Data farming around the world overview <s> INTRODUCTION <s> When should one engage the enemy directly and when should one maneuver? Of course, the answer depends on the circumstances, and the simulations presented in this paper are simply illustrative. The purpose is to describe one aspect of an on-going Marine Corps research program called Project Albert. This aspect is our attempt to capture, i.e. distill, the essence of a subject in as simple a simulation as possible. We don't even call our models "simulations", preferring the word "distillations". Because they are by nature simple, we can run them many times to try to gain insight into our questions, and we have the ability to grow even more data to explore interesting regions of output, a process we call "data farming". The process of looking at many manifestations of an operation and then trying to build a deeper understanding of the whole structure is a sample of what we call "operational synthesis". A contrasting approach would be to increase the verisimilitude up to the limits of computing power and run a small number of iterations. Many current military application efforts are in line with this contrasting approach and yet don't seem to meet the challenges required to answer our questions. <s> BIB001 </s> Data farming around the world overview <s> INTRODUCTION <s> from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 1 Workshop 13 <s> BIB002 </s> Data farming around the world overview <s> INTRODUCTION <s> from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 2 Workshop 14 <s> BIB003
Data Farming was first introduced to the Winter Simulation community at the 1999 Winter Simulation Conference in Phoenix BIB001 . The ideas behind Data Farming had been initially developed much earlier, but were introduced by Dr. Horne to the defense community in 1997  in concert with the combination of agentbased models with high performance computing that was the start of Project Albert. Project Albert was a congressionally funded modeling and simulation initiative of the United States Marine Corps (USMC) motivated by the fact that complex adaptive systems are pervasive in USMC operations. The philosophy of Project Albert was to pair simple, efficient, abstract models with high performance computing to explore large design spaces. When these models and high performance computing are combined with efficient experimental designs developed in work pioneered at NPS (e.g. see  , a huge sample space can be explored very rapidly. And when rapid prototyping capabilities and collaborative environments are introduced into the Data Farming process, progress on questions, even long-standing and difficult questions involving many interacting variables, is possible. Project Albert used what are referred to as agent-based distillation models. These are a type of computer simulation which attempts to model the critical factors of interest in combat without explicitly modeling all of the physical details. Some of the models used in Project Albert were MANA, PAX, and Pythagoras, all agent-based models, although the methods developed can be applied using any type of simulation model. These models continue to be developed and recent updates are described in  , BIB003 , and BIB002 respectively. But agent-based models are small and abstract and can easily be run many times to test a variety of parameter values and get an idea of the landscape of possibilities. The term distillation is added, because the intent is to distill the question at hand down into as simple a representation as possible. Also, models used in Project Albert were specifically developed and used because the capability to rapidly prototype scenarios is very important in the process. Although Project Albert was a US sponsored effort, it had a strong spirit of international collaboration which made possible a great deal of cooperative effort among researchers around the world. For example, the three models mentioned in the paragraph above were developed in New Zealand, Germany, and the US respectively. And the fact that Project Albert was question-based also allowed practitioners from around the world to rally around the developing Data Farming methodologies because of the impact upon their shared application interests. Because many of the questions of interest have wide applicability, the work teams at international workshops typically consist of representatives from two or three and sometimes up to six different countries and the workshops overall have usually consisted of about 8 to 10 teams. The first international workshop was organized in 1999 and 12 workshops were held under the auspices of Project Albert which ended in September 2006. Since that time the international Data Farming community has continued coopera-tive efforts and the most recent workshops, the 16 th and 17 th workshops, were held in 2008. Our presentation here will highlight these most recent workshops, but details on the other workshops and other Data Farming efforts can be found at http://harvest.nps.edu, the website of the SEED (Simulation Experiments and Efficient Designs) Center for Data Farming at the United States Naval Postgraduate School.