In this thesis I inquire how an interdependent relationship is perceived to affect virtual team member’s interaction and the process of developing knowledge in the team. In order to explore these issues a qualitative case study was conducted and data gathered through the subjective experiences of team members constituting a virtual team in Statoil through the following research question: How is the interdependent relationship between virtual team members perceived to affect interaction and the process of developing knowledge in the team? In this thesis an interdependent relationship is understood as team members relating to each other as individuals that are mutually dependent on and responsible for the team’s actions. This interdependent relationship is the fundament for interaction in which team members build on and refine each other’s ideas and knowledge in order to reach their common goals and objectives. Principles from dialogue techniques, by the concepts of perspective making and perspective taking, are elaborated as a means to support interdependent interaction and knowledge creation in the virtual team. The empirical findings in this particular case study suggest that the informants perceive their interdependent and technologically mediated relationship to represent both challenges and possibilities in relation to their interaction and the process of developing knowledge within the team. Further, acknowledging this interdependent relationship and having the capacity to take the other’s perspective, seems decisive in order to develop shared understanding, complementary knowledge and high-quality decisions in the virtual team. The main findings in this study are: The interdependent relationship between the virtual team members is perceived characterized by involvement, vulnerability, power and shared responsibility Trust is seen as a vital precondition for interaction between the interdependent virtual team members Developing a shared situational understanding through listening to other’s perspectives seems crucial in order to utilize the potential for developing knowledge in the virtual team

This thesis utilizes statistical physics concepts and mathematical modeling to study complex systems. I investigate the emergent complexities in two systems: (i) the stock volume volatility in the United States stock market system; (ii) the robustness of networks in an interdependent lattice network system. In Part I, I analyze the United States stock market data to identify how several financial factors significantly affect scaling properties of volume volatility time intervals. I study the daily trading volume volatility time intervals between two successive volume volatilities above a certain threshold q, and find a range of power law distributions. I also study the relations between the form of these distribution functions and several financial factors: stock lifetimes, market capitalization, volume, and trading value. I find that volume volatility time intervals are short-term correlated. I also find that the daily volume volatility shows a stronger long-term correlation for sequences of longer lifetimes. In Part II, I apply percolation theory to interacting complex networks. The dependency links between the two square lattice networks have a typical length r lattice units. For two nodes connecting by a dependency link, one node fails once the node on which it depends in the other network fails. I show that rich phase transition phenomena exist when the length of the dependency links r changes. The results suggest that percolation for small r is a second-order transition, and for larger r is a first-order transition. The study suggests that interdependent infrastructures embedded in two-dimensional space become most vulnerable when the interdependent distance is in the intermediate range, which is much smaller than the size of the system.

The present study examined the effects of adult attachment and cultural orientation on sacrifice behaviors and the corresponding emotional reactions, using a cross-cultural sample in the U.S. and China. Strain-tests protocol was utilized in this study, in which an individual (i.e., the asker) was asked to share with their romantic partner a personal goal that required a major sacrifice from their partner (i.e., the responder), and then entered a discussion to process their plan of carrying out this goal. The final sample included 115 couples from the U.S. and 99 couples from China. Results indicated that responder's attachment avoidance was negatively associated with sacrifice offered to their partners and they reported more positive emotions as a result. The interaction between attachment anxiety and nationality was significant. Specifically, in the U.S. sample, individuals with high attachment anxiety were more likely to offer sacrifice for their partners, but this effect was reversed in the Chinese sample. In addition, interdependent self-construal (ISC) was a significant moderator for the relation between attachment anxiety and sacrifice behavior. Individuals who endorsed higher ISC and higher anxious attachment were less likely to make sacrifice for their partner. Furthermore, when individual offered more sacrifice, they reported more positive emotions in general, but this effect was stronger in the Chinese sample than the U.S. sample. In addition, it was found that responders who endorsed higher ISC offered more sacrifice to their partner when asked to. These results advanced our understanding of the complex role of adult attachment in negotiating situations when partners in romantic relationships have different goals, as well as possible cultural differences in the expression of the attachment influences. The findings also highlighted the systemic perspective in understanding the roles of both partners' individual traits (i.e., attachment) and cultural values (i.e., interdependent self-construal) on their behaviors (i.e., sacrifice) in romantic relationships. Counseling implications, limitations, and future research directions were discussed.

Complex networks appear in almost every aspect of our daily life and are widely studied in the fields of physics, mathematics, finance, biology and computer science. This work utilizes percolation theory in statistical physics to explore the percolation properties of complex networks and develops a reinforcement scheme on improving network resilience. This dissertation covers two major parts of my Ph.D. research on complex networks: i) probe—in the context of both traditional percolation and k-core percolation—the resilience of complex networks with tunable degree distributions or directed dependency links under random, localized or targeted attacks; ii) develop and propose a reinforcement scheme to eradicate catastrophic collapses that occur very often in interdependent networks. We first use generating function and probabilistic methods to obtain analytical solutions to percolation properties of interest, such as the giant component size and the critical occupation probability. We study uncorrelated random networks with Poisson, bi-Poisson, power-law, and Kronecker-delta degree distributions and construct those networks which are based on the configuration model. The computer simulation results show remarkable agreement with theoretical predictions. We discover an increase of network robustness as the degree distribution broadens and a decrease of network robustness as directed dependency links come into play under random attacks. We also find that targeted attacks exert the biggest damage to the structure of both single and interdependent networks in k-core percolation. To strengthen the resilience of interdependent networks, we develop and propose a reinforcement strategy and obtain the critical amount of reinforced nodes analytically for interdependent Erdős-Rényi networks and numerically for scale-free and for random regular networks. Our mechanism leads to improvement of network stability of the West U.S. power grid. This dissertation provides us with a deeper understanding of the effects of structural features on network stability and fresher insights into designing resilient interdependent infrastructure networks.

In the course of figuring out one’s identity, the proactive manner in which an individual defines one’s self or builds one’s character is an important aspect of the venture. Markus and Kitayama (1991) described and contrasted interdependent self-construal and independent self-construal, two forms of self-construction. This paper presents the theoretical link between interdependent self-construal and psychological resilience. Identity development is viewed through the interdependent self-construal perspective and it is explained how interdependence provides an individual with more and better opportunities for identity development. Through a conceptual tie to self-compassion, interdependent self-construal may be a key component to experiencing psychological resilience.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Don M. Gruenbacher / Caterina M. Scoglio / As today's networks are no longer individual networks, networks are less robust towards failures and attacks. For example, computer networks and power networks are interdependent. Computer networks provide smart control for power networks, while power networks provide power supply. Localized network failures and attacks are amplified and exacerbated back and forth between two networks due to their interdependencies. This dissertation focuses on finding solutions to enhance network robustness. Software-defined networking provides a programmable architecture, which can dynamically adapt to any changes and can reduce the complexities of network traffic management. This architecture brings opportunities to enhance network robustness, for example, adapting to network changes, routing traffic bypassing malfunction devices, dropping malicious flows, etc. However, as SDN is rapidly proceeding from vision to reality, the SDN architecture itself might be exposed to some robustness threats. Especially, the SDN control plane is tremendously attractive to attackers, since it is the "brain" of entire networks. Thus, researching on network robustness helps protect network from a destructive disaster. In this dissertation, we first build a novel, realistic interdependent network framework to model cyber-physical networks. We allocate dependency links under a limited budget and evaluate network robustness. We further revise a network flow algorithm and find solutions to obtain a basic robust network structure. Extensive simulations on random networks and real networks show that our deployment method produces topologies that are more robust than the ones obtained by other deployment techniques. Second, we tackle middlebox chain problems using SDN. In computer networks, applications require traffic to sequence through multiple types of middleboxes to accomplish network functionality. Middlebox policies, numerous applications' requirements, and resource allocations complicate network management. Furthermore, middlebox failures can affect network robustness. We formulate a mixed-integer linear programming problem to achieve a network load-balancing objective in the context of middlebox policy chain routing. Our global routing approach manages network resources efficiently by simplifying candidate-path selections, balancing the entire network and using the simulated annealing algorithm. Moreover, in case of middlebox failures, we design a fast rerouting mechanism by exploiting the remaining link and middlebox resources locally. We implement proposed routing approaches on a Mininet testbed and evaluate experiments' scalability, assessing the effectiveness of the approaches. Third, we build an adversary model to describe in detail how to launch distributed denial of service (DDoS) attacks to overwhelm the SDN controller. Then we discuss possible defense mechanisms to protect the controller from DDoS attacks. We implement a successful DDoS attack and our defense mechanism on the Mininet testbed to demonstrate its feasibility in the real world. In summary, we vertically dive into enhancing network robustness by constructing a topological framework, making routing decisions, and protecting the SDN controller.

The cyber-physical systems (CPS), such as smart grid and intelligent transportation system, permeate into our modern societies recently. The infrastructures in such systems are closely interconnected and related, e.g., the intelligent transportation system is based on the reliable communication system, which requires the stable electricity provided by power grid for the proper function. We call such mutually related systems interdependent networks. This thesis addresses the cascading failure issue in interdependent cyber physical system. We consider CPS as a system that consists of physical-resource and computational-resource networks. The failure in physical-resource network might cause the failures in computational-resource network, and vice versa. This failure may recursively occur and cause a sequence of failures in both networks. In this thesis, we propose two novel interdependence models that better capture the interdependent networks. Then, we study the effect of cascading failures using percolation theory and present the detailed mathematical analysis on failure propagation in the system. By calculating the size of functioning parts in both networks, we analyze the robustness of our models against the random attacks and failures. The cascading failures in smart grid is also investigated, where two types of cascading failures are mixed. We estimate how the node tolerance parameter T (ratio of capacity to initial workload) affect the system performance. This thesis also explores the small clusters. We give insightful views on small cluster in interdependent networks, under different interdependence models and network topologies.

Layered security systems pose significant challenges while attempting to monitor security related activities. The varying attributes embedded within each layer as well as the attribute interdependencies within and across layers takes measurement complexity to an exponential state. The many interdependencies at play in an interconnected infrastructure further exacerbates the ability to measure overall security assurance. Then enters the patient attacker who infiltrates one layer of this security system and waits for the opportune time to infiltrate another layer. The ability to simulate and understand risk with respect to time in this dynamic environment is critical to the decision maker who must work under time and cost constraints. This thesis seeks to improve methods for interdependent risk assessment particularly when a patient attacker is present.

No description available.

Stochastic and deterministic approaches for modeling complex networks are presented. The methodology combines analysis of the structure formed by the interconnections among the elements of a network with an assessment of the vulnerability towards the propagation of cascading failures. The goal is to understand the mutual interplay between the structure of the network connections and the propagation of cascading failures. Two fundamental issues related to the optimal design and operation of complex networks are addressed. The first concerns the impact that cascading failures have on networks due to the connectivity pattern linking their components. If the state of load on the network components is high, the risk of cascade spreadings becomes significant. In this case, the needed reduction of the connectivity efficiency to prevent the propagation of failures affecting the entire system is quantified. The second issue concerns the realization of the most efficient connectivity in a network that minimizes the propagations of cascading failures. It is found that a system that routinely approaches the critical load for the onset of cascading failures during its operation should have a larger efficiency value. This allows for a smoother transition to the cascade region and for a reasonable reaction time to counteract the onset of significant cascading failures. The interplay between the structure of the network connections and the propagation of cascading failures is assessed also in interdependent networks. In these systems, the linking among several network infrastructures is necessary for their optimal and economical operation. Yet, the interdependencies introduce weaknesses due to the fact that failures may cascade from one system to other interdependent systems, possibly affecting their overall functioning. Inspired by the global efficiency, a measure of the communication capabilities among interdependent systems, i.e. the interdependency efficiency, is defined. The relations between the structural parameters, i.e. the system links and the interdependency links, and the interdependency efficiency, are also quantified, as well as the relations between the structural parameters and the vulnerability towards the propagation of cascading failures. Resorting to this knowledge, the optimal interdependency connectivity is identified. Similar to the spreading of failures, the formation of a giant component is a critical phenomenon emerging as a result of the connectivity pattern in a network. This structural transition is exploited to identify the formation of macrometastases in the developed model for metastatic colonization in tumor growth. The methods of network theory proves particularly suitable to reproduce the local interactions among tumor cells that lead to the emergent global behavior of the metastasis as a community. This model for intercellular sensing reproduces the stepwise behavior characteristic of metastatic colonization. Moreover, it prompts the consideration of a curative intervention that hinders intercellular communication, even in the presence of a significant tumor cell population. / Ph. D.