Pythagoras is a multi-sided agent-based model (ABM) created to support the growth and refinement of the U.S. Marine Corps Warfighting Laboratory's's Project Albert.
As Pythagoras has grown in capability, it has been applied to a wide variety of tactical, operational and campaign-level topics in conventional and irregular warfare.
This simple scenario was constructed by one analyst in about two hours, and illustrates both the ease of use and the applicability of Pythagoras to many combat and non-combat analysis situations.
This scenario was used to examine alternatives for convoy protection, such as IED Agent-based simulations create software entities that are capable of responding to their perceived or actual situations based upon sets of decision rules.
Pythagoras introduces new capabilities to modeling and simulation, such as “soft” decision rules, dynamic affiliation, behavior-change triggers, and non-lethal weapons effects.
It has been used to study tactics, techniques and procedures in response to a weapon of mass destruction attack on a military installation.
It is currently being used to support two different studies (one by Northrop Grumman and the other by students at the Naval Postgraduate School) of population dynamics in areas of the world where an insurgency is possible and the Marines are sent in to provide disaster relief after an earthquake.
It is particularly suitable for data farming — executing large numbers of repetitions of parametric runs to identify areas of unexpected behaviors and nonlinear results in a coevolving landscape.
Pythagoras originally began as a method by which the existing US Marine Corps-provided Archimedes model could be enhanced, modified, or controlled to enable it to run large problem sets on multiple platforms and be analyzed via data farming techniques on the Gilgamesh platform located at the Maui High Performance Computing Center (MHPCC).