Sentient World Simulation: full concept paper transcription

Sentient World Simulation (SWS): A Continuously Running Model of the Real World

A Concept Paper for Comments

Government POC: Tony Cerri, Anthony.Cerri@je.jfcom.mil, JFCOM J9, Experimentation Engineering Lead

Technical POC: Dr. Alok Chaturvedi, alok@purdue.edu, Purdue University, West Lafayette, IN 47907

Version 2.0, August 22, 2006

Modeling and simulation quickly becomes out of sync with new events, the emergence of new forces, and newly proposed theories. The goal of the Sentient World Simulation (SWS) is to build a synthetic mirror of the real world with automated continuous calibration with respect to current real-world information, such as major events, opinion polls, demographic statistics, economic reports, and shifts in trends. The ability of a synthetic model of the real world to sense, adapt, and react to real events distinguishes SWS from the traditional approach of constructing a simulation to illustrate a phenomena. Behaviors emerge in the SWS mirror world and are observed much as they are observed in the real world.

SWS consists of components capable of capturing new events as they occur anywhere in the world... the set of models that make up the synthetic environment encompass the behavior of individuals, organizations, institutions, infrastructures and geographies while simultaneously capturing the trends emerging from the interaction among entities as well as between entities and the environment.

The core component of SWS is an agent-based environment named the Synthetic Environment for Analysis and Simulation (SEAS), designed to be agnostic to the type of simulations and choice of models in order to allow experimentation in the context of multiple and potentially conflicting theories.

The basic building blocks consist of individuals, organizations, institutions, information, and geographies. From these building blocks, communities are built, constructed into societies, nations, and ultimately providing a synthetic world. Coarse-grained models use a small sample of the population and localized fine-grained models use a full sample of the population in an area. The same individual who experiences events from a fine-grained model can be influenced by a coarse-grained model.

SEAS provides all aspects of behavior within the PMESII categorization and enables the application of Effects Based Thinking to the reference world of SWS.

Burgeoning technology in the area of knowledge discovery has matured so that Web crawlers and spiders are now used in research and industry. Applying this technology to news portals, blogs, and other internet sources enables large amounts of data to be gathered and processed in a short amount of time.

Other sources of data besides the internet are incorporated using the same knowledge discover and semantic extraction, such as proprietary, enterprise system, and classified data sources (classified and proprietary data would only be incorporated in select excursions.)

JFCOM showcased the first prototype of SWS, SWS v0.5, in the Urban Resolve 2015 (UR 2015) exercise. UR 2015 was set in a synthetic urban setting and consisted of both simulation and real-time HITL [human-in-the-loop] inputs. SWS 0.5 consisted of SEAS environment, a Society of Simulations and Systems implemented through SimBridge, a deployment of xNA with knowledge from a classified repository and data from research on public data, and an Excursion Manager.

A repository populated by analysts' understanding of Operational Net Assessment (ONA) is matched with corresponding entities in xNA, enabling SEAS to draw from classified ONA data to conduct certain classified excursions in UR2015.

TACSIM used statistics such as public opinion of Blue (one side in the multiplayer war game) to determine the willingness of the population in a city sector to provide intelligence reports.

SWS provides an environment for testing Psychological Operations (PSYOP) and Civil Affairs activities, capable of illustrating the impact of these activities on populations. Commercial users can construct experiments to use proprietary data in a controlled environment.

Augmenting real-time information with near real-time and faster than real-time simulations allows RCC to develop and test multiple courses of action to anticipate and shape behaviors of adversaries, neutrals, and partners.

SWS will provide the ability to develop and assess DIME campaigns.