Fenômenos coletivos em redes sociais como a formação de linguagem ou cultura, crenças, emergência de consenso em relação a algum assunto, aquisição de conhecimento e aprendizagem, dentre outros, tem conduzido a um grande interesse no estudo de comportamentos cooperativos e fenômenos sociais, resultando numa grande variedade de dinâmicas de opinião. Nestes modelos, uma população de agentes interagentes carrega uma variável (ou um conjunto delas) numérica cujo valor representa uma opinião sobre um tópico, com interpretações distintas em cada contexto. Inspirados em conceitos de mecânica estatística e mecanismos sociais, estes estados evoluem governados por regras matemáticas que controlam a dinâmica de interação entre os agentes e a influência de fatores externos. Outro ingrediente importante na modelagem de sistemas reais é que a representação das interações entre agentes difere bastante de reticulados ou misturas homogêneas, sendo mais bem descritas por redes complexas. Neste trabalho, estudamos a dinâmica de opinião de Krause e Hegselmann. Neste modelo, agentes possuem opiniões que assumem valores contínuos e são atualizados de acordo com a vizinhança compatível, definida pelo princípio da confiança limitada. Após apresentar uma revisão da literatura, estudamos a dinâmica de opinião no contexto de Redes Complexas, seguido de modificações do modelo que consideram a ação de ruído e campo externo (propaganda). Finalmente, propomos um modelo de consenso cuja interpretação está inserida no contexto de aquisição de conhecimento por agentes interagentes que realizam observações sujeitas a erros. Os resultados mostram como os diferentes tipos de topologia influenciam no comportamento das dinâmicas. / Collective phenomena in social networks such as formation of language or culture, beliefs, emergence of consensus on any subject, knowledge acquisition and learning, among others, has led to an increasing interest in the study of cooperative behavior and social phenomena, resulting in great variety of opinion dynamics. In these models, a population of interacting agents holds a variable (or a set of them) whose numerical value is an opinion on a topic, with different interpretations in each context. Inspired by concepts from statistical mechanics and social mechanisms, these states evolve governed by mathematical rules that control the dynamics of interaction between agents and the influence of external factors. Another important ingredient in the modeling of real systems is the representation of the interactions between agents, which strongly differs from lattices or fully mixed states, being better described by complex networks. In the present work, we study the opinion dynamics of Krause and Hegselmann. In this model, agents hold opinions that assume continuous values and are updated according to their compatible neighborhood, defined by the bounded confidence principle. After presenting a literature review, we studied the opinion dynamics in the context of complex networks, followed by modifications of the model considering the effect of noise and external field (advertising). Finally, we propose a consensus model interpreted as a process of knowledge acquisition by interacting agents that make observations subject to errors. The results show how the topology influences the dynamic behavior.

Fenômenos coletivos em redes sociais como a formação de linguagem ou cultura, crenças, emergência de consenso em relação a algum assunto, aquisição de conhecimento e aprendizagem, dentre outros, tem conduzido a um grande interesse no estudo de comportamentos cooperativos e fenômenos sociais, resultando numa grande variedade de dinâmicas de opinião. Nestes modelos, uma população de agentes interagentes carrega uma variável (ou um conjunto delas) numérica cujo valor representa uma opinião sobre um tópico, com interpretações distintas em cada contexto. Inspirados em conceitos de mecânica estatística e mecanismos sociais, estes estados evoluem governados por regras matemáticas que controlam a dinâmica de interação entre os agentes e a influência de fatores externos. Outro ingrediente importante na modelagem de sistemas reais é que a representação das interações entre agentes difere bastante de reticulados ou misturas homogêneas, sendo mais bem descritas por redes complexas. Neste trabalho, estudamos a dinâmica de opinião de Krause e Hegselmann. Neste modelo, agentes possuem opiniões que assumem valores contínuos e são atualizados de acordo com a vizinhança compatível, definida pelo princípio da confiança limitada. Após apresentar uma revisão da literatura, estudamos a dinâmica de opinião no contexto de Redes Complexas, seguido de modificações do modelo que consideram a ação de ruído e campo externo (propaganda). Finalmente, propomos um modelo de consenso cuja interpretação está inserida no contexto de aquisição de conhecimento por agentes interagentes que realizam observações sujeitas a erros. Os resultados mostram como os diferentes tipos de topologia influenciam no comportamento das dinâmicas. / Collective phenomena in social networks such as formation of language or culture, beliefs, emergence of consensus on any subject, knowledge acquisition and learning, among others, has led to an increasing interest in the study of cooperative behavior and social phenomena, resulting in great variety of opinion dynamics. In these models, a population of interacting agents holds a variable (or a set of them) whose numerical value is an opinion on a topic, with different interpretations in each context. Inspired by concepts from statistical mechanics and social mechanisms, these states evolve governed by mathematical rules that control the dynamics of interaction between agents and the influence of external factors. Another important ingredient in the modeling of real systems is the representation of the interactions between agents, which strongly differs from lattices or fully mixed states, being better described by complex networks. In the present work, we study the opinion dynamics of Krause and Hegselmann. In this model, agents hold opinions that assume continuous values and are updated according to their compatible neighborhood, defined by the bounded confidence principle. After presenting a literature review, we studied the opinion dynamics in the context of complex networks, followed by modifications of the model considering the effect of noise and external field (advertising). Finally, we propose a consensus model interpreted as a process of knowledge acquisition by interacting agents that make observations subject to errors. The results show how the topology influences the dynamic behavior.

Skyrme functional is commonly used for the description of ground-state and dynamical properties of atomic nuclei. To describe the dynamical properties in the microscopic self-consistent way, we employed Separable Random Phase Approximation (SRPA) based on Skyrme functional. This work describes theory of Skyrme Hartree-Fock and SRPA and presents numerical calculation of E1 and M1 giant resonances in spherical nuclei Ca-40 - Fe-56. There is some evidence for non-zero ground-state deformation of the nucleus Fe-56, so it is treated also with such assumption. The results obtained for various parametrizations are compared to the experimental data.

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous. Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change. Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.

This thesis explores the problems raised by the aggregation of entities into a global, collective level, an old problem encountered in many fields of science. We work on three projects related to the aggregation problem in social systems, using tools derived from statistical physics, and more generally quantitative tools. The first project focus on a paradigmatic model of the emergence of puzzling macroscopic behavior from simple individual rules, Schelling's segregation model. We hence propose an analytical resolution of this model and we studied analytically and via simulations the effect of several forms of cooperation between individual agents on the collective behavior. These questions are tackled in a mutually beneficial way for both economics and physics. The second project is based on the exploration of huge databases on scientific literature. We hence produce several 'science maps' representing the fields of complex systems (its internal structure and coherence being analysed through the references used by ~140000 relevant articles) and the research carried out in a scientific institution such as the ENS de Lyon. Finally, the third project deals with the elaboration of models of social phenomena based on natural sciences tools but sociologically grounded. We hence present the elaboration process of a model built with a team of sociologists. We then propose an opinion model specifically designed to explore a single question: the existence of lasting structure from non lasting entities.

Comprendre les propriétés des composés à électrons fortement corrélés nouvellement découverts est un important défi à la fois pour des raisons fondamentales et un impact industriel à long terme. Une activité expérimentale sur les métaux et supraconducteurs à électrons lourds a mis en évidence des effets qui se démarquent clairement de notre compréhension actuelle. Le but de cette thèse est de modéliser les effets de spin spéciaux qui ont été observés en réponse à un champ magnétique dans le supraconducteur CeCoIn(5). Elle est composée de deux parties. Dans un premier temps nous avons à faire à la distribution anormale du champ magnétique local dans le réseau de vortex révélé par les expériences de diffraction de neutrons à petits angles et rotation de spin muonique. Sur la base de a théorie de Ginzburg-Landau avec prise en compte de l'effet de spin, nous analysons l'inhomogénéité du champ local dans le réseau de vortex et calculons des expressions pour les facteurs de forme en diffraction neutronique et la largeur de raie statique en rotation de spin muonique. Nous montrons que les données expérimentales anormales sont le résultat de supercourants générés par le spin circulant autour du cœur du vortex et donnent une augmentation de l'inhomogénéité du champ sur une distance de l'ordre de la longueur de corrélation du supraconducteur à partir de l'axe du vortex. L'importance de l'effet est contrôlée par une seule quantité (le paramètre de Maki) qui permet la détermination de propriétés physiques du système à partir de données expérimentales. La seconde partie traite d'une transition d'onde de densité de spin presque commensurable dans un supraconducteur non-conventionnel. Elle est motivée par l'observation du confinement d'un ordre d'onde de densité de spin dans la phase supraconductrice de CeCoIn(5) dans un champ magnétique. Dans le cadre de la formulation spin-fermion nous proposons un mécanisme pour la transition de l'état fondamental qui consiste du ralentissement du mode collectif de fluctuation de densité de spin induit par le champ (exciton de spin) vers un ordre statique. Cela représente un scénario par lequel la transition vers l'ordre de spin est reliée intrinsèquement au supraconducteur. / Understanding the properties of newly discovered strongly correlated electron compounds is a considerable challenge for both fundamental matters and long-term industrial impact. Experimental activity on heavy electron metals and superconductors has lead to highlighting effects that depart from current knowledge. The thesis is aimed at modelling effects that have been observed in response to magnetic field in the heavy electron superconductor CeCoIn$_5$. This consists of two parts. In the first time we deal with the vortex lattice state anomalous local magnetic field space variations as highlighted by small angle neutron scattering and muon spin rotation experiment. On the basis of the Ginzburg-Landau theory with account of spin effect, we analyse the local field inhomogeneity in the vortex lattice and derive expressions for the neutron scattering form factors and muon spin rotation static linewidth. The anomalous experimental data are shown to be result of spin driven supercurrents which circulate around the vortex cores and lead to an increase with external field in the internal field inhomogeneity on a distance of the order of the superconducting coherence length from the vortex axis. The importance of the effect is controlled by a single quantity (the Maki parameter). The second part is on nearly commensurate spin density wave transition in a quasi two-dimensional superconductor. It is motivated by observation of the confinement of spin density wave ordering inside the superconducting state of CeCoIn$_5$ in magnetic field. In the frame of the spin-fermion formulation we propose a mechanism for the ground state transition consisting in the field-induced slowing down of a collective spin density fluctuation mode (spin-exciton) to static ordering. This represents a scenario by which the transition to spin ordering is intrinsically related to superconductivity

L'objectif principal de la thèse consiste à explorer des problématiques propres aux sciences sociales et à les étudier à l'aide d'outils issus du champ de la physique statistiques et des systèmes complexes. Le travail de la thèse s'est ainsi décliné sur trois grands sujets dont la problématique principale est la question de l'agrégation d'entités individuelles en une structure collective. Le premier sujet est centré sur un exemple paradigmatique de l'émergence d'un comportement collectif macroscopique inattendu à partir de règles individuelles simples: le modèle de ségrégation de Schelling. Nous avons notamment proposé une résolution analytique inédite de ce modèle et nous avons étudié analytiquement et via des simulations l'impact de différentes formes de coopération entre agents individuels sur le comportement collectif global. Cette thématique a été développée à la fois d'un point de vue économique et d'un point de vue physique. Le second sujet porte sur l'exploration de bases de données bibliométriques. Nous avons ainsi produit des 'cartes des sciences' représentant le champ des systèmes complexes (sa structure interne étant décrypté via une analyse des références utilisées par ~140000 articles) ou encore l'état de la recherche au sein d'un établissement tel que l'ENS de Lyon. Enfin, le troisième thème porte sur l'élaboration de modèles basés sur des outils des sciences 'dures' mais sociologiquement fondés. Nous présentons ainsi le processus d'élaboration d'un modèle construit avec une équipe de sociologues. Enfin, nous développons un modèle d'opinion répondant spécifiquement à une question: l'existence de structures qui durent à partir d'entités qui ne durent pas. / This thesis explores the problems raised by the aggregation of entities into a global, collective level, an old problem encountered in many fields of science. We work on three projects related to the aggregation problem in social systems, using tools derived from statistical physics, and more generally quantitative tools. The first project focus on a paradigmatic model of the emergence of puzzling macroscopic behavior from simple individual rules, Schelling's segregation model. We hence propose an analytical resolution of this model and we studied analytically and via simulations the effect of several forms of cooperation between individual agents on the collective behavior. These questions are tackled in a mutually beneficial way for both economics and physics. The second project is based on the exploration of huge databases on scientific literature. We hence produce several 'science maps' representing the fields of complex systems (its internal structure and coherence being analysed through the references used by ~140000 relevant articles) and the research carried out in a scientific institution such as the ENS de Lyon. Finally, the third project deals with the elaboration of models of social phenomena based on natural sciences tools but sociologically grounded. We hence present the elaboration process of a model built with a team of sociologists. We then propose an opinion model specifically designed to explore a single question: the existence of lasting structure from non lasting entities.

Dans cette thèse, nous présentons un cadre théorique pour étudier les systèmes de particules actives fonctionnant avec une quantité discrète d'états internes qui contrôlent le comportement externe de ces objets. Les concepts théoriques développés dans cette thèse sont introduits afin de comprendre un grand nombre de systèmes biologiques multi-agents dont les individus présentent différents types de comportements se succédant au cours du temps. Par construction, le modèle théorique suppose que l'observateur extérieur a accès uniquement au comportement visible des individus, et non pas à leurs états internes. C'est seulement après une étude détaillée de la dynamique comportementale que l'existence de ces états internes devient évidente. Cette analyse est cruciale pour pouvoir associer les comportements observés expérimentalement avec un ou plusieurs états internes du modèle. Cette association entre les états et les comportements doit être faite selon les observations et la phénoménologie du système biologique faisant l'objet de l'étude. Les scénarios qui peuvent être observés en utilisant notre modèle théorique sont déterminés par la conception du mécanisme interne des individus (nombre d'états internes, taux de transition, etc…) et seront de nature markovienne par construction. Tous les travaux expérimentaux et théoriques contenus dans cette thèse démontrent que notre modèle est approprié pour décrire des systèmes réels montrant des comportements intermittents individuels ou collectifs. Ce nouveau cadre théorique pour des particules actives avec états internes, introduit ici, est encore en développement et nous sommes convaincus qu'il peut potentiellement ouvrir de nouvelles branches de recherche à l'interface entre la physique, la biologie et les mathématiques. / In this thesis we introduce a theoretical framework to understand collections of active particles that operate with a finite number of discrete internal states that control the external behavior of these entities. The theoretical concepts developed in this thesis are conceived to understand the large number of existing multiagent biological systems where the individuals display distinct behavioral phases that alternate with each other. By construction, the premise of our theoretical model is that an external observer has access only to the external behavior of the individuals, but not to their internal state. It is only after careful examination of the behavioral dynamics that the existence of these internal states becomes evident. This analysis is key to be able to associate the experimentally observed behaviors of individuals with one or many internal states of the model. This association between states and behaviors should be done accordingly to the observations and the phenomenology displayed by the biological system that is being the subject of study. The possible scenarios that can be observed using our theoretical model are determined by the design of the internal mechanism of the individuals (number of internal states, transition rates, etc...) and will be of markovian nature by construction. All the experimental and theoretical work contained in this thesis is evidence that our model is suitable to be used to describe real-life systems showing individual or collective intermittent behaviors. This here-introduced new framework of active particles with internal states is still in development and we are convinced that it can potentially open new branches of research at the interface between physics, biology and mathematics.

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous. Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change. Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.

Active matter describes systems that convert energy from their environment into directed motion. Therefore, these systems are in intrinsic nonequilibrium, unlike their passive counterparts. From a theoretical point of view, such active systems have been modeled by agent-based models, as well as hydrodynamic approaches, which allowed for the investigation of a wide range of observed collective phenomena characterizing active matter. In this thesis we develop a microscopic field-theoretical approach to describe generic properties of active systems. This description combines the phase field crystal model with a polar order parameter and a self-propulsion term. First, we validate this approach by reproducing results obtained with corresponding agent-based models, such as binary collisions, collective migration and vortex formation. We also perform a direct comparison between our model and a microscopic phase field description of active matter. Next, we use this continuum approach to simulate some larger active systems and to analyze the coarsening process in active crystals, as well as the mechanisms leading to mobile clusters. We show the generality of our approach by extending it to binary mixtures of interacting active and passive particles. Also in this case, we first validate the model by reproducing known results, such as enhanced crystallization via active doping and the suppression of collective migration in an active bath in the presence of fixed obstacles. Interestingly, for the regime of mobile passive particles in an active bath a laning state is found, which is characterized by an alignment of the active particles that is globally nematic, but polar within each lane. This state represents a theoretical prediction feasible to be validated experimentally. Finally, we explore the field of topological active matter. We develop an agent-based model to describe self-propelled particles on curved surfaces and study the complex spatiotemporal patterns that emerge. / Aktive Materie beschreibt Systeme, die Energie aus ihrer Umgebung in gerichtete bewegung umwandeln. Im Gegensatz zur passiven Materie befinden sich diese Systeme nie im physikalischen Gleichgewicht und offenbaren dadurch interessante physikalische Phänomene. Vom theoretischen Standpunkt her wurde aktive Materie bereits simuliert, typischerweise durch agenten-basierte Modelle oder hydrodynamische Ansätze, die es ermöglichen eine Vielzahl der auftretenden kollektiven Bewegungsprinzipien zu untersuchen. In dieser Doktorarbeit entwickeln wir einen mikroskopischen Kontinuumsansatz um die generischen Eigenschaften von aktiven Systemen zu untersuchen. Unsere Beschreibung kombiniert das Phasenfeld-Kristall Modell mit einem polaren Ordnungsparameter und einem Antriebsterm. Zuerst validieren wir den Ansatz durch Reproduktion bekannter Ergebnisse agenten-basierter Modelle, wie binäre Kollisionen, kollektive Bewegung und Wirbelformationen. Des Weiteren führen wir einen direkten Vergleich zwischen unserem Modell und einer mikroskopischen Phasenfeldbeschreibung aktiver Materie durch. Danach nutzen wir den kontinuierlichen Ansatz um große aktive Systeme zu simulieren und analysieren den Vergröberungsprozess in aktiven Kristallen und Mechanismen der mobilen Aggregatbildung. Wir illustrieren die Allgemeingültigkeit unseres Simulationsansatzes durch die Erweiterung auf binäre Systeme, in denen sowohl aktive als auch passive Partikel enthalten sind. Auch in diesem Fall validieren wir das Modell durch Vergleiche mit bekannten Resultaten, wie zum Beispiel die verstärkte Kristallisation durch aktives Doping oder die Unterdrückung kollektiver Bewegung durch die Einführung von Hindernissen in einem aktiven Bad. Interessanterweise finden wir bei der Präsenz mobiler passiver Partikel in einem aktiven Bad einen Fahrspur-Zustand, in welchem die aktiven Partikel nematische Fahrspuren bilden und sich nur jeweils innerhalb einer Fahrspur nematisch polar anordnen. Dieser bisher unbekannte Zustand stellt eine theoretische Vorhersage dar, die experimentell geprüft werden kann. Schließlich begeben wir uns auf das Gebiet der topologischen aktiven Materie. Wir entwickeln ein agenten-basiertes Modell um selbst-angetriebene Partikel auf gekrümmten Oberflächen zu beschreiben und untersuchen die dabei auftretenden zeitlich und räumlich komplexen Muster.%, die dabei auftreten.