The Structure and Dynamics of Large Scale Organizational and Engineering Networks
My research in the emerging area of large-scale engineering and economic systems aims at better understanding and improving the performance and diffusion dynamics of large-scale decentralized organizations. Of particular interest to me is the strategically important organizational system: distributed problem solving, and large-scale product design and development.
Distributed problem solving, which often involves an intricate network of interconnected tasks carried out by hundreds of designers, is fundamental to the creation of complex manmade systems. The structure and dynamics of the interconnections within the problem solving process can have a significant effect on the efficiency, effectiveness and profits of the organization. In my research, I have embarked on a new area of engineering and management science research whose main objective is to characterize the real-world structure, and eventually the dynamics of complex organizational networks. This research could aid decision-makers in coping with complexity, developing operational strategies, and improving the decision making process.
In recent years, understanding the structure and function of complex networks has become the foundation for explaining many different real-world complex biological, technological and informal social phenomena. Techniques from statistical physics have been successfully applied to the analysis of these networks, and have uncovered surprising statistical structural properties that have also been shown to have a major effect on their functionality, dynamics, robustness, and fragility.
I have shown that the structure of information flow networks that are at the heart of large-scale problem solving efforts have properties that are similar to those displayed by other social, biological and technological networks. In this context, I have further identified novel properties that may be characteristic of other information-carrying networks. A detailed model and analysis of problem solving dynamics on complex networks has been developed, and it has been shown how the underlying network topologies provide direct information about the characteristics of this dynamics. Applications of the new analysis methodology and techniques introduced in this work include innovation diffusion and technology adoption, contagion and cascades on complex networks, epidemiology, and information transmission. The theory is also relevant to understanding various social phenomena on complex social networks including cultural change, fads, fashion, norms, and customs, among others.
Network of information flows between tasks of a vehicle, operating system, and hospital building large-scale design. These task networks consist of enormous number of directed information flows between hundreds of development tasks. Each task is assigned to one or more actors ("design teams," "engineers," or "scientists") who are responsible for it.
Large-scale design networks exhibit a noticeable asymmetry between the distributions of incoming and outgoing information flows, suggesting that the incoming capacities of tasks are much more limited than their counterpart outgoing capacities. The cut-offs observed in the in-degree and out-degree distributions might reflect Herbert Simon’s notion of bounded rationality, and its extension to group-level information processing.
The distinctive asymmetry between the distributions of incoming and outgoing information flows (links) of large-scale design networks has implications for their functionality, sensitivity, and robustness (error tolerance) properties.