Estimating the near-term changes of an organization with simulations

Abstract

Estimating the changes of an organization's performance under uncertainty has been one of major topics in management, counter-terrorism, command and control, etc. In this paper, we propose a data-farming framework, 'Near- Term Analysis', to predict the changes over time in network. Near-Term Analysis uses Dynamic Network Analysis metrics (in ORA) for estimating changes in a Multi-Agent Simulation model of social and knowledge network evolution (called Dynet). Specifically, Near-Term Analysis simulates the social dynamics within an organization based on an organization's meta-matrix and expected isolation events of agents, and it generates its estimation about the degree of knowledge diffusion from the simulation over the simulated time period. From this analysis, we found this tool is useful in detecting inefficient entities in organization structures and expecting the impacts of the loss of agents. Furthermore, the simulation result correlated with the social network analysis measures qualitatively. We believe that this framework can be used to detect the vulnerabilities of terrorist groups, military command and control structures, corporate structures, etc.