The science of Design of Experiments (DOE) has been around for over a century, pioneered by R.A. Fisher for agricultural studies. Many of the classic experiment designs can be used in simulation studies. However, computational experiments have far fewer restrictions than do real-world experiments, in terms of costs, number of factors, time required, ability to replicate, ability to automate, etc. Consequently, a framework specifically oriented toward large-scale simulation experiments is warranted.
People have been conducting computational experiments for as long as computers have been around. The term “data farming” is more recent, coined in 19984 in conjunction with the Marine Corp's Project Albert,5 in which small agent-based distillation models (a type of stochastic simulation) were created to capture specific military challenges. These models were run thousands or millions of times at the Maui High Performance Computer Center6 and other facilities. Project Albert analysts would work with the military subject matter experts to refine the models and interpret the results.
Initially, the use of brute-force full factorial (gridded) designs meant that the simulations needed to run very quickly and the studies required high-performance computing. Even so, only a small number of factors (at a limited number of levels) could be investigated, due to the curse of dimensionality.
The SEED Center for Data Farming7 at the Naval Postgraduate School8 also worked closely with Project Albert in model generation, output analysis, and the creation of new experimental designs to better leverage the computing capabilities at Maui and other facilities. Recent breakthroughs in designs specifically developed for data farming can be found in910 among others.
A series of international data farming workshops have been held since 1998 by the SEED Center for Data Farming.11 International Data Farming Workshop 1 occurred in 1991, and since then 16 more workshops have taken place. The workshops have seen a diverse array of representation from participating countries, such as Canada, Singapore, Mexico, Turkey, and the United States.12
The International Data Farming Workshops operate through collaboration between various teams of experts. The most recent workshop held in 2008 saw over 100 teams participating. The teams of data farmers are assigned a specific area of study, such as robotics, homeland security, and disaster relief. Different forms of data farming are experimented with and utilized by each group, such as the Pythagoras ABM, the Logistics Battle Command model, and the agent-based sensor effector model (ABSEM).13
Lucas, T. W.; Kelton, W. D.; Sanchez, P. J.; Sanchez, S. M.; Anderson, B. L. (2015). "Changing the Paradigm: Simulation, Now a Method of First Resort". Naval Research Logistics. 62 (4): 293–305. doi:10.1002/nav.21628. hdl:10945/57859. S2CID 60846350. /wiki/Susan_M._Sanchez ↩
https://www.cso.nato.int/Pubs/rdp.asp?RDP=STO-TR-MSG-088 [bare URL] https://www.cso.nato.int/Pubs/rdp.asp?RDP=STO-TR-MSG-088 ↩
"Data Farming". Archived from the original on 2015-08-29. Retrieved 2014-04-22. https://web.archive.org/web/20150829212237/http://www.datafarming.org/Data_Farming/Welcome.html ↩
Brandstein, A.; Horne, G. (1998). "Data Farming: A Meta-Technique for Research in the 21st Century". Maneuver Warfare Science. Quantico, VA: Marine Corps Combat Development Command. ↩
http://projectalbert.org [bare URL] http://projectalbert.org ↩
https://www.mhpcc.hpc.mil/ [bare URL] https://www.mhpcc.hpc.mil/ ↩
http://harvest.nps.edu [bare URL] http://harvest.nps.edu ↩
http://www.nps.edu/ [bare URL] http://www.nps.edu/ ↩
Kleijnen, J. P. C.; Sanchez, S. M.; Lucas, T. W.; Cioppa, T. M. (2005). "A User's Guide to the Brave New World of Designing Simulation Experiments". INFORMS Journal on Computing. 17 (3): 263–289. doi:10.1287/ijoc.1050.0136. /wiki/Susan_M._Sanchez ↩
Sanchez, S. M.; Sanchez, P.; Wan, H. (2021). "Work Smarter, Not Harder: A Tutorial on Designing and ConductingSimulation Experiments" (PDF). 2021 Winter Simulation Conference (WSC). Piscataway, NJ: Institute of Electrical and Electronics Engineers, Inc. pp. 1–15. doi:10.1109/WSC52266.2021.9715422. hdl:10945/44883. ISBN 9780903440660. S2CID 247059747. 9780903440660 ↩
Horne, G., & Schwierz, K. (2008). Data farming around the world overview. Paper presented at the 1442-1447. doi:10.1109/WSC.2008.4736222 ↩