Date of Graduation

5-2013

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level

Graduate

Department

Industrial Engineering

Advisor

Edward A. Pohl

Committee Member

Justin Chimka

Second Committee Member

Jose Ramirez-Marquez

Third Committee Member

Chase Rainwater

Keywords

Social sciences; Applied sciences; Actor interactions; Communication paths; Information dissemination; Reliability analysis; Social networks

Abstract

The primary focus of this dissertation is on the quantification of actor interaction and the dissemination of information through Social networks. Social networks have long been used to model the interactions between people in various Social and professional contexts. These networks allow for the explicit modeling of the complex interrelations between relevant individuals within an organization and the role they play in the decision making process. This dissertation considers Social networks represented as network flow models in which actors have the ability to provide some level of influence over other actors within the network. The models developed incorporate performance metrics and reliability analysis established in the multi-state reliability literature to gain insights into organizational behavior.

After a brief introduction, Chapter 2 provides a survey of the relevant literature on several topics of interest within this dissertation. In Chapter 3, actor criticality findings using traditional Social network analysis are compared to those obtained via multi-state reliability importance measures. Chapter 4 extends the model developed in Chapter 3 to consider that an actor's Social interaction and level of influence within the organization are not only multi-valued and stochastic in nature but also a function of the interactions with its neighbors. A Monte Carlo simulation model is presented to evaluate the reliability of the network, and network reliability is evaluated under various influence communication rules. In Chapter 5, a hierarchical network structure is investigated where actors are arranged in layers and communication exists between layers. A probability mass function is developed to compute the expected level of influence at the target nodes as a function of the existing communication paths within the network. An illustrative example is used to demonstrate the effects on expected influence at the target as connections are either added or removed and when the uncertainty associated with an actor's influence level is removed. Finally, in Chapter 6, a methodology is developed for eliciting the probabilities associated with the influence levels used in the network analysis of Chapters 3 - 5.

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