Suchergebnisse

Varios estudios demuestran que la corrupción no es únicamente un problema de negociación en el que un agente busca un objetivo en particular y un oficial exige un soborno para realizar su trabajo. Otras líneas de investigación analizan como las estructuras sociales pueden originar una sociedad corrupta. En este trabajo, combino la aproximación individualista de la teoría económica con el enfoque más social de otras ciencias sociales como la sociología, las ciencias políticas y la administración de empresas. Esto me permite estudiar la corrupción y el papel de las agencias de monitoreo cuando los actos corruptos se desarrollan bajo una estructura de redes sociales. El objetivo es comprobar que si la red es completa (i.e. no exhibe ninguna brecha), la coordinación entre agentes es más fluída y es más sencillo alcanzar objetivos corruptos. Además, cuando se incluye a las agencias de monitoreo, es fundamental que las instituciones y estas agencias complementen sus labores para prevenir el acto de corrupción de una manera más efectiva. Para ello, combino dos teorías sobre el comportamiento humano: la teoría de redes sociales y la teoría de juegos. Después de estudiar cuatro posibles escenarios, dos sin incluir las agencias de monitoreo y dos incluyéndolas, demuestro que la hipótesis planteada no puede ser descartada. Inclusive, si la penalidad que castiga el comportamiento corrupto es baja (o el costo administrativo de la justicia es alto) entonces los agentes preferirán involucrarse en actividades corruptas, sobre todo cuando la red es completa. Por el contrario, si la honestidad es altamente recompensada, los agentes tienen incentivos suficientes para no aceptar propuestas corruptas. La existencia de agencias de monitoreo, representadas como medios de comunicación libres, pueden desmotivar la corrupción desde el inicio. ; Many studies show that corruption is not only a bargaining problem between an agent that has an specific objective and an official that demand a bribe to do her job. Other lines of research analyzes how social structures are the origin of a corrupt society. In this work I combine the individualistic approach of economic theory with the social view of other social sciences such as sociology, political sciences and business administration to study corruption and the role of monitoring agencies when corruption takes place in a network structure. I will try to prove that if the social network is complete (i.e. it exhibits no gaps) then the coordination among agents is more fluent and it is easier to achieve corrupt objectives. On the other hand, institutions can generate incentives even if the network is complete. When monitoring agencies are included, it is important for institutions and monitoring agencies to complement each other to prevent more effectively the act of corruption. For this, I combine two theories on human behavior: social network theory and game theory. After studying four possible settings, two without monitoring agencies and two including them, I find that the hypothesis cannot be ruled out. Moreover, if the penalty for corrupt behavior is low (or the administrative cost of justice is high) then agents will prefer to get involved in corrupt activities, specially if the network is complete. On the other hand, if honesty is highly rewarded, agents have enough incentives to reject corrupt proposals. The existence of monitoring agencies represented as free press can discourage corruption from the beginning.

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Important aspects of social networking analysis are covered in this work by combining experimental and theoretical research. A specific focus is devoted to emerging trends and the industry needs associated with utilizing data mining techniques. Some of the techniques covered include data mining advances in the discovery and analysis of communities, in the personalization of solitary activities (like searches) and social activities (like discovering potential friends), in the analysis of user behavior in open fora (like conventional sites, blogs and fora) and in commercial platforms (like e-auctions), and in the associated security and privacy-preservation challenges, as well as social network modeling, scalable, customizable social network infrastructure construction, and the identification and discovery of dynamic growth and evolution patterns using machine learning approaches or multi-agent based simulation. These topics will be of interest to practitioners and researchers alike in this dynamic and growing field.

2.3 Brief History of ERGMs2.4 Network Data Amenable to ERGMs; 3 Formation of Social Network Structure; 3.1 Tie Formation: Emergence of Structure; 3.1.1 Formation of Social Ties; 3.1.2 Network Configurations: Consequential Network Patterns and Related Processes; 3.1.3 Local Network Processes; 3.1.4 Dependency (and Theories of Network Dependence); 3.1.5 Complex Combination of Multiple and Nested Social Processes; 3.2 Framework for Explanations of Tie Formation; 3.2.1 Network Self-Organization; 3.2.2 Individual Attributes; 3.2.3 Exogenous Contextual Factors: Dyadic Covariates.