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<p>Temporal point processes (TPP) are mathematical approaches for modeling asynchronous event sequences by considering the temporal dependency of each event on past events and its instantaneous rate. Temporal point processes can model various problems, from earthquake aftershocks, trade orders, gang violence, and reported crime patterns, to network analysis, infectious disease transmissions, and virus spread forecasting. In each of these cases, the entity's behavior with the corresponding information is noted over time as an asynchronous event sequence, and the analysis is done using temporal point processes, which provides a means to define the generative mechanism of the sequence of events and ultimately predict events and investigate causality.</p> <p><br></p> <p>Among point processes, Hawkes process as a stochastic point process is able to model a wide range of contagious and self-exciting patterns. One of Hawkes process's well-known applications is predicting the evolution of viral processes on networks, which is an important problem in biology, the social sciences, and the study of the Internet. In existing works, mean-field analysis based upon degree distribution is used to predict viral spreading across networks of different types. However, it has been shown that degree distribution alone fails to predict the behavior of viruses on some real-world networks. Recent attempts have been made to use assortativity to address this shortcoming. This thesis illustrates how the evolution of such a viral process is sensitive to the underlying network's structure. </p> <p><br></p> <p>In Chapter 3, we show that adding assortativity does not fully explain the variance in the spread of viruses for a number of real-world networks. We propose using the graphlet frequency distribution combined with assortativity to explain variations in the evolution of viral processes across networks with identical degree distribution. Using a data-driven approach, by coupling predictive modeling with viral process simulation on real-world networks, we show that simple regression models based on graphlet frequency distribution can explain over 95\% of the variance in virality on networks with the same degree distribution but different network topologies. Our results highlight the importance of graphlets and identify a small collection of graphlets that may have the most significant influence over the viral processes on a network.</p> <p><br></p> <p>Due to the flexibility and expressiveness of deep learning techniques, several neural network-based approaches have recently shown promise for modeling point process intensities. However, there is a lack of research on the possible adversarial attacks and the robustness of such models regarding adversarial attacks and natural shocks to systems. Furthermore, while neural point processes may outperform simpler parametric models on in-sample tests, how these models perform when encountering adversarial examples or sharp non-stationary trends remains unknown. </p> <p><br></p> <p>In Chapter 4, we propose several white-box and black-box adversarial attacks against deep temporal point processes. Additionally, we investigate the transferability of white-box adversarial attacks against point processes modeled by deep neural networks, which are considered a more elevated risk. Extensive experiments confirm that neural point processes are vulnerable to adversarial attacks. Such a vulnerability is illustrated both in terms of predictive metrics and the effect of attacks on the underlying point process's parameters. Expressly, adversarial attacks successfully transform the temporal Hawkes process regime from sub-critical to into a super-critical and manipulate the modeled parameters that is considered a risk against parametric modeling approaches. Additionally, we evaluate the vulnerability and performance of these models in the presence of non-stationary abrupt changes, using the crimes and Covid-19 pandemic dataset as an example.</p> <p><br></p> <p> Considering the security vulnerability of deep-learning models, including deep temporal point processes, to adversarial attacks, it is essential to ensure the robustness of the deployed algorithms that is despite the success of deep learning techniques in modeling temporal point processes.</p> <p> </p> <p>In Chapter 5, we study the robustness of deep temporal point processes against several proposed adversarial attacks from the adversarial defense viewpoint. Specifically, we investigate the effectiveness of adversarial training using universal adversarial samples in improving the robustness of the deep point processes. Additionally, we propose a general point process domain-adopted (GPDA) regularization, which is strictly applicable to temporal point processes, to reduce the effect of adversarial attacks and acquire an empirically robust model. In this approach, unlike other computationally expensive approaches, there is no need for additional back-propagation in the training step, and no further network is required. Ultimately, we propose an adversarial detection framework that has been trained in the Generative Adversarial Network (GAN) manner and solely on clean training data. </p> <p><br></p> <p>Finally, in Chapter 6, we discuss implications of the research and future research directions.</p>

The research question that moves the present work is whether and how competition law shall play a role in making our society more ‘sustainable’, intending this term in a broad meaning, and therefore linked to social, economic and environmental sustainability. The question raises from the awareness of the problems that are affecting our society, also if we refer at its democratic foundations. In particular, we considered that issues such as increasing income inequalities, raising market concentration rates and the even faster climate changes are topics that cannot be outside the academic analysis of the various policies. Therefore, if we try to answer at the question if competition law shall play a role in this context, the analysis should start from the very foundations of this discipline. At this purpose, in our research, we scrutinised how the most representative competition law regimes in the world - i.e., the US antitrust law and the EU competition law systems – developed during their history. This analysis is conducted by reading through legislative sources, policy statements, judicial decisions and scholar works. What emerges is that competition law shall not only be focused on mere economic and econometric objectives, such economic efficiency, but it was intended more as a structural instrument, created for preventing the concentration of an excessive degree of economic power on the same subject or on a bounce of entities. Therefore, after having affirmed this structuralist aim of competition law, it is possible to understand how every other objective shall be considered as a by-product of a healthy competitive process, and not as an end of competition law in itself. This is particularly clear in the European context, as competition law ought not to be intended as a separate or lone subject, but as a field of law well rooted into the EU and its Member States’ constitutional traditions. After having established that competition law shall play a role in the transition towards a more sustainable society, the focus moves on how this task shall be performed. For this purpose, the present research scrutinised the issues we mentioned before, by making a comparative analysis between the EU and the U.S. competition law and antitrust models and, inside the EU environment, among the various solutions adopted in the Member States. This analysis first needed to be carried out by means of an empirical assessment of the issue at stake, especially from an economic standpoint. Then, the legal tools needed in order to reach the desired outcomes were scrutinised, first by making reference to the solutions already adopted by enforcers and Courts on the basis of the existing rules, and, subsequently, new tools are analysed and proposed. In particular, the research establishes a connection between income inequalities and the increasing rates of market concentration. The latter dynamic was deemed particularly intense in the digital market context, which are characterised by market dynamics which escape from the common understanding of competition, as they lead the market to tip in favour of a firm, usually the first mover. In a nutshell, they are characterised by a sort of winner takes all structure. This field represents the core of this research, as it is where excessive market concentration shows most its detrimental effects and the need to a structuralist approach to competition law appears much needed. Therefore, this work aims to provide its contribution to the very active academic debate on this field. However, this research does not want to be limited to the digital market problem but is directed at casting lights on the need for a multi-value approach to competition law at 360 degrees, which can turn into a multi-tool enforcement to better tailor the application of competition rules to all the analysed issues, which are however interrelated thanks to the broad concept of ‘sustainability’ outlined above, in line with the Brundtland Report on sustainability issued in 1987 by the World Commission on Environment and Development. What emerges is that competition law ought to play a role in the transition towards a more sustainable economy and society. This depends on policy choices, and this work is aimed – in the realm of the current scholar debate on this topic – at providing its constructive contribution. However, what is important to affirm is that policy choices directed at establishing the multi-value and multi-tool competition law described above are not only based on progressive or hipster academic ideas, but they are deeply rooted into our societies’ constitutional traditions, and, in the end, in a healthy conception of the liberal economy itself.

Rainfed agriculture is inherently risky, with climate change expected to intensify its variability. In the West African Sahel, where agriculture is crucial not only for subsistence but for national and household incomes through cotton production, the need to safeguard farmers' livelihoods against risk is essential. Formal crop insurance providers in such contexts cannot easily rely on traditional models, where indemnifications are based on realized losses, and have instead proposed a stream of index-based insurance products which indemnify clients based on a predefined, and yet objective parameter (the index). One promising product for Burkinabe cotton farmers is, the Double-Trigger Index-Based Insurance (2TIC), whose two-tier triggering mechanism has the potential of reducing moral hazard and minimizing basis risk. This dissertation uses three essays to consider a farmer-centric approach to assessing the implications of this double trigger mechanism for index-based insurance. The first essay explores cotton farmers' judgments of fairness vis-à-vis the 2TIC indemnification system by using Principal Component Analysis (PCA) and Logistic Regression Analyses, and examines if and how these judgments affect decisions to subscribe. The second essay assesses the impact of 2TIC on farmers' cotton-derived net income by employing Coarsened Exact Matching (CEM). The third essay compares the actuarially fair premium of the 2TIC with the commercial premium paid by cotton farmers, by using statistical approaches. The study provides important evidence-based insights into how 2TIC can be improved and promoted by incorporating farmers' needs and perspectives.

Artificiell intelligens har under senare år tagit en allt större roll inom företagsvärlden. Organisationer kan utnyttja AI till att effektivisera rekryteringsprocessen vilket sparar tid och pengar. Studier visar att träning av AI kan leda till diskriminering. Detta är ofta en sidoeffekt till hur AI har tränats, exempelvis genom data innehållandes bias, eller att programmerarens egna fördomar blir del av AI:s beslutsunderlag. Syfte: Syftet med den här studien är att genom en systematisk litteraturstudie skapa en inblick i de konsekvenser som användandet av AI kan ha på rekryteringsprocessen. Rapporten ämnar även att undersöka hur användandet av AI kan kopplas till diskriminering av kandidater. Avslutningsvis önskar studien att ge rekommendationer till hur AI i fortsättningen kan användas i rekryteringsprocessen utan att åsamka skada på organisationer eller individer. Resultat: Resultatet tyder på att rekryterare och kandidater upplever AI som bias. Resultaten visar även vikten av transparens och det fortsatta behovet av människor i rekryteringsprocessen, för att öka förtroendet och undvika diskriminering och partiskt beslutsfattande från AI. Dessutom visar studien att kandidater uppfattar att AI inte kan identifiera unika drag och personliga ansträngningar från kandidater, och därför anser tekniken som orättvis. Den här studien observerar en polariserad syn på AI som å ena sidan mer partisk än människor, och å andra sidan mindre partisk än människor. Detta förmår författarna till den här rapporten att diskutera komplexiteten i ämnet, och att AI liksom människor, kan orsaka partiskhet. Slutsats: Kandidater och rekryterare anser att AI i rekrytering är fördomsfullt. Uppfattningen påverkas av individuella aspekter. AI i rekrytering kan leda till diskriminering av ålder, kön och etnicitet, diskrimineringen kan ofta härledas till hur AI har tränats. AI kan användas i de delar av rekrytering som historiskt inneburit lite kontakt mellan organisation och kandidat, tillexempel CV granskning. Mänsklig närvaro behövs för att öka förtroende hos kandidater. / Artificial intelligence has during recent years taken a significant place in the business world. Organisations benefit from AI by streamlining the recruitment process and thus saving money by reducing cost and time. Studies show how training of AI can cause discrimination. This is often a side effect of how the AI has been trained, using data containing bias, or due to the programmers personal bias being incorporated in the AI decision making. Purpose: The purpose of this study is to create insight into the consequences that the use of AI can have on the recruitment process. Furthermore, this study aims to examine how AI can be linked to discrimination against candidates. Lastly, this study wishes to give recommendations on how AI can be used for recruitment in the future, without causing harm to individuals or organisations. This study has analysed contemporary management research concerning the area using a systematic literature review. Results: The literature suggests that AI is perceived by recruiters and candidates as biassed. The results show the importance of transparency and the continued need for humans in the process, to enhance trust and avoid discrimination and biassed decision making from AI. Furthermore, this study shows that candidates perceive that AI is unable to identify uniqueness and personal efforts and thus believe the technology to be unfair. This study observes a polarised view of AI as more biassed than humans on one hand, and less biassed than humans on the other. This induces the authors of this paper to discuss the complexity of the matter and that AI, like humans, can cause bias. Conclusion: Candidates and recruiters believe that AI in recruitment is biassed. The perception is influenced by individual aspects. AI in recruitment can lead to discrimination of age, gender and ethnicity, the discrimination can often be traced to how the AI has been trained. AI can be used in those parts of recruitment that historically involved little contact between organisation and candidate, such as CV scanning. Human presence is needed to increase trust in candidates.

We conducted experiments on norm focusing. The tests were carried out with two versions of dictator games: in one version of the game, the dictator had to allocate a gain of e10, while in the other version, a loss of e−10 needs to be allocated. In a first treatment, we focused subjects on the average giving in similar previous dictator games. The second treatment focused subjects on the behaviour of what a self-interested actor should do. In total, N = 550 participants took part in our experiments. We found (1) a significant difference in giving behaviour between gain and loss treatments, with subjects being moderately more self-interested in the loss domain, (2) a significant effect of focusing subjects on the average behaviour of others, but (3) no effect of focusing subjects on the behaviour of self-interested actors.

The last decade has brought with it a wave of innovative technology, shifting the channels through which creative content is created, consumed, and categorized. And, as our interactions with creative multimedia content shift towards online platforms, the sheer quantity of content on these platforms has necessitated the integration of algorithmic guidance in the discovery of these spaces. In this way, the recommendation algorithms that guide users' interactions with various art forms have been cast into the role of gatekeepers and begun to play an increasingly influential role in shaping the creation of artistic content. The work laid out in the following chapters fuses three major areas of research: graph representation learning, music information retrieval, and fairness as applied to the task of music recommendation. In recent years, graph neural networks (GNNs), a powerful new architecture which enables deep learning approaches to be applied to graph or network structures, have proven incredibly influential in the music recommendation domain. In tandem with the striking performance gains that GNNs are able to achieve, many of these systems, have been shown to be strongly influenced by the degree, or number of outgoing edges, of individual nodes. More concretely, recent works have uncovered disparities in the qualities of representations learned by state of the art GNNs between nodes which are strongly and weakly connected. Translating these findings to the sphere of recommender systems, where nodes and edges are used to represent the interactions between users and various items, these disparities in representation that are contingent upon a node's connectivity can be seen as a form of popularity bias. And, indeed, within the broader recommendation community, popularity bias has long been considered an open problem, in which recommender systems begin to favor mainstream content over, potentially more relevant, but niche or novel items. If left unchecked these algorithmic nudged towards previously popular content can create, intensify, and enforce negative cycles that perpetuate disparities in representation on both the user and the creator ends of the content consumption pipeline. Particularly in the recommendation of creative (e.g. musical) content, the downstream effects in these disparities of visibility can have genuine economic consequences for artists from under-represented communities. Thus, the problem of popularity bias is something that must be addressed from both a technical and societal perspective. And, as the influence of recommender systems continues to spread, the effects of this phenomenon only become more spurious, as they begin to have critical downstream effects that shape the larger ecosystems in which art is created. Thus, the broad focus of thesis is the mitigation of popularity bias in music recommendation. In order to tailor our exploration of this issue to the graph domain, we begin by formalizing the relationship between degree fairness and popularity bias. In doing so, we concretely define the notion of popularity, grounding it in the structural principles of an interaction network, and enabling us to design objectives that can mitigate the effects of popularity on representation learning. In our first work, we focus on understanding the effects of sampling on degree fairness in uni-partite graphs. The purpose of this work is to lay the foundation for the graph neural network model which will underlie our music recommender system. We then build off this first work by extending the initial fairness framework to be compatible with bi-partite graphs and applying it to the music domain. The motivation of this work is rooted in the notion of discovery, or the idea that users engage with algorithmic curation in order to find content that is both novel and relevant to their artistic tastes. We present the intrinsic relationship between discovery objectives and the presence of popularity bias, explaining that the presence of popularity bias can blind a system to the musical qualities that underpin the underlying needs of music listening. As we will explain in later sections, one of the key elements of this work is our ability to ground our fairness notion in the musical domain. Thus, we propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems. In order to facilitate this domain awareness, we perform extensive dataset augmentation, taking two state of the art music recommendation datasets and augmenting them with rich multi-modal node-level features. Finally, we ground our evaluation in the cold start setting, showing the importance of inductive methodologies in the music space. / La dernière décennie a apporté avec elle une vague de technologies innovantes, modifiant la manière dont le contenu créatif est créé, consommé et catégorisé. Et, à mesure que nos interactions avec les contenus multimédias créatifs se déplacent vers les plateformes en ligne, la quantité de contenu sur ces plateformes a nécessité l’intégration d’un guidage algorithmique dans la découverte de ces espaces. De cette façon, les algorithmes de recommandation qui guident les interactions des utilisateurs avec diverses formes d’art ont été jetés dans le rôle de gardiens et ont commencé à jouer un rôle de plus en plus influent dans l’élaboration de la création de contenu artistique. Le travail présenté dans les chapitres suivants fusionne trois grands domaines de recherche : l’apprentissage de la représentation graphique, la recherche d’informations musicales et l’équité appliquée à la tâche de recommandation musicale. Alors que l’influence des systèmes de recommandation continue de s’étendre et de s’intensifier, il est crucial de prendre en compte les effets en aval que les choix de conception peuvent avoir sur l’écosystème plus large de la création artistique. Ces dernières années, l’intégration des réseaux sociaux dans la tâche de recommandation musicale a donné naissance aux réseaux neuronaux de graphes (GNN), une nouvelle architecture capable de faire des prédictions sur les structures de graphes. Parallèlement aux gains miraculeux que les GNN sont capables de réaliser, bon nombre de ces systèmes peuvent également être la proie de biais de popularité, les forçant à privilégier le contenu grand public par rapport à des éléments potentiellement plus pertinents, mais de niche ou nouveaux. S’il n’est pas maîtrisé, ce cycle négatif peut perpétuer les disparités de représentation entre la musique d’artistes, de genres ou de populations minoritaires. Et, ce faisant, les disparités dans la visibilité des éléments peuvent entraîner des problèmes à la fois du point de vue des performances et de la société. L’objectif de la thèse est l’atténuation du biais de popularité. Premièrement, le travail formalise les liens entre l’équité individuelle et la présence d’un biais de popularité parmi les contenus créatifs. Ensuite, nous étendons un cadre d’équité individuelle, en l’appliquant au domaine de la recommandation musicale. Le coeur de cette thèse s’articule autour de la proposition d’une approche basée sur l’équité individuelle et sensible au domaine qui traite le biais de popularité dans les systèmes de recommandation basés sur les réseaux de 5 neurones graphiques (GNN). L’un des éléments clés de ce travail est notre capacité à ancrer notre notion d’équité dans le domaine musical. Afin de faciliter cette prise de conscience du domaine, nous effectuons une augmentation étendue des ensembles de données, en prenant deux ensembles de données de recommandation musicale à la pointe de la technologie et en les augmentant avec de riches fonctionnalités multimodales au niveau des noeuds. Enfin, nous fondons notre évaluation sur le démarrage à froid, montrant l’importance des méthodologies inductives dans l’espace musical.

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Yes / Recent decades have witnessed a growing number of states around the world relying on border control measures, such as immigration detention, to govern human mobility and control the movements of those classified as “unauthorised non-citizens.” In response to this, an increasing number of scholars from several disciplines, including psychologists, have begun to examine this phenomenon. In spite of the widespread concerns raised, few studies have been conducted inside immigration detention sites, primarily due to difficulties in gaining access. This body of research becomes even scanter when it comes to the experiences of detained women. This study is the first of its kind to have surveyed 93 women confined in an Italian immigration detention facility. A partial mediation model with latent variables was tested through partial least structural equation modelling (PLS-SEM). The findings revealed the negative impact that unfair immigration procedures have on detained women’s human dignity, which in turn negatively affects their self-rated physical and mental health. Overall, our study sheds light on the dehumanisation and damage to human dignity that immigration detention entails, as well as its negative impact on the health of those affected. This evidence reinforces the image of these institutions as sites of persistent injustice, while stressing the need to envision alternative justice-oriented forms to address human mobility. / FE’s doctoral research, on which this article relies, was supported by the Portuguese Foundation for Science and Technology (SFRH/BD/87854/2012), and her subsequent work was supported by the Portuguese Foundation for Science and Technology (grant number: CEECIND/00924/2018/CP1541/CT0004).