In this dissertation, we determine how the dispersion, network formation and alignment of carbon nanotubes in polymer nanocomposites affect the electrical properties of two different polymer composite systems: high temperature bismaleimide (BMI) and polyethylene. The knowledge gained from this study will facilitate optimization of the above mentioned parameters, which would further enhance the electrical properties of polymer nanocomposites. BMI carbon fiber composites filled with nickel-coated single walled carbon nanotubes (Ni-SWNTs) were processed using high temperature vacuum assisted resin transfer molding (VARTM) to study the effect of lightning strike mitigation. Coating the SWNTs with nickel resulted in enhanced dispersions confirmed by atomic force microscopy (AFM) and dynamic light scattering (DLS). An improved interface between the carbon fiber and Ni-SWNTs resulted in better surface coverage on the carbon plies. These hybrid composites were tested for Zone 2A lightning strike mitigation. The electrical resistivity of the composite system was reduced by ten orders of magnitude with the addition of 4 weight percent Ni-SWNTs (calculated with respect to the weight of a single carbon ply). The Ni-SWNTs - filled composites showed a reduced amount of damage to simulated lightning strike compared to their unfilled counterparts indicated by the minimal carbon fiber pull out. Methods to reduce the electrical resistivity of 10 weight percent SWNTs -- medium density polyethylene (MDPE) composites were studied. The composites processed by hot coagulation method were subjected to low DC electric fields (10 V) at polymer melt temperatures to study the effect of viscosity, nanotube welding, dispersion and, resultant changes in electrical resistivity. The electrical resistivity of the composites was reduced by two orders of magnitude compared to 10 wt% CNT-MDPE baseline. For effective alignment of SWNTs, a new process called Electric field Vacuum Spray was devised to overcome viscosity within the dispersed nanotube polymer system, and produce conductive MDPE-SWNT thin films. Polarized Raman spectroscopy and scanning electron microscopy (SEM) analysis on the samples showed an improvement in SWNT -- SWNT contacts and alignment in the polymer matrix. The resistivity of the samples processed by this new method was two order magnitudes lower than the samples processed by hot coagulation method subjected to electric field.

No description available.

Graph theoretical approaches have become a powerful tool for investigating the architecture and dynamics of complex networks. The topology of network graphs revealed small-world properties for very different real systems among these neuronal networks. In this study, we observed the early development of mouse retinal ganglion cell (RGC) networks in vitro using timelapse video microscopy. By means of a time-resolved graph theoretical analysis of the connectivity, shortest path length and the edge length, we were able to discover the different stages during the network formation. Starting from single cells, at the first stage neurons connected to each other ending up in a network with maximum complexity. In the further course, we observed a simplification of the network which manifested in a change of relevant network parameters such as the minimization of the path length. Moreover, we found that RGC networks self-organized as small-world networks at both stages; however, the optimization occurred only in the second stage.

La modelación individual de la interacción entre agentes en Economía ha recibido particular atención en los últimos años. Estos aportes han recaído inicialmente, y siguiendo la tradición académica, en modelos analíticos que contemplan la aparición de interacciones locales entre los individuos. Esta ineracción permite, en algún punto, determinar resultados de conducta diferentes comparados con un análisis en el que los agentes se comporten aisladamente. La moderación de dicha interacción en términos formales ha utilizado herramitentas matemáticas que permiten la traslación de efectos por parte de un individuo hacia otro. La teoría de juegos, dado que analiza en el comportamiento óptimo de un agente que se ve restringido en su accionar por las estrategias de otros agentes, resulta un modelo analítico clásico de ciertos casos de interacción. La tesis utiliza el enfoque basado en agentes para modelar las interacciones entre agentes y comprobar si los resultados teóricos pueden ser alcanzados a través de un modelo computacionalmente evolutivol. Sus resultados no son formales sino numéricos. Su sustento no es básicamente matemático sino computacional. Los agentes resultan ser bajo este marco de investigaciòn objetos de programas. Los objetos agente tienen estados y reglas de comportamiento. Correr un modelo de agentes comprende la inicialización de la población de agentes, el proceso subsiguientede interacción entre los mismos y el monitoreo del resultado final. En definitiva, correr el modelo en el tiempo la suficiente cantidad de veces, en caso de ser estocástico, es lo único que se necesita para "resolverlo". La tesos comprende 5 capítulos. El primero es una introducción general mientras que el segundo se aboca a explayar los modelos teóricos que serán testeados por modelos computacionales. El tercer capítulo presenta la metodología computacional de algoritmos evolutivos y el capítulo 4 analiza el resultado de los experimentos computacionales. Finalmente, la tesis concluye con el capítulo 5. Como síntesis del trabajo, los algoritmos evolutivos fueron exitosos en reproducir los resultados extremos de formación de redes (la red vacía y la red completa) pero fallaron en replicar los resultados de arquitecturas intermedias propuestas por la teoría. / The modeling of interaction between agents in economics has received particular attention in recent years. These contribu-tions have focused intially, and following the acaademic tradition, in analytically tracktable models that showed emer-gence of local interactions among individuals. This interaction can, at some point, determining different behavioral outcomes compared with an analysis in which agents behave in isolation. The modeling of this interaction in formal terms used mathematical tools that allow the translation of effects from one individual to another. Game theory, modelling the optimal behavior of an agent constrained in its actions by the strategies of other agents, is a classical analytic model for certain cases of interaction. The thesis uses agent-based approach to model interactions between agents and contrast if theoretical results can be achieved through an evolutionary computational model.Their results are not formal but numeri-cal. Their background is not mathematical but basically computing. The agents appear under this framework of re-search as program objects. Agent as objects have sates and rules of behavior.Running an agent model includes te initia-lization of the population of agents, the subsequent process of interaction among them and monitoring the outcome. In short, running the model a sufficient number of times, if it is stochastic, is all that is needed to "solve" it. The thesis consists of 5 chapters. The first is a general intoduction while in the second es summarized the theoretical models that are going to be tested by computer models. The third chapter presents the computational methodology of evolutionary algorithms and chapter 4 discusses the results of computa-tional experiments. Finally, the thesis concludes with Chapter 5. As a summary of the work, evolutionary algorithms were successful in reproducing the extreme results (the empty network and the complete network) of network formation games but failed to replicate the results of inter-mediate architectures proposed by the theory.

Nowadays people and organizations are more and more interconnected in the forms of social networks: the nodes are social entities and the links are various relationships among them. The social network theory and the methods of social network analysis (SNA) are being increasingly used to study such real-world networks in order to support knowledge management and decision making in organizations. However, most existing social network studies focus on the static topologies of networks. The dynamic network link formation process is largely ignored. This dissertation is devoted to study such dynamic network formation process to support knowledge management and decision making in networked environments. Three challenges remain to be addressed in modeling and analyzing the dynamic network link formation processes. The first challenge is about modeling the network topological changes using longitudinal network data. The second challenge is concerned with examining factors that influence formation of links among individuals in networks. The third challenge is regarding link prediction in evolving social networks. This dissertation presents four essays that address these challenges in various knowledge management domains. The first essay studies the topological changes of a major international terrorist network over a 14-year period. In addition, this paper used a simulation approach to examine this network's vulnerability to random failures, targeted attacks, and real world authorities' counterattacks. The second essay and third essay focuses on examining determinants that significantly influence the link formation processes in social networks. The second essay found that mutual acquaintance and vehicle affiliations facilitate future co-offending link formation in a real-world criminal network. The third essay found that homophily in programming language preference, and mutual are determinants for forming participation links in an online Open Source social network. The fourth essay focuses on the link prediction in evolving social networks. It proposes a novel infrastructure for describing and utilizing the discovered determinants of link formation process (i.e. semantics of social networks) in link prediction to support expert recommendation application in an Open Source developer community. It is found that the integrated mechanism outperforms either user-based or Top-N most recognized mechanism.

We test the performance of myopic and farsighted stability concepts in a network formation experiment with a stream of payoffs and relatively unstructured link formation process. A subtle treatment variation demonstrates clearly the power of myopic stability concepts in precisely identifying the set of the most stable networks. However, we also find support for the predictions of farsighted concepts of stability, especially those that assume players' pessimism about the eventual outcome of a deviation. This is the first study to demonstrate that there exist environments where farsighted stability concepts identify empirically stable networks that are not identified by myopic concepts. Thus, myopic stability concepts are not necessarily sufficient to predict all stable outcomes in empirical applications.

Neste projeto analisamos jogos de formação de redes que são variantes do problema da floresta de Steiner, nos quais indivíduos desejam conectar conjuntos de vértices terminais em um grafo de forma a minimizar seus custos, podendo dividir o custo das arestas com os demais participantes. Estudamos como o método de divisão de custos influencia na existência e na qualidade dos equilíbrios desses jogos em comparação com o valor da solução ótima centralizada. / In this project we analyze network formation games that are variants of the Steiner forest problem, in which individuals wish to connect sets of terminal vertices of a graph in a way that minimizes their costs, being able to divide the cost of an edge with the other participants. We study how the method used to divide the costs influences the existence and quality of the equilibria of these games in relation to the centralized optimal solution.

The use of personal contacts and the role of education as a signal of the Worker's productivity are two important aspects of the job search process. The aim of this thesis is to develop a model that combines both approaches. We distinguish between random and strategic models of job information networks. In the former case the structure of the network is given, while in the latter it depends on the strategic decision of the Workers. We present a strategic model of network formation with two types of Workers who are able to signal their productivity by the level of their education. When applying for a job they have two possibilities of contacting the Employer: a direct application and an indirect application through a friend who currently works for the Employer.

This dissertation contains two essays which examine the roles that individual incentives, competition, and information play in network formation. In the first essay, I examine a model in which two competing groups offer different allocation rules that may depend on the network of connections among the individuals that make up each group. I assume the existence of a single divisible good, such as a monetary prize, which will be divided amongst the members of the winning network. The probability of winning the prize will depend on the network sizes. I examine two well-known allocation rules: the Myerson value and the egalitarian rule. I prove existence of equilibria and characterize the properties of the two networks. The implications of the equilibria networks for the outcome of the contest are discussed. I find that the winning probability of the network using the Myerson value has an upper bound very close to one half. There is no such upper bound for the network using the egalitarian rule. In my second essay, I examine a dynamic model of network formation in which individuals use reinforcement learning to choose their actions. Typically, economic models of network formation assume the entire network structure to be known to all individuals involved. The introduction of reinforcement learning allows us to relax this assumption. Through the use of a state-dependent reinforcement learning rule, one may allow for varying degrees of information available to the agents. Three informational settings are examined and I determine what networks, if any, each model may converge to in the limit. The long-run behavior of each model is examined through the use of simulations and compared to one another. I find that amount and type of information agents have access plays an important role in which networks emerge when there is no dominant strategy for the agents choosing links. If there is a dominant link choosing strategy, the most efficient network structure quickly emerges in each informational setting. Together, these essays investigate how information, incentives, and competition may affect network formation. Individual incentives in the presence of competition can create tension between an individual's social ties and the overall network size. Information plays a key role in the emergent network topologies when there are no dominate network building strategies.

Dans un monde où les réseaux deviennent une forme dominante d’organisation, la structure des réseaux et la position des individus en leur sein affectent les comportements individuels et les résultats économiques agrégés. L’analyse de la formation des réseaux par un planificateur central ou par les individus est au cœur de cette thèse en économie des réseaux. Le Chapitre 1 étudie de manière théorique la formation et la protection optimale des réseaux par un planificateur central sachant qu’un agent externe peut détruire k liens. La protection s’effectue soit en densifiant les liens entre les nœuds, soit en protégeant les liens. Lorsque le coût de protection est suffisamment faible, un réseau minimalement connecté constitué de liens protégés garantit le flux de communication; si ce coût est élevé, la solution optimale est de former un réseau symétrique où chaque nœud possède au moins k+1 liens non-protégés. Le Chapitre 2 explore la formation décentralisée de réseaux en laboratoire en analysant les décisions individuelles de formation de liens lorsqu’un agent a une valeur supérieure aux autres et que le processus de formation de liens est séquentiel. Les résultats montrent que la séquentialité facilite la coordination sur des réseaux efficaces mais qui ne correspondent pas à l’équilibre parfait en sous-jeu. L’hétérogénéité entre les agents accroit l’asymétrie du réseau en raison de la polarisation des liens sur l’agent à valeur supérieure. Le Chapitre 3 étudie l’impact de la formation endogène d’un réseau sur l’importance des effets de pairs, avec une application aux comportements malhonnêtes. Afin d’identifier les effets des comparaisons sociales, deux environnements contrôlés sont créés en laboratoire dans lesquels les individus choisissent ou non leurs pairs, puis observent leur comportement. Les résultats montrent que les effets de pairs sur les comportements malhonnêtes sont significativement accrus lorsque les individus peuvent choisir leurs pairs. / In a world where networks become a dominant form of organization, the structure of networks and the position of individuals in these networks affect individual behavior and aggregate economic outcomes. The analysis of network formation by a central planner or by individuals themselves is at the heart of this thesis on the economics of networks.Chapter 1 theoretically studies the optimal formation and protection of networks by a central planner knowing that an external agent can destroy k links. The protection of the network can be guaranteed either by densifying the links between nodes, or by protecting the links. When the cost of protection is relatively small, a minimally connected network composed of protected links guarantees the communication flow; if this cost is high, the optimal solution is to form a symmetric network where each node has at least k+1 non-protected links.Chapter 2 explores the decentralized formation of networks in the laboratory by analyzing individual linking formation decisions when one agent has a higher value than others and that the linking formation process is sequential. The results show that sequentiality facilitatesthe coordination on efficient networks but that do not correspond to the Subgame PerfectEquilibrium. The heterogeneity across agents increases the asymmetry of networks because of the polarization of links on the agent with a higher value.Chapter 3 studies the impact of the endogenous formation of networks on the importance of peer effects, applied to dishonest behavior. In order to identify the effects of social comparisons, two controlled environments are designed in the laboratory in which individuals choose or not their peers, and then observe their behavior. The results show that peer effects on dishonest behavior are significantly higher when individuals can choose their peers.