Data farming: what it is, and why you need it!

Abstract

Simulation models are integral to modern scientific research, national defense, industry and manufacturing, and public policy debates. These models tend to be extremely complicated, often with large numbers of factors and many sources of uncertainty, but recent breakthroughs help analysts deal with this complexity. Data farming is a descriptive metaphor that captures the notion of generating data purposefully in order to maximize the information “yield” from simulation models. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, uncover interesting features of complex simulation response surfaces, and explicitly identify cause-and-effect relationships. Data farming has been used in the defense community for over a decade, and has resulted in quantum leaps in the breadth, depth, and timeliness of the insights yielded by simulation models.