Die Dissertation besteht aus drei unabhängigen Essays zur Anwendung der kooperativen Spieltheorie auf den internationalen Erdgashandel. Das internationale Erdgasnetz wird von einem stilisierten quantitativen Modell dargestellt, welches aus den wichtigsten Produzenten, Verbrauchern, Pipelines und LNG-Terminals besteht. Die Koalitionsfunktion berücksichtigt wesentliche Merkmale des internationalen Gashandels und schildert die Abhängigkeiten zwischen den Akteuren. Das Spiel wird mit dem Shapley-Wert und dem Nukleolus gelöst, welche alternative Indizes für die Darstellung der Machtstruktur sind. Kapitel 1 versucht die Interessen der Spieler in drei Pipeline-Projekte, d.h. Nord Stream, South Stream und Nabucco, zu rationalisieren. Das Kapitel ordnet die Investitionen in die neuen Pipelines ihren Auswirkungen auf die Verhandlungsmacht der Akteure zu und zieht dabei Rückschlüsse auf die Aussagekraft der Lösungskonzepte. Der Shapley-Wert rechtfertigt die jüngsten Investitionen in die Pipelines als rationale Versuche, die Machtstruktur zu verändern. Im Gegensatz zum Shapley-Wert kann der Nukleolus die empirischen Beobachtungen nicht nachbilden. Kapitel 2 studiert Pipeline-Projekte im zentralasiatischen Raum. Die Ergebnisse erklären die Unterstützung der zentralasiatischen Länder für eine nach China verlaufende Pipeline statt einem nach Europa führenden Projekt, und zeigen, dass die Konkurrenz zwischen China und Europa für das zentralasiatische Erdgas vernachlässigbar ist. Kapitel 3 untersucht die bedeutendsten Entwicklungen in Angebot und Nachfrage im globalen LNG-Markt sowie deren Einfluss auf die Machtstruktur im eurasischen Erdgashandel. Die Entwicklungen kommen den europäischen Verbrauchern deutlich zugute, aber scheitern dabei, die Macht der etablierten Anbieter um Europa, beispielsweise Russlands, einzudämmen. / The dissertation contains three independent essays on the application of cooperative game theory on the international natural gas trade. The international natural gas network is represented by a stylized disaggregated quantitative model consisting of its major producers, customers, trunk-pipelines and LNG terminals. The value function considers essential characteristics of the international gas trade and characterizes the interdependencies between the major actors. The game is solved with the Shapley value and the nucleolus as alternative indexes for capturing the power of different players. Chapter 1 aims to rationalize players'' interest in the three pipeline projects, i.e., Nord Stream, South Stream, and Nabucco. The chapter relates the investments into the new pipelines to their impact on the power structure and thereby makes conclusions about the explanatory power of the solution concepts. While the Shapley value can explain the recent investments in the new pipelines as a rational attempt to alter the power structure in the network, the nucleolus, in contrast, fails to replicate the empirical evidence. Chapter 2 studies pipeline options of the Central Asian countries aimed at diversifying their transit routes and export markets. The results explain the Central Asian countries'' endorsement for an eastbound pipeline to China rather than a Europe-bound option, and show negligible demand competition between China and Europe for Central Asian supplies. Chapter 3 investigates major supply and demand developments in the global LNG market as well as their impact on the power structure in the Eurasian natural gas trade. The developments benefit European consumers significantly, but barely curtail the power of the established suppliers in close proximity to Europe, such as Russia.

State-of-the-art Machine Learning (ML) algorithms show impressive results for a myriad of applications. However, they operate as a sort of a black box: the decisions taken are not human-understandable. There is a need for transparency and interpretability of ML predictions to be wider accepted in society, especially in specific fields such as medicine or finance. Most of the efforts so far have focused on explaining supervised learning. This project aims to use some of these successful explainability algorithms and apply them to Reinforcement Learning (RL). To do so, we explain the actions of a RL agent playing Atari’s Breakout game, using two different explainability algorithms: Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). We successfully implement both algorithms, which yield credible and insightful explanations of the mechanics of the agent. However, we think the final presentation of the results is sub-optimal for the final user, as it is not intuitive at first sight. / De senaste algoritmerna för maskininlärning (ML) visar imponerande resultat för en mängd olika tillämpningar. De fungerar dock som ett slags ”svart låda”: de beslut som fattas är inte begripliga för människor. Det finns ett behov av öppenhet och tolkningsbarhet för ML-prognoser för att de ska bli mer accepterade i samhället, särskilt inom specifika områden som medicin och ekonomi. De flesta insatser hittills har fokuserat på att förklara övervakad inlärning. Syftet med detta projekt är att använda några av dessa framgångsrika algoritmer för att förklara och tillämpa dem på förstärkning lärande (Reinforcement Learning, RL). För att göra detta förklarar vi handlingarna hos en RL-agent som spelar Ataris Breakout-spel med hjälp av två olika förklaringsalgoritmer: Shapley Additive Explanations (SHAP) och Local Interpretable Model-agnostic Explanations (LIME). Vi genomför framgångsrikt båda algoritmerna, som ger trovärdiga och insiktsfulla förklaringar av agentens mekanik. Vi anser dock att den slutliga presentationen av resultaten inte är optimal för slutanvändaren, eftersom den inte är intuitiv vid första anblicken. / Els algoritmes d’aprenentatge automàtic (Machine Learning, ML) d’última generació mostren resultats impressionants per a moltes aplicacions. Tot i això, funcionen com una mena de caixa negra: les decisions preses no són comprensibles per a l’ésser humà. Per tal que les prediccion preses mitjançant ML siguin més acceptades a la societat, especialment en camps específics com la medicina o les finances, cal transparència i interpretabilitat. La majoria dels esforços que s’han fet fins ara s’han centrat a explicar l’aprenentatge supervisat (supervised learning). Aquest projecte pretén utilitzar alguns d’aquests existosos algoritmes d’explicabilitat i aplicar-los a l’aprenentatge per reforç (Reinforcement Learning, RL). Per fer-ho, expliquem les accions d’un agent de RL que juga al joc Breakout d’Atari utilitzant dos algoritmes diferents: explicacions additives de Shapley (SHAP) i explicacions model-agnòstiques localment interpretables (LIME). Hem implementat amb èxit tots dos algoritmes, que produeixen explicacions creïbles i interessants de la mecànica de l’agent. Tanmateix, creiem que la presentació final dels resultats no és òptima per a l’usuari final, ja que no és intuïtiva a primera vista.

Trouble and bug reports are essential in software maintenance and for identifying faults—a challenging and time-consuming task. In cases when the fault and reports are similar or identical to previous and already resolved ones, the effort can be reduced significantly making the prospect of automatically detecting duplicates very compelling. In this work, common methods and techniques in the literature are evaluated and compared on domain-specific and multimodal trouble reports from Ericsson software. The number of samples is few, which is a case not so well-studied in the area. On this basis, both traditional and more recent techniques based on deep learning are considered with the goal of accurately detecting duplicates. Firstly, the more traditional approach based on natural language processing and machine learning is evaluated using different vectorization techniques and similarity measures adapted and customized to the domain-specific trouble reports. The multimodality and many fields of the trouble reports call for a wide range of techniques, including term frequency-inverse document frequency, BM25, and latent semantic analysis. A pipeline processing each data field of the trouble reports independently and automatically weighing the importance of each data field is proposed. The best performing model achieves a recall rate of 89% for a duplicate candidate list size of 10. Further, obtaining knowledge on which types of data are most important for duplicate detection is explored through what is known as Shapley values. Results indicate that utilizing all types of data indeed improve performance, and that date and code parameters are strong indicators. Secondly, a Siamese network based on Transformer-encoders is evaluated on data fields believed to have some underlying representation of the semantic meaning or sequentially important information, which a deep model can capture. To alleviate the issues when having few samples, pre-training through automatic data labeling is studied. Results show an increase in performance compared to not pre-training the Siamese network. However, compared to the more traditional model it performs on par, indicating that traditional models may perform equally well when having few samples besides also being simpler, more robust, and faster. / Buggrapporter är kritiska för underhåll av mjukvara och för att identifiera fel — en utmanande och tidskrävande uppgift. I de fall då felet och rapporterna liknar eller är identiska med tidigare och redan lösta ärenden, kan tiden som krävs minskas avsevärt, vilket gör automatiskt detektering av dubbletter mycket önskvärd. I detta arbete utvärderas och jämförs vanliga metoder och tekniker i litteraturen på domänspecifika och multimodala buggrapporter från Ericssons mjukvara. Antalet tillgängliga träningsexempel är få, vilket inte är ett så välstuderat fall. Utifrån detta utvärderas både traditionella samt nyare tekniker baserade på djupinlärning med målet att detektera dubbletter så bra som möjligt. Först utvärderas det mer traditionella tillvägagångssättet baserat på naturlig språkbearbetning och maskininlärning med hjälp av olika vektoriseringstekniker och likhetsmått specialanpassade till buggrapporterna. Multimodaliteten och de många datafälten i buggrapporterna kräver en rad av tekniker, så som termfrekvens-invers dokumentfrekvens, BM25 och latent semantisk analys. I detta arbete föreslås en modell som behandlar varje datafält i buggrapporterna separat och automatiskt sammanväger varje datafälts betydelse. Den bäst presterande modellen uppnår en återkallningsfrekvens på 89% för en lista med 10 dubblettkandidater. Vidare undersöks vilka datafält som är mest viktiga för dubblettdetektering genom Shapley-värden. Resultaten tyder på att utnyttja alla tillgängliga datafält förbättrar prestandan, och att datum och kodparametrar är starka indikatorer. Sedan utvärderas ett siamesiskt nätverk baserat på Transformator-kodare på datafält som tros ha en underliggande representation av semantisk betydelse eller sekventiellt viktig information, vilket en djup modell kan utnyttja. För att lindra de problem som uppstår med få träningssexempel, studeras det hur den djupa modellen kan förtränas genom automatisk datamärkning. Resultaten visar på en ökning i prestanda jämfört med att inte förträna det siamesiska nätverket. Men jämfört med den mer traditionella modellen presterar den likvärdigt, vilket indikerar att mer traditionella modeller kan prestera lika bra när antalet träningsexempel är få, förutom att också vara enklare, mer robusta, och snabbare.

Spinal cord injury, whether traumatic or nontraumatic, can partially or completely damage sensorimotor pathways between the brain and the body, leading to heterogeneous gait abnormalities. Mobility impairments also depend on other factors such as age, weight, time since injury, pain, and walking aids used. The ASIA Impairment Scale is recommended to classify injury severity, but is not designed to characterize individual ambulatory capacity. Other standardized tests based on subjective or timing/distance assessments also have only limited ability to determine an individual's capacity. Data-driven techniques have demonstrated effectiveness in analysing complexity in many domains and may provide additional perspectives on the complexity of gait performance in persons with spinal cord injury. The studies in this thesis aimed to address the complexity of gait and functional abilities after spinal cord injury using data-driven approaches. The aim of the first manuscript was to characterize the heterogeneous gait patterns in persons with incomplete spinal cord injury. Dissimilarities among gait patterns in the study population were quantified with multivariate dynamic time warping. Gait patterns were classified into six distinct clusters using hierarchical agglomerative clustering. Through random forest classifiers with explainable AI, peak ankle plantarflexion during swing was identified as the feature that most often distinguished most clusters from the controls. By combining clinical evaluation with the proposed methods, it was possible to provide comprehensive analyses of the six gait clusters.     The aim of the second manuscript was to quantify sensorimotor effects on walking performance in persons with spinal cord injury. The relationships between 11 input features and 2 walking outcome measures - distance walked in 6 minutes and net energy cost of transport - were captured using 2 Gaussian process regression models. Explainable AI revealed the importance of muscle strength on both outcome measures. Use of walking aids also influenced distance walked, and  cardiovascular capacity influenced energy cost. Analyses for each person also gave useful insights into individual performance.     The findings from these studies demonstrate the large potential of advanced machine learning and explainable AI to address the complexity of gait function in persons with spinal cord injury. / Skador på ryggmärgen, oavsett om de är traumatiska eller icke-traumatiska, kan helt eller delvis skada sensoriska och motoriska banor mellan hjärnan och kroppen, vilket påverkar gången i varierande grad. Rörelsenedsättningen beror också på andra faktorer såsom ålder, vikt, tid sedan skadan uppstod, smärta och gånghjälpmedel. ASIA-skalan används för att klassificera ryggmärgsskadans svårighetsgrad, men är inte utformad för att karaktärisera individens gångförmåga. Andra standardiserade tester baserade på subjektiva eller tids och avståndsbedömningar har också begränsad möjlighet att beskriva individuell kapacitet. Datadrivna metoder är kraftfulla och kan ge ytterligare perspektiv på gångens komplexitet och prestation. Studierna i denna avhandling syftar till att analysera komplexa relationer mellan gång, motoriska samt sensoriska funktion efter ryggmärgsskada med hjälp av datadrivna metoder. Syftet med den första studien är att karaktärisera de heterogena gångmönster hos personer med inkomplett ryggmärgsskada. Multivariat dynamisk tidsförvrägning (eng: Multivariate dynamic time warping) användes för att kvantifiera gångskillnader i studiepopulationen. Hierarkisk agglomerativ klusteranalys (eng: hierarchical agglomerative clustering) delade upp gång i sex distinkta kluster, varav fyra hade lägre hastighet än kontroller. Med hjälp av förklarbara AI (eng: explainable AI) identifierades det att fotledsvinkeln i svingfasen hade störst påverkan om vilken kluster som gångmönstret hamnat i. Genom att kombinera klinisk undersökning med datadrivna metoder kunde vi beskriva en omfattande bild av de sex gångklustren. Syftet med den andra manuskriptet är att kvantifiera sensoriska och motoriska faktorerans påverkan på gångförmåga efter ryggmärgsskada. Med hjälp av två Gaussian process-regressionsmodeller identiferades sambanden mellan 11 beskrivande faktorer och 2 gång prestationsmått, nämligen gångavstånd på 6 minuter samt metabola energiåtgång. Med hjälp av förklarbar AI påvisades det stora påverkan av muskelstyrka på både gångsträckan och energiåtgång. Gånghjälpmedlet samt kardiovaskulär kapaciteten hade också betydande påverkan på gångprestation. Enskilda analyser gav insiktsfull information om varje individ. Resultaten från dessa studier visar på potentiella tillämpningar av avancerad maskininlärning och AI metoder för att analysera komplexa relationer mellan funktion och motorisk prestation efter ryggmärgsskada. / <p>QC 20240221</p>

Computer simulation is a commonly applied technique for studying electronic warfare duels. This thesis aims to apply machine learning techniques to convert simulation output data into knowledge and insights regarding defensive actions for a ship facing multiple hostile missiles. The analysis may support tactical decision-making, hence the interpretability aspect of predictions is necessary to allow for human evaluation and understanding of impacts from the explanatory variables. The final distance for the threats to the target and the probability of the threats hitting the target was modeled using a multi-layer perceptron model with a multi-task approach, including custom loss functions. The results generated in this study show that the selected methodology is more successful than a baseline using regression models. Modeling the outcome with artificial neural networks results in a black box for decision making. Therefore the concept of interpretable machine learning was applied using a post-hoc approach. Given the learned model, the features considered, and the multiple threats, the feature contributions to the model were interpreted using Kernel SHapley Additive exPlanations (SHAP). The method consists of local linear surrogate models for approximating Shapley values. The analysis primarily showed that an increased seeker activation distance was important, and the increased time for defensive actions improved the outcomes. Further, predicting the final distance to the ship at the beginning of a simulation is important and, in general, a guidance of the actual outcome. The action of firing chaff grenades in the tracking gate also had importance. More chaff grenades influenced the missiles' tracking and provided a preferable outcome from the defended ship's point of view.

This dissertation addresses five fundamental resource allocation problems on networks, all of which have applications to support Homeland Security or industry challenges. In the first application, we model and solve the strategic problem of minimizing the expected loss inflicted by a hostile terrorist organization. An appropriate allocation of certain capability-related, intent-related, vulnerability-related, and consequence-related resources is used to reduce the probabilities of success in the respective attack-related actions, and to ameliorate losses in case of a successful attack. Given the disparate nature of prioritizing capital and material investments by federal, state, local, and private agencies to combat terrorism, our model and accompanying solution procedure represent an innovative, comprehensive, and quantitative approach to coordinate resource allocations from various agencies across the breadth of domains that deal with preventing attacks and mitigating their consequences. Adopting a nested event tree optimization framework, we present a novel formulation for the problem as a specially structured nonconvex factorable program, and develop two branch-and-bound schemes based respectively on utilizing a convex nonlinear relaxation and a linear outer-approximation, both of which are proven to converge to a global optimal solution. We also investigate a fundamental special-case variant for each of these schemes, and design an alternative direct mixed-integer programming model representation for this scenario. Several range reduction, partitioning, and branching strategies are proposed, and extensive computational results are presented to study the efficacy of different compositions of these algorithmic ingredients, including comparisons with the commercial software BARON. The developed set of algorithmic implementation strategies and enhancements are shown to outperform BARON over a set of simulated test instances, where the best proposed methodology produces an average optimality gap of 0.35% (compared to 4.29% for BARON) and reduces the required computational effort by a factor of 33. A sensitivity analysis is also conducted to explore the effect of certain key model parameters, whereupon we demonstrate that the prescribed algorithm can attain significantly tighter optimality gaps with only a near-linear corresponding increase in computational effort. In addition to enabling effective comprehensive resource allocations, this research permits coordinating agencies to conduct quantitative what-if studies on the impact of alternative resourcing priorities. The second application is motivated by the author's experience with the U.S. Army during a tour in Iraq, during which combined operations involving U.S. Army, Iraqi Army, and Iraqi Police forces sought to interdict the transport of selected materials used for the manufacture of specialized types of Improvised Explosive Devices, as well as to interdict the distribution of assembled devices to operatives in the field. In this application, we model and solve the problem of minimizing the maximum flow through a network from a given source node to a terminus node, integrating different forms of superadditive synergy with respect to the effect of resources applied to the arcs in the network. Herein, the superadditive synergy reflects the additional effectiveness of forces conducting combined operations, vis-à-vis unilateral efforts. We examine linear, concave, and general nonconcave superadditive synergistic relationships between resources, and accordingly develop and test effective solution procedures for the underlying nonlinear programs. For the linear case, we formulate an alternative model representation via Fourier-Motzkin elimination that reduces average computational effort by over 40% on a set of randomly generated test instances. This test is followed by extensive analyses of instance parameters to determine their effect on the levels of synergy attained using different specified metrics. For the case of concave synergy relationships, which yields a convex program, we design an inner-linearization procedure that attains solutions on average within 3% of optimality with a reduction in computational effort by a factor of 18 in comparison with the commercial codes SBB and BARON for small- and medium-sized problems; and outperforms these softwares on large-sized problems, where both solvers failed to attain an optimal solution (and often failed to detect a feasible solution) within 1800 CPU seconds. Examining a general nonlinear synergy relationship, we develop solution methods based on outer-linearizations, inner-linearizations, and mixed-integer approximations, and compare these against the commercial software BARON. Considering increased granularities for the outer-linearization and mixed-integer approximations, as well as different implementation variants for both these approaches, we conduct extensive computational experiments to reveal that, whereas both these techniques perform comparably with respect to BARON on small-sized problems, they significantly improve upon the performance for medium- and large-sized problems. Our superlative procedure reduces the computational effort by a factor of 461 for the subset of test problems for which the commercial global optimization software BARON could identify a feasible solution, while also achieving solutions of objective value 0.20% better than BARON. The third application is likewise motivated by the author's military experience in Iraq, both from several instances involving coalition forces attempting to interdict the transport of a kidnapping victim by a sectarian militia as well as, from the opposite perspective, instances involving coalition forces transporting detainees between interment facilities. For this application, we examine the network interdiction problem of minimizing the maximum probability of evasion by an entity traversing a network from a given source to a designated terminus, while incorporating novel forms of superadditive synergy between resources applied to arcs in the network. Our formulations examine either linear or concave (nonlinear) synergy relationships. Conformant with military strategies that frequently involve a combination of overt and covert operations to achieve an operational objective, we also propose an alternative model for sequential overt and covert deployment of subsets of interdiction resources, and conduct theoretical as well as empirical comparative analyses between models for purely overt (with or without synergy) and composite overt-covert strategies to provide insights into absolute and relative threshold criteria for recommended resource utilization. In contrast to existing static models, in a fourth application, we present a novel dynamic network interdiction model that improves realism by accounting for interactions between an interdictor deploying resources on arcs in a digraph and an evader traversing the network from a designated source to a known terminus, wherein the agents may modify strategies in selected subsequent periods according to respective decision and implementation cycles. We further enhance the realism of our model by considering a multi-component objective function, wherein the interdictor seeks to minimize the maximum value of a regret function that consists of the evader's net flow from the source to the terminus; the interdictor's procurement, deployment, and redeployment costs; and penalties incurred by the evader for misperceptions as to the interdicted state of the network. For the resulting minimax model, we use duality to develop a reformulation that facilitates a direct solution procedure using the commercial software BARON, and examine certain related stability and convergence issues. We demonstrate cases for convergence to a stable equilibrium of strategies for problem structures having a unique solution to minimize the maximum evader flow, as well as convergence to a region of bounded oscillation for structures yielding alternative interdictor strategies that minimize the maximum evader flow. We also provide insights into the computational performance of BARON for these two problem structures, yielding useful guidelines for other research involving similar non-convex optimization problems. For the fifth application, we examine the problem of apportioning railcars to car manufacturers and railroads participating in a pooling agreement for shipping automobiles, given a dynamically determined total fleet size. This study is motivated by the existence of such a consortium of automobile manufacturers and railroads, for which the collaborative fleet sizing and efforts to equitably allocate railcars amongst the participants are currently orchestrated by the \textit{TTX Company} in Chicago, Illinois. In our study, we first demonstrate potential inequities in the industry standard resulting either from failing to address disconnected transportation network components separately, or from utilizing the current manufacturer allocation technique that is based on average nodal empty transit time estimates. We next propose and illustrate four alternative schemes to apportion railcars to manufacturers, respectively based on total transit time that accounts for queuing; two marginal cost-induced methods; and a Shapley value approach. We also provide a game-theoretic insight into the existing procedure for apportioning railcars to railroads, and develop an alternative railroad allocation scheme based on capital plus operating costs. Extensive computational results are presented for the ten combinations of current and proposed allocation techniques for automobile manufacturers and railroads, using realistic instances derived from representative data of the current business environment. We conclude with recommendations for adopting an appropriate apportionment methodology for implementation by the industry. / Ph. D.

<p dir="ltr">Anomaly detection is an active research field that widely applies to commercial applications to detect unusual patterns or outliers. Time series anomaly detection provides valuable insights into mission and safety-critical applications using ever-growing temporal data, including continuous streaming time series data from the Internet of Things (IoT), sensor networks, healthcare, stock prices, computer metrics, and application monitoring. While Generative Adversarial Networks (GANs) demonstrate promising results in time series anomaly detection, the opaque nature of generative deep learning models lacks explainability and hinders broader adoption. Understanding the rationale behind model predictions and providing human-interpretable explanations are vital for increasing confidence and trust in machine learning (ML) frameworks such as GANs. This study conducted a structured and comprehensive assessment of post-hoc local explainability in GAN-based time series anomaly detection using SHapley Additive exPlanations (SHAP). Using publicly available benchmarking datasets approved by Purdue’s Institutional Review Board (IRB), this study evaluated state-of-the-art GAN frameworks identifying their advantages and limitations for time series anomaly detection. This study demonstrated a systematic approach in quantifying the extent of GAN-based time series anomaly explainability, providing insights for businesses when considering adopting generative deep learning models. The presented results show that GANs capture complex time series temporal distribution and are applicable for anomaly detection. The analysis from this study shows SHAP can identify the significance of contributing features within time series data and derive post-hoc explanations to quantify GAN-detected time series anomalies.</p>

Abstract The aim of this thesis is to devise new paradigms on radio resource sharing including cache-enabled virtualized large cellular networks for mobile network operators (MNOs). Also, self-organizing resource allocation for small cell networks is considered. In such networks, the MNOs rent radio resources from the infrastructure provider (InP) to support their subscribers. In order to reduce the operational costs, while at the same time to significantly increase the usage of the existing network resources, it leads to a paradigm where the MNOs share their infrastructure, i.e., base stations (BSs), antennas, spectrum and edge cache among themselves. In this regard, we integrate the theoretical insights provided by stochastic geometrical approaches to model the spectrum and infrastructure sharing for large cellular networks. In the first part of the thesis, we study the non-orthogonal multi-MNO spectrum allocation problem for small cell networks with the goal of maximizing the overall network throughput, defined as the expected weighted sum rate of the MNOs. Each MNO is assumed to serve multiple small cell BSs (SBSs). We adopt the many-to-one stable matching game framework to tackle this problem. We also investigate the role of power allocation schemes for SBSs using Q-learning. In the second part, we model and analyze the infrastructure sharing system considering a single buyer MNO and multiple seller MNOs. The MNOs are assumed to operate over their own licensed spectrum bands while sharing BSs. We assume that multiple seller MNOs compete with each other to sell their infrastructure to a potential buyer MNO. The optimal strategy for the seller MNOs in terms of the fraction of infrastructure to be shared and the price of the infrastructure, is obtained by computing the equilibrium of a Cournot-Nash oligopoly game. Finally, we develop a game-theoretic framework to model and analyze a cache-enabled virtualized cellular networks where the network infrastructure, e.g., BSs and cache storage, owned by an InP, is rented and shared among multiple MNOs. We formulate a Stackelberg game model with the InP as the leader and the MNOs as the followers. The InP tries to maximize its profit by optimizing its infrastructure rental fee. The MNO aims to minimize the cost of infrastructure by minimizing the cache intensity under probabilistic delay constraint of the user (UE). Since the MNOs share their rented infrastructure, we apply a cooperative game concept, namely, the Shapley value, to divide the cost among the MNOs. / Tiivistelmä Tämän väitöskirjan tavoitteena on tuottaa uusia paradigmoja radioresurssien jakoon, mukaan lukien virtualisoidut välimuisti-kykenevät suuret matkapuhelinverkot matkapuhelinoperaattoreille. Näiden kaltaisissa verkoissa operaattorit vuokraavat radioresursseja infrastruktuuritoimittajalta (InP, infrastructure provider) asiakkaiden tarpeisiin. Toimintakulujen karsiminen ja samanaikainen olemassa olevien verkkoresurssien hyötykäytön huomattava kasvattaminen johtaa paradigmaan, jossa operaattorit jakavat infrastruktuurinsa keskenään. Tämän vuoksi työssä tutkitaan teoreettisia stokastiseen geometriaan perustuvia malleja spektrin ja infrastruktuurin jakamiseksi suurissa soluverkoissa. Työn ensimmäisessä osassa tutkitaan ei-ortogonaalista monioperaattori-allokaatioongelmaa pienissä soluverkoissa tavoitteena maksimoida verkon yleistä läpisyöttöä, joka määritellään operaattoreiden painotettuna summaläpisyötön odotusarvona. Jokaisen operaattorin oletetaan palvelevan useampaa piensolutukiasemaa (SBS, small cell base station). Työssä käytetään monelta yhdelle -vakaata sovituspeli-viitekehystä SBS:lle käyttäen Q-oppimista. Työn toisessa osassa mallinnetaan ja analysoidaan infrastruktuurin jakamista yhden ostaja-operaattorin ja monen myyjä-operaattorin tapauksessa. Operaattorien oletetaan toimivan omilla lisensoiduilla taajuuksillaan jakaen tukiasemat keskenään. Myyjän optimaalinen strategia infrastruktuurin myytävän osan suuruuden ja hinnan suhteen saavutetaan laskemalla Cournot-Nash -olipologipelin tasapainotila. Lopuksi, työssä kehitetään peli-teoreettinen viitekehys virtualisoitujen välimuistikykenevien soluverkkojen mallintamiseen ja analysointiin, missä InP:n omistama verkkoinfrastruktuuri vuokrataan ja jaetaan monen operaattorin kesken. Työssä muodostetaan Stackelberg-pelimalli, jossa InP toimii johtajana ja operaattorit seuraajina. InP pyrkii maksimoimaan voittonsa optimoimalla infrastruktuurin vuokrahintaa. Operaattori pyrkii minimoimaan infrastruktuurin hinnan minimoimalla välimuistin tiheyttä satunnaisen käyttäjän viive-ehtojen mukaisesti. Koska operaattorit jakavat vuokratun infrastruktuurin, työssä käytetään yhteistyöpeli-ajatusta, nimellisesti, Shapleyn arvoa, jakamaan kustannuksia operaatoreiden kesken.

Chapter 1: The Impact of Culture on the Second-Generation Immigrants’ Level of Trust in Canada Trust is one of the main elements of social capital; it determines the extent to which an individual cooperates with others. In this chapter, I assess whether cultural factors influence the level of trust in the population of second-generation immigrants in Canada. This paper is related to two strands of empirical literature. The first analyses the determinants of trust and the second studies the cultural transmission of values, attitudes and beliefs. I follow closely the literature on the cultural transmission and use an epidemiological approach to assess whether trust of second-generation immigrants is affected by their cultural heritage. This approach consists of comparing information about the outcomes of second-generation immigrants with that of the country of origin of their ancestry. We apply this approach using the Ethnic Diversity Survey (EDS), the World Value Survey (WVS) and the European Value Survey (EVS). Estimation results show that the average level of trust in the countries of origin of the ancestors of the second-generation immigrants has a strong significant impact on their level of trust. Thus, individual whose country of ancestry displays a high level of trust, tend to have a high level of trust. This provides evidence that individuals’ level of trust is not only explained by their personal experiences, characteristics, and the environment in which they live; but also by the culture in their country of ancestry. This means that culture does matter! I find that the results remain robust even if certain key countries are omitted or a different data set is used. Chapter 2: Decomposing Health Achievement and Socioeconomic Health Inequalities in Presence of Multiple Categorical Information This chapter presents a decomposition of the health achievement and the socioeconomic health inequality indices by multiple categorical variables and by regions. I adopt Makdissi and Yazbeck's (2014) counting approach to deal with the ordinal nature of the data of the United States National Health Interview Survey 2010. The findings suggest that the attributes that contribute the most to the deviation from perfect health in the United States are: anxiety, depression and exhaustion. Also, I find that the attributes that contribute the most to the total socioeconomic health inequality are ambulation, depression and pain. The regional decomposition results suggest that, if the aversion to socioeconomic health inequality is high enough, socioeconomic health inequalities between regions are the main contributors to the total socioeconomic health inequality in the United States. Chapter 3: Accounting for Freedom and Economic Resources in the Assessment of Changes in Women Poverty in Sub-Saharan Africa This chapter assesses the importance of freedom in women’s wellbeing in twelve Sub-Saharan Africa countries by using data from Demographic Health Surveys. This paper presents a poverty comparison by using the stochastic dominance approach and relies on the economic resources and freedom as the two aspects of wellbeing which evokes the multidimensionality of poverty. This study is related to the following three pieces of literature: the sequential stochastic dominance, the multidimensional poverty, the Sen’s capability approach which is based on freedom. This paper is built on Makdissi et al. (2014) but differs from it in a number of respects. First, it focuses on poverty instead of welfare. Secondly, it applies the Shapley decomposition to determine the contributions of the economic resource distribution and the incidence of the threat of domestic violence to poverty changes over time. Consistent with previous work on the importance of freedom, I find that more freedom, i.e. less threat of domestic violence, affects women’s wellbeing positively since it decreases women’s poverty. The results indicate that women’s wellbeing has improved in Burkina Faso, Ghana, Kenya, Lesotho, Madagascar, Malawi, Rwanda, Senegal, and Zimbabwe and deteriorated in Ethiopia, Nigeria and Tanzania.

This work concentrates on the study of mathematical models of human behaviour in dynamic games; in particular games with aspects of cooperation, implementation of a model and experimentation with the model. The game DarkElf was chosen for this project. It is a strategic, turn based game with economic and military features, where the decisions made by players are simultaneously implemented at a predetermined time.