By deeply integrating diverse disciplines, research converges upon solutions to complex challenges facing society today. Convergence can be difficult to achieve, given current cultural and institutional roadblocks that have created the silos of disciplinary structures. Complexity Leadership Theory (CLT) provides a framework to re-conceptualize knowledge-producing organizations whose desired outcomes are learning, adaptability, and innovation. Instead of focusing on individual behaviors, CLT focuses on the interactive dynamics of the collective.
Guided by the CLT framework and led by our co-PI Gemma Jiang, we are implementing complexity-sensitive developmental evaluation (DE) methodologies. We are exploring emergent behaviors in complex, dynamic social networks to initiate and sustain convergent research; evaluate the influence of social networks on attitudes of individual members; capture the complexities of a multifaceted experience, and provide a useful case for transdisciplinary research communities. We anticipate that our research network will evolve to a more robust state; our ability to monitor this progress will enable effective intervention to strengthen nascent initial connections.
The DE plan includes social network analysis. This method provides network visualization and metrics to aid in diagnostics of the overall network structure of the community and each individual. An increasingly robust network is evidence for convergence. Social network analysis examines relationship patterns among interacting and interdependent agents. . Social network analysis with ORA returns network visualizations, as well as network- and agent-level metrics (see figure below). These results can be used for performing diagnostics and devising evidence-based intervention.
By examining network structures as a whole, we can understand how well information and ideas are flowing through the organization and make changes. If we identify disconnection between major disciplinary groups, we can design strategies to connect them. Agent-level network measures provide insights on each individual’s level of integration into the network. In addition, we are evaluating the effects of network dynamics on desired outcomes, such as research progress. We expect to see the network structures among our team to become more robust. The evolution of network structures will provide convincing evidence for convergence in our approaches. We also expect individuals already at strategic positions to become more capable of taking advantage of their network positions, and individuals in peripheral positions to become more integrated as we apply networked