Data farming coevolutionary dynamics in repast

This paper describes the application of data farming techniques (Brandstein and Home 1998) to explore various aspects of revolutionary dynamics (McKelvey 2002) in organization science. Data farming is an iterative process using high-performance computing to execute and vary agent-based models, collect and explore statistical results, and integrate these results for the purposes of growing more data by virtue of generative analysis. The tool of choice for creating these agent-based models is the University of Chicago's Social Science Research Computing's (2004) Recursive Porous Agent Simulation Toolkit (Re-Past). The paper concludes with a brief description of Tivnan's (2004) Coevolutionary model of boundary-spanning Agents and Strategic Networks (C-BASN), an extension of Hazy and Tivnan's (2004) Model of Organization, Structural Emergence, and Sustainability (MOSES).