Boundary spanning in organizational learning: Preliminary computational explorations
Institute for the Study of Convergence and Emergence Managing the Complex IV Conference, Fort Myers, FL, , , 2002
Abstract: Using the computational technique of agent-based modeling, we examined the ways boundary-spanning agents influence an organization’s learning capability under various environmental conditions. We propose a model wherein an organization is represented as a network of connected agents, tasks, resources and knowledge (Krackhardt & Carley 1998). Boundary spanning agents searched for and retrieved new information from across the organizational boundary (Richardson & Lissack, 2001) and returned to apply the information as task knowledge and to diffuse it throughout the organization. The Organizational Learning Systems Model (OLSM)(Schwandt 1997) was the basis for measuring outcomes at the organization level. We found that:(a) the number of boundary spanning agents was a predictor of collective survival in changing environments and (b) production was higher and fewer boundary spanners were necessary for higher levels of output when agents learned new tasks. We conclude with a discussion of the broad potential for this model and these computational techniques.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).