proposed under the framework of computational argumentation to perform an automatic analysis of argumentative discourse, and to develop argument-based computational persuasion techniques. With these objectives in mind, we first present a complete review of the state of the art and propose a classification of existing works in the area of computational argumentation. This review allows us to contextualise and understand the previous research more clearly from the human perspective of argumentative reasoning, and to identify the main limitations and future trends of the research done in computational argumentation. Secondly, to overcome some of these limitations, we create and describe a new corpus that allows us to address new challenges and investigate on previously unexplored problems (e.g., automatic evaluation of spoken debates). In conjunction with this data, a new system for argument mining is proposed and a comparative analysis of different techniques for this same task is carried out. In addition, we propose a new algorithm for the automatic evaluation of argumentative debates and we evaluate it with real human debates. Thirdly, a series of studies and proposals are presented to improve the persuasiveness of computational argumentation systems in the interaction with human users. In this way, this thesis presents advances in each of the main parts of the computational argumentation process (i.e., argument mining, argument-based knowledge representation and reasoning, and argument-based human-computer interaction), and proposes some of the essential foundations for the complete automatic analysis of natural language argumentative discourses.This thesis has been partially supported by the Generalitat Valenciana project PROME- TEO/2018/002 and by the Spanish Government projects TIN2017-89156-R and PID2020- 113416RB-I00.Ruiz Dolz, R. (2023). Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/194806Compendi

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications