CAESAR III, Temper, Pythia, Data Fusion Ptech AI wargame C2 process modeling

[Anti_Illuminati]

[lordssyndicate]

Indeed , you all have been exposed!

You plans to wage a war against the american people using the power grid and internet infrastructure have been exposed.

Perhaps if we have time I will add to this the rest of the  GMU documents that I have aquried thanks to Anti Illuminati.

Basically it's the rest of what Anti Illuminati posted here showing you everything you need to predict exactly how to commit perfect genocide  using massive super computers to help plan and execute all of it.

[Anti_Illuminati]

[Anti_Illuminati]

[Anti_Illuminati]

John Osterholz

Chair, Technical Council
Network Centric Operations Industry Consortium

Defense Department Will Require IPv6 Compliance, Says DoD's John Osterholz

Market Wire, June 2003

IPv6 SUMMIT -- John Osterholz, director of architecture and interoperability for the Department of Defense, told a gathering of technology elite that the DoD would phase out purchases of IPv4 network technologies by this fall and would instead begin trials of equipment and applications based on the new IPv6 protocol for the Internet within 30 days. He said the move was intended to build a "Global Information Grid" of Net-Centric operations that was fully distributed, available and secure. He noted that this would be an important part of fighting terrorism and ensuring homeland security.

"Al-Qaeda maintains a low profile and is highly distributed," noted Osterholz. "Until recently, we had no capability to operate similarly, and we understand it is an important capability. They were Net-Centric, we were not. Their command and control capability requires us to have a similar capability."

In his keynote, Osterholz laid out his plans for moving the entire DoD information technology infrastructure -- the world's largest, with an annual IT budget exceeding $30 billion -- into full IPv6 compliance by 2008. This represents an unprecedented move by the Defense Department to approach the entire commercial Internet infrastructure, which includes IPv6 Summit sponsors Cisco (NASDAQ: CSCO), Hewlett-Packard (NYSE: HP), Nokia (NYSE: NOK) and the Verio division of NT&T (NYSE: NTT), with detailed instructions on the networking standards it plans to support.

Historically, the DoD has created or commissioned vendors to build proprietary infrastructure. But the DoD's need for global, immediate access to secure, real-time information has moved the department from an infrastructure of data links between proprietary systems to a secure global enterprise built on the next generation of open systems. Osterholz called this system the Global Information Grid (GIG) and said one of its primary DoD uses will be "predictive battlespace awareness" that combines intelligence and operations technologies in a connected, real-time environment.

"Our soldiers need better information in order to make better decisions -- who to help and who to kill," continued Osterholz. "The lack of security and flexibility in the current IPv4 protocol is a drag on our wing. This isn't about do you trust the Internet for your kid's homework, it's do you trust your kid's life. If we fail, people die."

[Anti_Illuminati]

[Anti_Illuminati]

Hebrews 4:12   "For the word of God is quick, and powerful, and sharper than any twoedged sword, piercing even to the dividing asunder of soul and spirit, and of the joints and marrow, and is a discerner of the thoughts and intents of the heart." 

Revelation 1:18   "I am he that liveth, and was dead; and, behold, I am alive for evermore, Amen; and have the keys of hell and of death."

[Dig]

Hey Criminals that use towns, cities, communities as TESTING GROUNDS for your wargames...

GET THE F*CK OUT OF MY COUNTRY!!!!!!!!!!!

EVERYBODY KNOWS WHAT YOU ARE DOING

YOU TOTAL PIECES OF DOG SHIT!!!!!!!!!!!!!!!!!!!!!

[L2Design]

My brains going to explode.

Call-to-action... shall we print out and mail these traitors/our reps/senate/congress this?
Please call into the showwwwww.. we need to do something.

[voodo0]

My brains going to explode.

i feel the same way. 

[TheHouseMan]

I have a question to Anti_Illuminati: do you really think you've discovered something big, if it's publicly posted on the Internet?

[Joseon]

Very brief summary for the people, in more layman terms.


Basically, the Caesar II/EB(effect based) is an advanced system that centralise the infrastructure-decision making ability, of the action/reaction occurrence between the blue nodes(one faction) and the red nodes(adversary). This is all hooked into the information operation(IO) that occurs with the command and control center of military intelligence to create a Course of Action(CoA), then integrated to the Non (IO) military operations. This system was developed in George Mason University. After 2000, the DoD created various war games that would take into account 'effects' based missions. The Caesar II/EB system was developed during a time, no such effects based systems were present.  This new coordinated tool, created by George Mason, with the help of two S.Koreans, were tested in 4 major war games; That being Naval title X, Global 2001 and 2001, Joint Forces Command J-9, and Millennium Challenge2002.

If you need more, just ask.

[liko]

Very brief summary for the people, in more layman terms.


Basically, the Caesar II/EB(effect based) is an advanced system that centralise the infrastructure-decision making ability, of the action/reaction occurrence between the blue nodes(one faction) and the red nodes(adversary). This is all hooked into the information operation(IO) that occurs with the command and control center of military intelligence to create a Course of Action(CoA), then integrated to the Non (IO) military operations. This system was developed in George Mason University. After 2000, the DoD created various war games that would take into account 'effects' based missions. The Caesar II/EB system was developed during a time, no such effects based systems were present.  This new coordinated tool, created by George Mason, with the help of two S.Koreans, were tested in 4 major war games; That being Naval title X, Global 2001 and 2001, Joint Forces Command J-9, and Millennium Challenge2002.

If you need more, just ask.

yes please do go on.............if you don't mind.This is one area i'm sure alot of us here don't know much about .I wish Alex would tackle it.I would be very happy if you just cont......were you left off,or someone with the same knowledge.

[chrsswtzr]

My brains going to explode.

Call-to-action... shall we print out and mail these traitors/our reps/senate/congress this?
Please call into the showwwwww.. we need to do something.

Hmm.... that's one way to describe the feeling! Wow...

[chrsswtzr]

Joseon, that was a good synopsis of the whole overall broad look of it, thank you. I'm sure many of us here would appreciate anymore commentary you may have on the subject, as it's very easy on the eyes 

[gunDriller]

Good old Powerpoint Engineering, as we called it at one defense contractor I worked at.

The US Gov spends $100's of Billions on this type of work.

Now that I've learned Flash, maybe I should ask for a pay raise.

[Dig]

I have a question to Anti_Illuminati: do you really think you've discovered something big, if it's publicly posted on the Internet?

Not for nada, but since AI has been connecting dots and exposing the intricate details of the risk management insanity systems running the infrastructure of our country, you have been on some messianic mission to dissuade people from understanding the impact.  Do you work for Booz Allen Hamilton, MITRE, or Agile?  What gives dude, why are your panties in a bunch? Do you prefer that people do not know about Red Team/Blue Team wargames on US soil that involve wargames depicting mass casualties?

[Puff1]

Hey Criminals that use towns, cities, communities as TESTING GROUNDS for your wargames...

GET THE F*CK OUT OF MY COUNTRY!!!!!!!!!!!

EVERYBODY KNOWS WHAT YOU ARE DOING

YOU TOTAL PIECES OF DOG SHIT!!!!!!!!!!!!!!!!!!!!!

I agree and could add some commentary of my own to that, but it would involve additional usage of a massive amount of profanity.

[8bitagent]

Not for nada, but since AI has been connecting dots and exposing the intricate details of the risk management insanity systems running the infrastructure of our country, you have been on some messianic mission to dissuade people from understanding the impact.  Do you work for Booz Allen Hamilton, MITRE, or Agile?  What gives dude, why are your panties in a bunch? Do you prefer that people do not know about Red Team/Blue Team wargames on US soil that involve wargames depicting mass casualties?

I remember researching the Ptech/Mitre situation awhile back, but it wasn't until seeing AI's threads *exposing* AI(artificial intelligence) that I was able to connect more dots. There is definitely a huge skynet/PROMIS like worm worming through the entire 9/11 and globalist operation

The title references Jay Rockefeller...but isnt it funny how it was  David Rockefeller and Saudi BinLaden Group's main architect that built the world trade center?

[Anti_Illuminati]

Model Interoperation for Effects Based Planning

Lee W. Wagenhals
System Architectures 
Laboratory, 
ECE Department, MS 1G5
George Mason University, 
Fairfax, VA 22030-4444, USA
lwagenha@gmu.edu


Raymond A. Janssen
Rite-Solutions, Inc.
88 Silva Lane, 
Suite 220 East
Middletown, RI  02842
raymondjanssen@aol.com


Edward A. DeGregorio
Integrated Defense Systems
Raytheon Company
1847 West Main Road
Portsmouth, RI  02871
Edward_A_DeGregorio
@raytheon.com



Abstract
This paper describes an approach that broadens the capabilities of models used by command and control organizations to conduct effects based planning and operations by improving the understanding by which tactical actions affect the infrastructure and the civil environments in an area of operation.  The premise is that the quality of the commodity services provided by the infrastructure is a main factor affecting the socio-cultural attitudes and the actions of the local population.  The problem is that a lower-level tool provides quantification of commodity availability, but a higher-level tool needs belief quantification as its metric.  Lacking, today, is a method on how to interface these two.  The paper describes experimentation with the integration of two different modeling techniques that have been used to support effects based operations: Timed Influence Nets, and a civil environment modeling tool based on the W.  Leontief input-output economics model.  The paper describes the experiment design and the Iraqi scenario that were used to investigate the feasibility of three different types of interoperation between the models. The type and level of interoperation that was achieved and the impact on course of action evaluation is described along with overall observations and areas for further research.

1.   Introduction

Since 1992 the type of objectives that the military must address has expanded well be-yond those of traditional major combat operations.  As military operations become other than conventional war – whether against transnational terrorist threats or conducting stabilization operations – the need to broaden the focus of models that support effects based planning and operations has become critical.  One major challenge faced by command and control in the 21st century is to improve the understanding of the relationships between effects on the infrastructure and the civil environments in the area of operation.  Actions taken by all (coalition forces, the adversary, and the civilian population) interact to affect the outcome of the coalition’s course of action.  The quality of the commodity services provided by the infrastructure is one of the main factors affecting the socio-cultural atti-tudes, especially including the actions of the local population.   

Over the past 5 years, there have been ef-forts to develop different modeling techniques and tools to explore these concepts that are necessary for effects based planning.  For the most part these have been separate efforts with little collaboration between tool developers. 

The research described in this paper was motivated by the desire to improve the quality of effects based analysis and planning by identifying methods of interoperation between two different modeling techniques that have been used to support effects based operations.  The first technique is the probabilistic Timed Influence Net modeling approach and the sec-ond is a civil environment modeling tool that is based on the W. Leontief input-output economics model. The Timed Influence Net ap-proach generally is used for analysis at the operational and strategic level of warfare while the civil environment modeling tool is more focused on the immediate effects of tac-tical actions on the infrastructure.

This re-search effort was driven by the basic proposition that it is possible to improve effects based course of action evaluation by using these two models together exchanging information or data between them.  Of course the modeling approaches are very different from one an-other, so the question of how data or information could be passed between the models was unknown.  An experimental approach was taken to explore the potential interoperation between these two modeling techniques to determine if:  1) interoperation is possible, and 2) use of such interoperation would improve the overall analysis over that provided by the models independently.  A case study approach was taken using the situation in Iraq.

Section 2 briefly describes the two modeling tools and approaches. Section 3 then discusses the design of the experiment to include procedures for determining the feasibility and utility of using three different types of interoperation: data exchange; automatic tool-to-tool interoperation; and human to human.  The section also describes the Iraqi scenario that was used in the case study, the models that were developed based on that scenario, and the results of the effects based course of action analysis.  Section 4 describes the experimental results and the paper concludes in Section 5 with overall observations and areas for further research.

2. Effects Based Operations Models
 
Planning for effects based operations relies on several modeling techniques to analyze how potential actions can lead to various effects in an area of operation.  Two broad categories of models have been used to provide the framework for this analysis. The first cate-gory models an effects based plan (EBP) that relates actions to effects through a series of causal linkages.  The EBP then identifies a set of actions together with their timing and shows how those actions are expected to lead to the desired higher level effects.  The second category, System of System (SOS) models, represents the components of and links be-tween various systems in the area of operation.  These SOS models theoretically can be used to show how the state of various components or links in one system can affect the state of other components or links.  These sys-tems collectively are categorized as political, military, economic, social, infrastructure, and information (PMESII). 

In the context of effects based operations there is a relationship between these two categories of models.  The actions and the resulting effects described in the effects based plan should map to the SOS models that can indicate how affects on a component of a system can cause the nature or state of that system and perhaps other systems that are related to it to change.  Thus the SOS models can provide the explanation for some of the causal linkages that are described in the effects based plan model.  However, explicitly connecting these two models has not been the common practice.
 
Part of the difficulty is caused the by major differences in the levels of abstraction that exist in effects based plans and the SOS model.  For some of the physical systems in the PMESII construct, engineering or physics based models have been developed that can predict the impact of various actions on systems and assess their vulnerabilities.  When it comes to the cognitive belief and reasoning domain, engineering models are much less appropriate.  The purpose of affecting the physical systems is to convince the leadership of an adversary to change its behavior, that is, to make decisions that it would not otherwise make.

However, when an adversary is imbedded within a culture and depends upon elements of that culture for support, the effects of physical actions may influence not only the adversary but also the individuals and organizations within the culture that can choose to support, be neutral, or oppose the adversary.  Thus, the effects on the physical systems influence the beliefs and the decision making of the adversary and the cultural environment in which the adversary operates.

Because of the subjective nature of belief and reasoning, probabilistic modeling techniques such as Bayesian Nets and their influence net cousin have been applied to these types of problems.  Models created using these techniques can relate actions to effects through probabilistic cause and effect relationships.  Such probabilistic modeling techniques can be used to represent an effects based plan and they can be used to analyze how the actions affect the beliefs and decisions by the adversary. 

2.1 Timed Influence Nets

Influence Nets (IN) [Rosen and Smith, 1996] and their Timed Influence Nets (TIN) extension are abstractions of Probabilistic Belief Nets, also called Bayesian Networks (BN) [Jenson 2001, Neopolitan, 2003], the popular tool among the Artificial Intelligence community for modeling uncertainty. BNs, INs and TINs use a graph theoretic representation that shows the relationships between random variables.  These random variables can represent various elements of a situation that can be described in a declarative statement, e.g., X happened, Y likes Z, etc. 

Influence Nets are Directed Acyclic Graphs where nodes in the graph represent random variables, while the edges between pairs of nodes represent causal relationships.  Mathematically while Influence Nets are similar to Bayesian Networks, there are key differences. BNs suffer from the often intractable task of knowledge elicitation of conditional probabilities. To overcome this limitation, INs use CAST Logic [Chang, et al. 1994; Rosen 1996], a variant of Noisy-OR [Agosta, 1991], as a knowledge acquisition interface for eliciting conditional probability tables. This logic simplifies knowledge elicitation by reducing the number of parameters that must be provided.  INs are appropriate for modeling situations in which the estimate of the conditional probability is subjective, e.g., when modeling potential human reactions and beliefs, and when subject matter experts find it difficult to fully specify all of the conditional probability pair values.  TINs extend INs by adding the element of time as delays for both the random variable nodes as well as the edges between pairs of nodes.

The modeling of the causal relationships in TINs is accomplished by creating a series of cause and effect relationships between some desired effects and the set of actions that might impact their occurrence in the form of an acyclic graph. The actionable events in a TIN are drawn as root nodes (nodes without incoming edges). Generally, desired effects or objectives the decision maker is interested in, are modeled as leaf nodes (nodes without outgoing edges). In some cases, internal nodes are also effects of interest.  Typically, the root nodes are drawn as rectangles while the non-root nodes are drawn as rounded rectangles.  Figure 1 shows a partially specified TIN.

Nodes B and E represent the actionable events (root nodes) while node C represents the objective node (leaf node). The directed edge with an arrowhead between two nodes shows the parent node promoting the chances of a child node being true, while the roundhead edge shows the parent node inhibiting the chances of a child node being true. The inscription associated with each arc shows the corresponding time delay it takes for a parent 4 node to influence a child node. For instance, event B, in Figure 1, influences the occurrence of event A after 5 time units.

Figure 1 An Example Timed Influence Net (TIN)

The purpose of building a TIN is to evaluate and compare the performance of alternative courses of actions. The impact of a selected course of action on the desired effects is analyzed with the help of a probability profile.  Consider the TIN shown in Figure 1. Suppose the following input scenario is decided: actions B and E are taken at times 1 and 7, respectively. Because of the propagation delay associated with each arc, the influences of these actions impact event C over a period of time. As a result, the probability of C changes at different time instants. A probability profile draws these probabilities against the corresponding time line. The probability profile of event C is shown in Figure 2. 

Figure 2 An Example Probability Profile

In building the IN, the modeler must assign values to the pair of parameters that show the causal strength for each directed link that connects pairs of nodes and a baseline probability for each non-root node.  The CAST logic is based on a heuristic that uses these quantified relationships and the baseline parameter to compute the conditional probability matrix for each non-root node. Finally, each root node is given a prior probability, which is the initial probability that the random variable associated with the node (usually a potential action) is true.  This last item is referred to as an input scenario. 

When the modeler converts the IN into a TIN, each link is assigned a corresponding delay d that represents the communication delay. Each node has a corresponding delay e that represents the information processing delay. A pair (p, t) is assigned to each root node, where p is a list of real numbers representing probability values. For each probability value, a corresponding time interval is defined in t.  Collectively the combination of the time ordered root-node set is (informally) the course of action. 

To analyze the TIN, the analyst selects the leaf and/or internal nodes that represent the effects of interest and generates probability profiles for these nodes.  The probability profiles for different courses of action can then be compared. 

GMU has developed a tool called Pythia with support from ONR, AFOSR, and AFRL (and initially with support from AFIWC) that implements the TIN development technique and provides an analysis environment. The basic problem that it helps solve is given a set of actionable events, determine the Courses of Action that maximize the achievement of desired effects as a function of time. 

2.2 Civil Environment Model

The Civil Environment Model (CEM) has been developed by Raytheon with support from the Air Force Electronic System Center.  It is an effects-based operations logistics commodities model that reflects a nation’s ability to wage war based on damage effects to its civil infrastructure.  CEM uses damage models to determine strategic and cascading effects on the overall Battle Space Environment (BSE).

Figure 3 CEM Organization and Structure

CEM algorithms are based on W. Leontief I/O Economic Model, which is a set of linear equations whose optimal solution expresses a balance between competing demands on an infrastructure.  The CEM functionality can be used to relate physical damage effects and repair capabilities; model cascading effects; monitor production, storage, and transportation capabilities; and project the long-term effects of damage on a national level. 

Figure 3 graphically depicts the organization and structure of CEM. CEM uses a linear program solver to model the flow of commodities such as munitions, repair parts, food, Petroleum-Oil-Lubricants, and others (user defined) to the battlespace. Damage to the civil infrastructure, roads, railroads, inland waterways, factories, power plants, and the like, will result in smaller production, storage and/or transport capacity of the commodities.  Repair of facilities restores their capacity to function.  CEM employs a large database that contains descriptions of the various commodity systems that make up the infrastructure in the area of operation.  Its output is usually a large data set that shows the amount of production, storage, transport, and consumption for each commodity at each location as a function of time.
 
3.   Experiment Design and Execution

To conduct this research, an experimental approach was taken using Operation Iraqi Freedom (OIF) as the context. The basic concept was that it appeared to be likely that by combining the capabilities of Pythia TIN with CEM, one might model Iraqi behavior to get a more refined and useful analysis of courses of action.
The more general problem being addressed is:  Given a data set about a region or area of operation, how can a common data set be used in different types of models to synergistically analyze the situation and enable effects-based planning and assessment?

Figure 4 shows the basic construct.  The modeler (stick figure) uses that appropriate data from the common data set to construct different models, specifically a CEM model and a Pythia model.  The modeler uses the analysis capability of each model to generate its results as the figure shows.  The proposition is that it is possible to refine the models and analysis by using knowledge, information or data from the output of one model to inform the creation or analysis of a sibling model.  The question marks on the flows show the proposition being explored.

Figure 4 Using a Common Data Set in Different Model Types

The focus of the experiment was to discover useful interfaces between the Raytheon CEM capability and the GMU Pythia modeling tool and techniques.  Such interfaces could be data-to-data, model-to-model, or model-to-human-to-model.  The goal would be to improve the effectiveness of the combined evaluations that such interfaces might provide.  One of the challenges for this experiment was the lack of a clear definition of enhancement or improvement that can be quantified.  To our knowledge, this approach had not been tried before, and therefore we decided to conduct a discovery experiment [Alberts and Hayes, 2002] to 1) determine if interoperation techniques could be found and 2) discover potential benefits such interoperation might yield.
 
To evaluate these premises two propositions were formulated to guide the conduct of the experiment.   

Proposition 1: It is possible to use elements of the Pythia/Time Influence Net (TIN) model in the set up and analysis of the CEM models. 

Proposition 2: It is possible to use outputs from a CEM model to refine TIN models.  To evaluate these two propositions it was necessary to define a process exchanging data and information between both modeling capabilities and a method for evaluating the “enhancement” of the analysis that occurs when the elements of one model are used by the other. 

The following relationships between the two modeling approaches were first postulated:   

The EBO analysts uses both Effects-Based Plans (part of which may be expressed through an Influence Net) and System-of-System model (SOSM).  Note that CEM is a form of a SOSM. The EBP describes how actions that affect elements of the SOSM will cause other secondary and higher order effects that may be at the operational or even strategic level. First order effects tend to be physical and tactical; higher order effects may be cognitive and operational or strategic.  The SOSM shows the interdependencies between elements of the physical system that are objects of the primary effects expected by the actions in the EBP (the actions in the Influence Net).  Some SOSMs can show “dynamic” behavior given actions or effects on elements of the SOSM.  Most are based on physical phenomenon.

Figure 5 shows the postulated relationships between CEM and Pythia in more detail.  In the figure a fragment of a Pythia model is shown as the six blue boxes and the connecting blue arcs that are along the top and bottom of the figure.  A CEM model is shown as the box in the center.  The actions that are contained in a Pythia model are used to stimulate a CEM model.  The CEM model is run and analyzed to provide detailed information about commodity effects from those actions and temporal information about how long it takes for the action to cause the commodity effect.

Figure 5 Relationships Between CEM and Pythia Models

These outputs are used to refine the estimates of the strength of the causal or influencing relationships (from actions to commodity effects) and the time delays between the actions and the commodity effects that needed to convert the Influence Net into the Pythia model.  In the figure, the CEM showed that Actions i and j resulted in demand for Commodity i and j being satisfied for 6 and 20 hours a day, respectively.  Note that the analyst will still have to estimate the causal strengths of the commodity effects on the cognitive effects; however, a CEM model adds confidence to the estimate of the commodity effects in Pythia.  The refined Pythia can be used to calculate the probability of achieving various effects over time (probability profiles) when different potential courses of action are used.  The result can be recommendations for course of action.
 
From this formulation of the problem, the following sub-propositions were established:

1.   If CEM shows number of hours per day that demand is met for different commodities given certain actions, these values can be used to help define the premise(s) that go in the Pythia boxes.
 
2.   The Pythia analyst will have to establish what the impact of meeting demand has on cognitive effects.

3.   CEM may also be able to provide estimates of time delays for Pythia The following process for doing the analysis with the two models was formulated.
 
•   Sketch out EBP in Pythia including physical effects on the infrastructure and cognitive effects on the adversary and the general population to define the relationships between potential actions and the overall desired effects.

•   Set up the CEM to reflect the infrastructure in the Area of Operations and use it to identify possible tactical and operational level physical effects and identify “critical” physical effects.

•   Add parameter values to the EBP with the help of information obtained from the CEM and other knowledge.

•   Assess/compare COAs using both EBP and CEM.
•   Select COA and develop detailed plan.
•   Commence plan execution.
•   Use EBP and CEM to assess progress, identify opportunities and problems, and formulate changes to the plan using indicator data.

Figure 6 shows the proposed process flow.  The numbers in the figure are related to the process steps as follows:

1.   Analyst sketches out the TIN with effects, actions, relationships and potential observable indicators that effects have or have not occurred. Some actions and effects can be mapped to the CEM. 
2.   CEM analysis shows detailed physical effects on commodities from various actions. 
3.   The analyst “translates” physical effects from CEM to refine the TIN including adding time delays and possibly adjusting action to commodity influence strength values.
4.   CEM analysis gives detailed description of physical effects on the infrastructure. 
5.   Analyst uses TIN to produce probability profiles, comparing COAs for selection.

6; 7; 8. As the plan is executed, indicator data is used by both the TIN and the CEM to assess progress toward achieving objectives.  This process was followed during the experiment and the results obtained by following this process were subjectively compared to those that would be obtained by building and analyzing the models independently.

Figure 6 Sequence Diagram for Combined Analysis Process

3.1 The Operation Iraqi Freedom Scenario

The Operation Iraqi Freedom (OIF) provides the context to design an experiment that evaluates the propositions described above  that it is possible to use Pythia and CEM, with different levels of abstraction, together to synergistically enhance the evaluation and selection of courses of action (COA) for the sample problem space.

After the successful toppling of the Iraqi government in the beginning of OIF, coalition forces, led by the United States, initially identified seven key areas that were the pillars of completing the mission and then added an additional area.  These pillars are identified below. 

•   Defeat the terrorists and neutralize the insurgents.
•   Transition to security self reliance by the Iraqi government.
•   Support the establishment of a free and democratic Iraq.
•   Provide essential services to the Iraqi people.
•   Establish the foundation for a strong economy.
•   Promote the Rule of Law and promote civil rights.
•   Maintain international engagement and increase support for a democratic Iraq.
•   Promote strategic communications promoting public understanding of coalition efforts and public isolation of the insurgents.  These eight pillars were judged to be critical in establishing a “democratic” Iraq that could survive and thrive.  The first pillar was not originally present, but was added to the initial seven pillars as the terrorist and insurgent activity grew in intensity; in fact, it now has the potential to cause the other seven pillar efforts to fail.

The terrorist and insurgency efforts have been tailored to have maximum negative impact on coalition initiatives in each of the pillar areas.  The impact of their activity has many interrelated ramifications on many factors important to achieving stability in Iraq.  Primary areas of negative impact associated with terrorist/insurgency activity are associated with the following key elements:
 
•   Development of an internal Iraqi economy that provides for generation of national revenue, functioning in-country industries and employment for its citizens; 
•   Establishment of national and local government structures with supporting police and judicial infrastructure to provide for personal security and a fair system of law and order to promote civil obedience and provide individual freedom of choice; and
•   Existence of a supporting infrastructure of food, utilities (electricity, water and sanitation), housing and health services that promotes individual well being.


Note that the first and third of these involve the production and consumption of commodities.  There are complex interdependencies between key factors that create a real dilemma in terms of the way forward and represent very complex effects-based operations from the perspective of the coalition forces, the Iraqi government and the terrorist and insurgents.  Examining both macro and micro effects is important in understanding effects base operations in this context.
 
3.2 Overview of a Pythia Model of OIF

Sketching out a build of a Pythia model for Iraqi Insurgency starts by capturing the behavioral aspects of the key pillars for mission completion and insurgency activities that can impact realization of these pillars identified in the previous section.  These all fall into areas of action that the coalition (in support of Iraqi efforts) might take that, with the support of the Iraqi people, can realize desired end-state objectives.  At a high-level they organize into the flow shown in Figure 7.

Examination of these key pillars and their inter-relationship (going beyond the top-level view of Figure 7) makes it clear that the role of the individual Iraqi citizen will be a major contributor to realization of these key pillars.  Individual behavior is strongly impacted not only by good security and leadership in government, but also how the population perceives its own access to the necessary comforts of life and even the individual’s opportunity for advancement.  Specific pillars and areas directly impacted by terrorism include:

•   Existence of a supporting infrastructure of food, utilities (electricity, water and sanitation), housing and health services that promote individual well being;

•   Development of an internal Iraqi economy that provides for generation of national revenue, functioning in-country industries and employment for its citizens.

By in large these commodities and their availability have a significant impact on other OIF pillars such as providing essential services to the Iraqi people and establishing the foundation for a strong economy that helps the nation stand by itself.  This in turn leads to a desire to explicitly model commodities and their impact on individual participation and commitment as well as higher level political and economic goals for Iraq. 

The next step in sketching an EBP with Pythia is to take the general and high-level objectives of Figure 7 and identify more specific actions and effects necessary to realize the objectives.  Typically, one first places at the right side of the Pythia model the final end-state or ultimate strategic objectives of Figure 7.  Next, working from right to left, one identifies supportive strategic actions and other intermediate objectives from Figure 7 that are necessary to realize the end-state objectives.  In doing this, an “influence structure” evolves and some of the pillars of Figure 7 are found to necessarily precede realization of other pillars.

Figure 7 Organizing the Key Pillars of OIF

Finally, one identifies independent action that participating forces, including the adversary, might take.  Mostly, these input actions are tactical in nature that accomplish tactical objectives; with Pythia these are not just military actions but can be political and economic as well using all of the instruments of national power to effect the PMESII system of systems in the region.  The result at this point is a view that begins to show the structural potential for relationships between the major pillars, and this view is shown in Figure 8.  For purposes of aiding tracking, Figure 7 and Figure 8 use a matching coloring (light blue, light yellow and light green) scheme.
 
The creation of the Pythia TIN model accommodates identification of specific behavioral aspects that also have a role in realizing the OIF pillars, and Figure 8 shows three added aspects:

•   The role of the Iraqi individual (in blue)
•   Acts of terrorism (in red)
•   Commodity categories that influence individual behavior (the green border around the yellow box).

Introducing commodity characterization into the behavioral Pythia TIN model lays the groundwork for testing the proposition that Pythia and CEM can work together. 

There also may be aspects where behavior varies substantially by country region (geographic, political, ethnic, religion, etc.), and Figure 8 shows these possibilities with orange enclosure boxes.

Figure 8 sets Acts of Terrorism, as a separate independent actionable event, which has the same level of input independence control to the modeler as do Coalition and Iraqi input actions.  (Note that Figure 8 only loosely alludes to downstream influence dependency; the actual Pythia model, discussed following, shows explicitly which influence paths were modeled.)  This allows a direct look at realization of pillar objectives as influenced by the level of terrorism, not only on national and regional objectives, but also in the individual’s behavior for support of the Iraqi government.

Second, this modeling evolution chose to realize that for Iraqi behavior in many aspects of influence, effects vary throughout the country based on geographic, religious and ethnic boundaries.  In principal, the entirety of the model could be repeated region-by-region, but full use throughout all effects could lead to added modeling complexity beyond its worth in payoff for the experiment at hand here.

Figure 8 Major Elements and their Relational Roles

Thus, Figure 8 identifies four areas, denoted by orange borders, where modeling should be expanded on a regional basis.  On the left side or input area, it seems surely necessary to characterize terrorism activities by region.  In the area of “establish governance”, it is likely that common methods throughout the country were employed and there is not a need for regionalization.  But it was felt that response to the establishment of governance would vary by region, so those realizations are modeled separately for each region.  Similarly, the end behavior of the individual in support of the new government and economy also merits separate model characterization by region.  These refinements allow for setting some of the influence parameters of the model to better reflect expected regional behavior.

As discussed in beginning of Section 3, in an EBP the first-order effects tend to be physical and/or tactical.  As such, this EBP sketch includes modeling of commodities in the realm of tactical objectives in Figure 8.  For similar reasons, the behavior of the individual is placed in the downstream strategic objectives realm.  This approach facilitates test of the experimental propositions in a couple of ways.  First, it proposes an interface boundary across which the Pythia and CEM tools might exchange information (i.e, the green bordered box in Figure 8 ).

Secondly, it allows a Pythia-only to proceed forward so that a baseline for comparison based on added value by integrating the two tools might be made.  Thirdly, with the Pythia model, the influences from other events and effects within the sketched EBP are more easily included in the test versus adding them to CEM for test purposes, wherein much  additional work might be required due to the lower-level and more detailed nature of CEM.  This approach does open the possibility, however, that there may have to be an examination of the quantization on commodity types that a higher-level Pythia uses versus the greater detail that CEM provides; this is discussed in Section 3.6.2.

The intent with Pythia, then, is to explicitly model these commodities in a behavioral sense.  Also in the spirit of Figure 8, these commodities, actually what will be a high-level grouping of commodity types, are modeled separately for each region since their availability is expected to have a significant impact on individual behavior by region (note the orange enclosure boxes of Figure 8 ).
 
Transitioning to the Pythia model involves expanding the organization and structure of Figure 8 by determining specific pair-wise influence associations and setting parameters of influence and timing from the parent effect to the child effect.  Values for the influence associations are quantitative and can be determined from subject matter experts knowledgeable in the area.  For the purposes of the test of the experimental proposition, data contained in unclassified public sources were employed here.  The result of this is the Pythia model shown in Figure 9.  It should be noted that explicit influences employed followed some general patterns:

First, most of the items shown in Figure 9 generally have a direct left-to-right influence on their “closer neighbors” as suggested by the pictorial layout in Figure 8.

Second, the area of “commodities” of Figure 9 shows grouping (See Sec. 3.6.2 for an elaboration on the groups) of three categories of effects (Health, Infrastructure and Energy) for each of three regions (Kurd, Baghdad and South). The Pythia model shows that these nine items have influences originating from each of their three regional counterparts (Level of Terrorism, Regional Governance and Regional Security).  The modelers felt that these inputs (Terrorism, Governance and Security) have a key impact on the availability of commodities.  Third, in Figure 9 it should be noted that the three sub-events of the Establishing a System of Government of Figure 8 have a “serial” casual influence (shown as vertical arcs) not only on the commodities, but also the sense of well being and the final strategic objectives that will occur over time.

Figure 9 The Pythia Model for OIF

Finally, the commodity event “National Income from Crude” is shown to directly influence only the individual’s wellbeing.  This event is intended as a direct measure of money flowing into the country and as such, the model feeds its effect directly to the individual and thus also affecting wellbeing. This Pythia-only model handles commodities and their availability not in a quantitative, metric sense but rather in a behavioral satisfaction sense.  The Bayesian probabilistic values (which range from 0 (or False) to 1 (or True) of the Pythia nodes are taken to represent a degree to which the individual/consumer demand for commodities might be met, and in turn how that might influence the attitude of the individual to support the country’s government and economic goals.

For the proposition test purposes here, Pythia uses a high-level representation of commodities.  Actually what is shown are categories of commodities that are aggregate groupings of specific, typical commodities. All commodities do affect the behavior of the individual, but here for purposes of test of the hypotheses, they have been aggregated.  Finally, in Figure 9 it should be noted that the commodity “National Income from Crude” is not regionalized as the other commodities are represented.  Also, National Income from Crude is kept as an independent input variable in the model.  While it is true that other events and actions in the model affect the national production of crude, keeping it as an independent variable allows full flexibility in studying its influence.  Basically, this input action is the event that provides the country with money for success.  Conversely, the other three commodity categories, Health, Energy and Infrastructure, have been made totally dependent events with their state being driven by terrorism activities and the success in realizing regional governance and security.

[Anti_Illuminati]

3.3 Overview of CEM Model of OIF

In contrast to traditional logistics models, CEM models deal with abstract flows that have no physical representation in the battle space. Logistics models track individual containers of commodities and are concerned with the paperwork to maintain supply levels at designated locations. For example, a single missile on a truck could be destroyed in a logistics system. Civil Environment would be concerned with the missile production facility, the roads (transport commodity), oil drilling/production and electric power facilities and the flow of an aggregate commodity, “electricity” or “oil”. The actual facilities are represented as synthetic natural environment (SNE) features (See Figure 3). During the course of the OIF, IEDs and other forms of terrorism cause damage to some number of objects in the SNE. This is denoted by a change to the public damage state of the object(s). A consequence of the detonation is the issuance of an interaction, as defined in the Federated Object Model (FOM). The CE federate subscribes for the interactions and upon receipt, notes its position and polls known targets in the area for new damage. 

The scope of the CEM modeling effort for this research is limited to a notional OIF scenario based on open, world wide web sources.  The existing CEM databases for Iraq were adapted using this open source literature and by collaborating with the Rite Solutions George Mason University EBO Modeling team.  Various data sources were used; open source, State Department, etc.  The time span was 2003 – 2006.  CEM was tailored to produce the type of commodities that would have a direct influence on the attitude of the Iraqi individuals.  The database for this study was configured to produce estimates of the 14 commodities in Table 1.  For each of these commodities, CEM calculates the following key parameter values at a function of time:

P: the amount of a commodity that entered a district from production p: the amount of a commodity leaving a district to be used in production.
Z:  the amount of a commodity remaining in stock after redistribution.
C:  the amount of consumption of a commodity in a district.
These output values can be interpreted in terms of meeting the demands of the individual and to determine whether or not satisfaction is sufficient to support the efforts for establishing a long-term, stable government structure. 

3.4 Pythia Simulation Results

The Pythia model, Figure 9, was exercised for a set of courses of action (COAs) to establish its acceptability for a test of the interface propostions.  The COA set for Pythia inputs is presented in Figure 10.  Pythia’s primary GUI interface is these probability profiles.  A probability profile shows the calculated probability of a Pythia modeled event or effect versus time.  Input Actionable Events actually are the input “forcing function” or COA; even so, the same probability profile display can be used to show the “time of actions” for each input actionable event.

Figure 10 shows the behavior forced for all of the input actionable events in Figure 9. The time scale is in weeks.  These input actionable events provide a dynamic time element to driving Pythia.  Illustrating how probability profiles provide a view of the input forcing, the event “Coalition Imposition of Provisional Governance” reflects that the presence of coalition forces gives a dominating umbrella for provisional governance for the first 3 months.  During this same period the Coalition is only moderately successful in imposing security and the same time the Iraqis’ are just beginning their role in the restoration of self governance and self security.  During the next months the Iraqis role and success in providing self governance and self security increases, reaching its largest values in the 24 month time frame.  Illustrating further, the timing for the actionable event “National Income from Crude” was derived from published data that showed crude oil production levels for the mid 20032005 time frame.  Time of actions and corresponding probabilities of occurrence for other input events, such as “Level of Terrorism Activity in Kurdish” regions were derived from more subjective observations of publicly available reports of insurgent behavior.  In this case of terrorism across different regions, predictions were made also by comparing region-to-region over time and giving those regions a high probability if reported terrorism exceeded the levels in the other regions.  Thus, for example, Figure 10 shows the more recent surge of terrorism activities in the Baghdad region.  Now, it is at higher levels than the other two, even though terrorism in the South and Kurdish regions had peaked earlier and has since settled down.

The upper-central portion of Figure 9, in response to the time-varying inputs, models the establishment of a government and the regional progress toward realizing regional self-governance and security.  Some of those results are illustrated by Figure 11.  They show that a greater success is achieved in getting a constitution accepted and leaders elected, closely followed by operational judicial and police systems.  Civil obedience and religious freedom lag these, however.  First insight into regional differences is also shown, wherein the Kurdish region does very well with self governance.  The Baghdad region lags both in effective self governance and in realizing regional security.

Figure 10 Input COA to the Pythia Model

There are four end-state events in Figure 9, and their results for the illustrated Coarse of Action are shown in Figure 12. These results show that the model predicts quite a low chance of success for the primary OIF end objectives.

       Figure 11 Establishing Security          Figure 12 Realizing End-State Objectives

Any real progress toward the final end-state values does not become evident until into the third year after OIF commences.  There is some progress initially and then it seems to level off during the fourth year. It is to be pointed out again, though, the primary purpose for this model was to be a vehicle for testing the hypotheses regards including and integrating Pythia and CEM methods.  The primary purposes here are to insure that realistic inputs from subject matter expert data give a model that responds with cause/effect behavior.  During the checkout of this model, other COAs (not presented here) were tried; they indicated that this model is programmed in a manner that can handle an acceptable range of possible COAs, and is sufficient for the test of the hypotheses.

3.5 CEM Simulation Results

The CEM model was set up to analyze the 14 commodities shown in Table 1 across 18 regions in Iraq.  This set of commodities was determined from the examination of the Pythia model and the analyst’s rationale that these commodities impact the local and region support of the Iraqis.  The state of the various elements of the infrastructure was built as described in various open source descriptions.  Thus they represent the understanding of the infrastructure elements during a period of moderate terrorism with some reconstruction actions completed and others in progress.

CEM outputs data results in a spreadsheet form.  The spreadsheet list the four parameter values (P, p , Z, C) for each commodity by region for each one hour increment over a 168 hour (three week) period; a sample for the Repair Parts  commodity is shown in Figure 13.  Each line shows the hourly size of the stockpile for one of 18 regions in Iraq.

Figure 13 CEM Commodity Measures Over Time

CEM demonstrates its capabilities to model commodities at a detailed level.  Much data is produced as spreadsheets that capture commodity values from their initial condition at the start of the CEM model to however long the model is run.  The current CEM implementation performs these calculations on an hourly basis.
 
3.6 Model Interoperations

An evaluation of the propositions was conducted based on the types of interoperation that were achieved in the experiment.  As shown in Figure 5 and Figure 6, the focus was on interoperation from Pythia to the CEM model and from the CEM to the Pythia model.  Three types of interoperation were postulated.  The first was human to human (or modeler to modeler) in which knowledge gained by analysis of one model helped inform the second modeler so that it impacts either the model construction or analysis technique.  The second type of interoperation was model data to model data.

In this type of interoperation, data derived from the analysis of one model can be used, either directly or through some quantifying formula or algorithm, to improve the estimates of parameter values in the other model.  The actual transfer of the data occurs with human assistance, in part because the human is needed to determine the type and validity of the transfer.  This could lead to the third type of interoperation which is direct model to model in which one model is able to automatically pass data to the second model either in a push mode or in a pull mode.  This can lead to an automated federation of models that are connected via some protocol such as HLA.

One criterion for any interoperation is that it improves the validity of one or both of the models.  Each interoperation can be uni or bidirectional.  For example, it may be possible and useful to take data from the CEM model and use it in Pythia, but not possible or useful to go in the other direction.  That would be unidirectional interoperation. Bidirectional would mean that the interoperation is possible and useful in both directions.  Having built and analyzed both the Pythia model and the CEM model separately, the potential interoperations, first from Pythia to the CEM and then in the reverse direction, were then examined. 

3.6.1 Pythia to CEM Interoperations

The development of the Pythia model in Figure 9, based on the discussion of the OIF in Section 3, introduced a concept of an importance of role for how commodity availability influenced individual well-being and how the well-being of the individual influenced the end states.  It was clear that the original CEM was already well suited for making predictions of many of the traditional commodities.  In a modeler to modeler exchange, this led the Pythia modelers to suggest to the CEM modelers that CEM needed to predict new commodity behaviors, such as medical services/supplies and clothing.  CEM modelers agreed that such new commodities could be added.  It was agreed that previous CEM databases could be used largely as is and those previously developed scenarios became the basis for the initial test runs of CEM.

It became apparent that insurgent activities would cause damage to the commodity production and delivery systems.  For a data to data exchange, the Pythia TIN data for terrorism activity and security development can be made available for input to CEM to suggest damage and repair timings and probabilities. 

In previous CEM applications, such damage/repair guidance was found to be part of the initial setup of a CEM run.  Once that CEM run starts, it runs entirely from these initial conditions.  Ways were discussed wherein a series of CEM runs might be postulated with one CEM’s outputs final state values being feed into another CEM run.  Then, concepts for including repair could be handled by modifying the setup of the next, downstream CEM run.  No method for direct model to model interoperation was readily evident.
 
Development time and resources limited the changes that could be made to CEM.  Thus, for the purposes of testing the proposition, only a single-pass CEM run based on a single set of initial conditions was programmed and exercised.   

3.6.2 CEM to Pythia Interoperations

Pythia characterizes event activities in a probabilistic sense and in this context would request CEM to provide a 0to1 measure that available commodities can meet consumer demand.  CEM, as shown in Figure 13, produces consumption estimates for a commodity as well as a stockpile available for each commodity. Actual demand is met when there is a positive level of stockpile. Stockpile is determined by current stockpile minus consumption plus amount of production and flow into stockpile.  Consumption per capita can be calculated if one knows the number of consumers in each sub-region.  Three types of potential outputs that can be calculated from the CEM runs were considered. The first is a timed average of met demand based on the hourly amount of stockpile and the flow from the stockpile.

This yields a metric of the percentage time that demand is meet for each commodity.  The second concept is to not provide all of the detail from CEM, just the end-of-week levels of supply and the projected level of supply for the next week.  The ratio of these numbers may be an indicator or “happiness” with the commodity situation.  For commodities that cannot be “stored”, such as electricity, a third type of output might be useful for practices such as “rolling blackouts”.  There may be sufficient capability to meet demand, but flow rates cause rolling blackouts.  Consumers may end up being “somewhat satisfied” or dissatisfied even though their full demands are not strictly being met.  The first of these three possibilities, a timed average of met demand, was chosen for this study to examine the interface issues from CEM to Pythia.

It is also evident that CEM details the interrelationships between many commodities and realizes its accuracy by using, as much as it can, specific, real commodity details.  The CEM model calculates 14 different commodities over 18 regions.  This granularity, while fine for CEM, becomes a large complication for the high-level Pythia mode.  A resolve is to consolidate CEM’s commodities into “categories”, possibly as shown in Table 2.  The intent of defining these four categories (Health, Energy, Infrastructure and National Income) is to make their relationships to CEM commodities be as much non-overlapping as is possible.  This is done to simplify the setting of the influence probabilities within the Pythia model.
   

Table 2 Grouping CEM Commodities into
Categories for Pythia

Having developed the Pythia and CEM models in concert together, the category/commodity relationships of Table 2 be comes a candidate mechanism for data to data interfacing of the two.  This is the basis that fostered the definition in the Pythia model shown in Figure 9 of its four categories of commodities (Health, Energy, Infrastructure and National Income).  Creating these categories represents a form of human to human interoperation.  Knowledge gained from the CEM model is used to inform the development of the Pythia model.
 
The CEM model was run for a three week period.  The data collected was then consolidated into a single table showing the percentage to time that demand was met for each commodity in each of the 18 provinces over the three week period.  These results were further aggregated into the three regions and four categories of commodities used in the Pythia model so that the results could be used to evaluate the Pythia model and make potential changes to its parameters in a data to data exchange.  These results are shown in Table 3.

When the Pythia model was exercised, probability profiles of the four internal events related to commodities results.  An example is shown in Figure 14. These profiles of commodity measure show a view of Pythia alone.  The values drop from their initial values at time t = 0, primarily due to the rise in terrorism levels (Figure 10).  As success is realized through establishment of a Government, Governance and Security (Figure 11), the availability of commodities predicted by Pythia begins to increase.  Ultimately Pythia’s measure of individual well-being increases, with the Kurdish Region leading the pace and the Baghdad Region remaining the most troubling.

The model indicates that only in the Kurdish Region are reasonable abundant commodity level reached by month 48.  And as in the previous discussion, the Baghdad Region falls behind the other two regions. 

Figure 14 Pythia Representation of
Commodities for the Kurd Region

The results of the CEM run were compared to the Pythia (TIN) outputs as shown in Table 4.  To do this it was assumed that the CEM run occurred during a certain window in the time line of the Pythia run shown in Figure 14.  Since the CEM run was based on data that had the infrastructure fairly well intact, it was assumed that the CEM run was representative of the later time period in the Pythia run (months 39-42).
   
The behavioral aspects provided by Pythia to indicate commodity levels is very encouraging regards a useful interface boundary from CEM to Pythia.  These examples of commodity availability generated totally from Pythia parallel what one might expect that CEM would produce, given a similarity of inputs and time scale.  It indicates the feasibility of taking CEM outputs that measure commodity output and substituting them into the Pythia model of Figure 9 by replacing these dependent Pythia events with independent actionable events whose a priori probabilities versus time are set to duplicate the commodity availabilities predicted by CEM. This could be done either using data to data interoperation or direct model to model interoperation. Alternatively, it may be possible to make several snapshot runs of CEM at different points in time and use those results to adjust the parameters within the Pythia model so that the Pythia commodity results closely match those predicted by the CEM.

4. Experiment Results
When this research started, it was not at all clear what types of interoperation would be possible between Pythia and a CEM model in support of effects based planning.  Three levels of interoperation were postulated, and a procedure for building and using the two modeling techniques was formulated and followed.  Table 5 summarizes the findings with respect to the interoperation between these two types of models. 

Regards an interface from Pythia to CEM, neither a direct model–to-model interface nor a data to data interface from Pythia to CEM was found.  A human to human interface was found and used.  The Pythia modelers informed the CEM modelers what the main commodities were in the Pythia model and provided the concept that repair to elements of the infrastructure would occur.  In addition, insurgents could damage elements of the infrastructure so these concepts needed to be incorporated in the CEM model.  This provided better focus for the CEM model and provided the questions that were needed to be answered by CEM.

Regards an interface from CEM to Pythia, we did find and show a method for all three types of interoperation.  At the Pythia level events were defined that provided behavioral estimates of commodity availability with these events being influenced by other Pythia events that model the political and security environments of OIF, which provide for safe generation of commodities. 

The more accurate and detailed commodity information provided by CEM then becomes a substitution for the behavioral derived Pythia events.  The CEM derived commodity metrics become the “Time of Action” COAs that then drive the remainder of the higher-level Pythia model.  With this method, the development of the two modeling methods can proceed in parallel followed by substitution of CEM’s results into Pythia where more accurate modeling results are required.

5. Conclusions

This preliminary research revealed some promising insights into the possibility of obtaining better effects based analysis by employing various levels of interoperation between very different modeling techniques.  The question of the feasibility of integrating the use of Effects Based Plans, represented as probabilistic models, and System of System models, which provide a physical quantification, has been explored in some detail through the conduct of a discovery experiment.  Three levels of potential interoperation were explored.  Each level provided some perceived improvement to the collective analysis of the individual models.

The first level was a basic human to human interoperation where the knowledge generate from one modeling techniques is used to inform the other technique.  The cost of doing this is rather modest and the return is possibly quite significant in terms of improved modeling.  The second level, data to data interoperation, was shown also to be feasible from the more detailed CEM model to the more abstract Pythia model and to provide added insight into model results.  The cost is somewhat higher to achieve this level of interoperation because of the need to develop tailored algorithms or techniques for translating the results of one model into values that can be used to improve the other.

The most costly approach would be a direct model to model interoperation.  This requires not only the data exchange to be defined but also requires the model to model interface to be created so that the automatic transfer can occur.  The degree of difficulty and the worth of this form of interoperation is an area for further research.  In addition to conducting the experiment to explore the interoperation between the models, a process and various techniques to create that interoperation were developed.  These may be useful in further efforts to support the interoperation between EBP and SOS modeling techniques. 

In the case study used in this research, the value of creating the interoperation between a Pythia model of the EBP and a CEM model of the commodity system of system has been illustrated.  Having developed and interoperated with both models means that there is increased confidence in the Pythia TIN because key probability values generated therein have been confirmed by the much more detailed CEM.  The operational and strategic focus of Pythia helped set up CEM and thus focus its analytical findings.  Pythia provides a more strategic view of the situation that can be used to support analysis at this level, while CEM can be used to support more tactical level analysis about specific actions on the commodity system of systems. 

Overall, this effort points to a more robust approach for conducting effects based operations in the 21st Century and illustrates the use of experimentation to explore and discover new approaches.  This research may sharpen the focus of further efforts to better combine and integrate the variety of models and techniques that are available to support effects based operations.

Acknowledgment
This work was supported by the Raytheon Company IRAD funds under Purchase Order No. 4500244660 and by the Office of Naval Research under Contract No. N000140610081.


Reference list
Alberts, D. S. and Hayes R. E.. Editors,Code
of Best Practice for Experimentation,
CCRP Publication Series, July 2002
Agosta, J. M, “Conditional Inter-Causally Independent  Node Distributions, a Property of Noisy-OR Models”, In Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence, 1991. 
Chang, K. C., Lehner, P. E., Levis, A. H., Zaidi, S. A. K., and Zhao, X., “On Causal Influence-Logic”,Technical-Report, George Mason University, Center of Excellence for C3I, Dec. 1994.
Haider, S., and Levis, A. H., “Dynamic Influence Nets: An Extension of Timed Influence Nets for Modeling Dynamic Uncertain Situations”, In the Proceedings of 10th International Command and Control Research and Technology Symposium, Washington DC, June 2005.
Jensen, F. V.,Bayesian Networks and Decision Graphs, Springer-Verlag, 2001.
Neapolitan, R. E.,Learning Bayesian Networks, Prentice Hall, 2003.
Rosen, J. A., and Smith, W. L., “Influence Net Modeling with Causal Strengths: An Evolutionary Approach”, In the Proceedings of the Command and Control Research and Technology Symposium, Naval Post Graduate School, Monterey CA, June 1996.
Biographies
Dr. Lee W. Wagenhals is a Research Associate Professor with the System Architectures Laboratory of the Electrical and Computer Engineering Department at George Mason University. His education includes the BS in electrical engineering from Lehigh University (1965), the MS from the Air Force Institute of Technology (1971), and the Ph.D. in Information Technology from George Mason University (2000).
Dr. Raymond A. Janssen is a consultant working at Rite-Solutions.  Prior, he was a Senior Principal System Engineer at Raytheon Company.  His education includes a BS, and a MSEE and the Ph.D. in Electrical Engineering from the University of Minnesota (1970).  Edward DeGregorio is a Principal Investigator for a Raytheon Independent Research and Development (IRAD) project that is investigating the interoperability of Effects-based Modeling tools (developed by GMU) and more traditional physics-based modeling tool (attrition warfare, C3ISR, Civil Environment Modeling). He also is a Mission Area Lead on the Raytheon Enterprise Modeling and Simulation Program, funded by Corporate Engineering. Ed has a BA degree in Mathematics from University of Rhode Island (1967) and MS in Computer Science, URI (1974), along with postgraduate courses in Computer Science and Statistics.

[Joseon]

During the war games, GMU continued to work with the Blue/red nodes cell and the Information Operations during the drill. Experience was gained, in the drill. The gist of the operation was set when the the team was challenged to a series of expanding questions for the Caesar II system. The team's knowledge and experience further deduced, that the Caesar II could expand beyond the manner the tool it was designed.

As I asserted previously, the Caesar II/EB was designed to fit the need of an "Information Operation planning" that could intertwine or be coordinated with Non-information military operation. The Caesar tool, takes into account 'influence nets', expressed as 'probabilistic modeling techniques' and 'discrete event system modeling technique (Colored Petri Nets)', in order to take into account for the (temporal activity) calculation ability of the (COA). In other words, two types of nets were used as data for the COA of the information operation.

The 'influence nets' reach a 'static equilibrium' with the probabilistic model, which give the probability of 'effects' given a set of actions. It is essentially, an equation driven model, deriving to find a balance or answer after a series of actions are inputted into the
calculator.

Expounding on the 'influence nets', this modeling net provides the tool to determine the 'causal' relationship between the actions of the blue forces(represented by nodes), and the effect it renders the red forces(Adversary).  The blue-print of the  model also takes into account the strength, 'causal activity', of the relationship of conflicting nodes. The
factor of 'strength', is then integrated into a probabilistic model based on Bayesian Mathematics.
_________________________________________________________________________________________
according to Wikipedia:

According to the Bayesian probability calculus, the probability of a hypothesis given the data (the posterior) is proportional to the product of the likelihood times the prior probability (often just called the prior). The likelihood brings in the effect of the data, while the prior specifies the belief in the hypothesis before the data was observed.

More formally, the Bayesian probability calculus makes use of Bayes' formula - a theorem that is valid in all common interpretations of probability - in the following way:

    P(H|D) = {P(D|H)\P(H)}/{P(D)}

where

    * H is a hypothesis, and D is the data.
    * P(H) is the prior probability of H: the probability that H is correct before the data D was seen.
    * P(D | H) is the conditional probability of seeing the data D given that the hypothesis H is true. P(D | H) is called the likelihood.
    * P(D) is the marginal probability of D.
    * P(H | D) is the posterior probability: the probability that the hypothesis is true, given the data and the previous state of belief about the hypothesis.

P(D) is the a priori probability of witnessing the data D under all possible hypotheses. Given any exhaustive set of mutually exclusive hypotheses Hi, we have:

    P(D) = \sum_i P(D, H_i) = \sum_i P(D|H_i)P(H_i)\,.


Wait for the next page.

[Joseon]

After the 'influence net' has been completed by Bayesian Mathematics, a systems analyst reads the results, backwards, to find a solution that would be under military advantage. Obviously, the next step would be a CoA, after the analysts completes the data collection of the 'influence net'. With the CoA set into play, military planners must assess the 'available resources' to carry out the coordinated CoA. As well as selecting a CoA, the actions must be selected in a timely matter that would strike the optimum yield for the military. Thus, timing is huge in military operation.

The influence net itself must expand its initial bounds of duty, to create a systems model. More specifically, a 'discrete events dynamic systems model' transitions the 'influence net' to a bigger system called a 'colored petri net'. The influence net, in the new CP net model, are represented as transitions. Tokens, inside the CP net, represent the 'marginal probability' of an action. Since the 'influence net' becomes a transition(consequence of actions), there is no 'temporal information' available. So the data must be inputed into the CP net structure, not the 'influence net'.

Figure 2 helps tremendously to clarify the description. A three system approach in Figure 2 dictate the quality of the Caesar II/EB system:

"Each node represents an action, event, belief, or decision". The node in the 'influence net' structure is represented by a declarative statement. Arrows follow the course of action by the Red nodes(which is the beginning intercourse in the influence net). The arrows itself implies a causal relationship or veracity in its direction. The causative decision of the red nodes yield a Blue or Red node in the next series of events. Tinges of Blue or Red, imply advantage to the faction during the cascading of events.

Once the influence net is completed, with actions inputed to the cp net, the influence net get converted to an 'executable model' also known as the cp net. The cp net ultimately, makes the decision making process simpler for the Information operation. The third step, analysts use the executable to garner a 'probability profile', to sustain a 'marginal probability' of an action/reaction type sequence. The nodes from the influence net are
still represented as probability in the sequence of a time function T.

More to come.. Got to rest and relax. sorry AI and other people.

[Anti_Illuminati]

Modeling effects-based operations in support of war games


Lee W. Wagenhals, Alexander H. Levis*
C3I Center, George Mason University

ABSTRACT

The problem of planning, executing and assessing Effects-Based Operations (EBO) requires the synthesis of a number of modeling approaches. A prototype system to assist in developing Courses of Action (COAs) for Effects-Based operations and evaluating them in terms of the probability of achieving the desired effects has been developed and is called CAESAR II/EB.  Two of the key components of the system are: (a) an Influence net modeler such as the Campaign Assessment Tool (CAT) developed at AFRL/IF, and (b) an executable model generator and simulator based on the software implementation of Colored Petri nets called Design/CPN. The executable model, named COA/EB, is used to simulate the COAs and collect data on Measures of Performance (MOPs). One particular output is the probability of achieving the desired affect as a function of time. Probability profiles can be compared to determine the more effective COAs. This version of CAESAR II/EB was used successfully in August 2000 at the Naval War College in the war game Global 2000. Experiences with building and using the models both prior to the war game and during the war game to answer topical questions as they arose are described.

Keywords: Effects-Based Operations, Influence nets, Colored Petri nets

 

1. INTRODUCTION

Given the potential complexity of future situations, as evidenced by the operations in Bosnia and Kosovo, and the many consequences of the responses, an approach was needed that (a) related conventional and information operations to events and events to effects; (b) allowed for the critical time phasing of actions for maximum effect, and (c) provided in a timely manner the ability to carry out in near real time trade-off analyses of alternative Courses of Action (COAs). A prototype system to assist in developing Courses of Action for Effects-Based Operations (EBO) and evaluating them with respect to the effects they are expected to achieve has been developed and is called CAESAR II/EB1.

The process embodied in CAESAR II/EB consists of four steps. There is an additional step that must occur before the use of CAESAR II/EB. This is the determination of the desired effects: the goals are set by the National Command Authority at the strategic level and by the Commander through the Commander’s Intent for the operational level.


Figure 1: Establishment of desired effects

To reach the goals, certain effects must be achieved. The step is shown schematically in Figure 1. In the first step of CAESAR II/EB, intelligence analysts, often in collaboration with planners and operational staff, carry out Situation Analysis. This step produces an Influence net model that is key to planning for effects based operations.  An application such as CAT** or SIAM*** allows the intelligence analyst to build complex models of probabilistic influences between causes and effects and effects and actionable events, as shown in Fig. 2. the next Figure, 3, shows that the actionable events can consist of both conventional, special, and information operations. This graphic which also implies the existence of a library of models that can be used as modules to create new influence models that are appropriate for the specific situation.
_____________________________________________________________
*alevis@gmu.edu; phone 1 703 993 1619; fax 1 703 993 1708;http://viking.gmu.edu; George Mason University, C3I Center, MSN 4B5, Fairfax, VA 22030, USA.
** CAT is the Effects Based Campaign Planning and Assessment Tool under development at AFRL/IF and is used as a module in the CAESAR II/EB.   
*** SIAM is a COTS product developed by SAIC to support the intelligence community. It is used as an alternative module in CAESAR II/EB.

In the next step, the Influence net model is used to carry out sensitivity analyses to determine which actionable events, alone and in combination, appear to produce the desired effects. It should be noted that Influence nets are static probabilistic models; they do not take into account temporal aspects in relating causes and effects. However, they serve an effective role in relating actions to events and in winnowing out the large number of possible combinations. The result of this step is the determination of a number of actionable events that appear to produce the desired effects and an estimate of the extent to which the goal can be achieved. 

Once the Influence net of the situation has been developed, the situation analyst converts it into an executable model in the form of a Colored Petri net that allows the introduction of temporal aspects (Fig. 4). An automatic algorithm that performs this conversion has been developed, tested, and demonstrated.2 The Influence nets used for situation assessment contain a great deal of information in the form of beliefs about the relationships between events and the ultimate outcome or effect.  While they have an underlying rigorous mathematical model that supports analysis, they provide only a single probability value for a given set of actionable events.  They do not capture the effect of the sequence or timing of the actionable events.  Additional information needs to be inserted to account for temporal and logical sequencing of actionable events. A particular sequence of actionable events represents an alternative Course of Action. Note that in a threat environment proper sequencing is critical; reversal of two operations can endanger lives and or negate the impact of an information operation. Such reversals are not easily observed in a complex scenario with many concurrent tasks. The executable model brings these issues to the fore.


Figure 4:  Development of the Executable Colored Petri Net model

The executable model, when properly initialized with a scenario, is then used in simulation mode to determine their effectiveness of the candidate COAs by generating the timed probability profile for each one. A Commander can then make an informed choice and direct the planning staff to prepare the detailed plan for the chosen COA.

Carrying out simulations using the executable model is not the only way in which COA analysis and evaluation can be conducted. State Space Analysis of the Colored Petri net model derived from the Influence net can be conducted to reveal all of the probability sequences that can be generated by any timed sequence of actionable events.  The result of the state space analysis is a State Transition Diagram that is mathematically a lattice, as shown in the upper left hand corner of Figure 5.  This state transition diagram can be easily converted to a plot showing the range of probability values that can exist at each step in any probability profile.  This technique allows the analysts to see, at a glance, all of the potential effects that timing of the actionable events can have.  The analyst can then select the profiles that gives the best results and presents them to the Commander for selection.


Figure 5:  Decision support for COA selection

2. USING CAESAR II/EB IN WAR GAMES

Until recently, the concept for model-based development and evaluation of Courses of Action (COA) and the CAESAR II/EB tool suite had been demonstrated using realistic scenarios and data, but had not been tested in a dynamic operational environment. While realistic models have been created to test the concepts, the use of the tool suite within a working command and control structure had only been postulated. 

In early 2000, the Naval War College invited the Office of Naval Research to use the CAESAR II/EB tool suite in its capstone Title 10 war game, Global 2000, to gain insight into its potential utility for supporting COA development and evaluation.  This provided an important opportunity to test these concepts and tools in a realistic environment.  At one level, this participation could help determine if this approach to Course of Action development and selection for effects based operations can be used to support war games.

Even more importantly, the participation in the war game would provide insight into how these concepts could be incorporated in real-world operational environments.  Participation in the game would provide insights about the sources of the information needed to create the models, the type of expertise that is needed to build and analyze the models, and  the types of collaboration and dialog that need to occur between modelers, intelligence analysts, operational planners, and the commander and command staff. This section describes how the tool was used and the lessons learned from the experience. 

To present these results, the scenario used in Global has been significantly morphed to remove sensitivities associated with the actual war game scenario.  We begin by briefly describing the modified scenario, followed by the process used to analyze the situation to determine the desired effects. The next step is the development of the Influence nets.  The sensitivity analysis using the Influence net is then illustrated and the conversion of the Influence net to the executable model is described. The generation and use of the probability profiles for COA selection is discussed.  Several valuable insights and lessons were noted from the use of CAESAR II/EB in the game.

This war game concerns an area of operations (AOR) composed of several nations or entities.  One of the nations (the Nation of Borg) is technologically advanced and has become increasingly willing to use its military instrument of national power, primarily through posturing, to resolve its national security concerns.  Borg shares a border with a smaller nation (Alpha) over which there have been historic border disputes.  The Borg has adopted a strategy of conducting military exercises near the disputed border with Alpha.  While these exercises have always terminated peacefully, they provide the potential for the build up of military forces that could be used for a quick-strike invasion of Alpha. The Blue nation is technologically advanced and is a member of a coalition of partners called the Federation.  Blue is geographically separated from the AOR.  An ally of Alpha, Blue has a diplomatic and military presence in Alpha. Blue has active trade relationships with all nations in the AOR. 

The first step in using CAESAR II/EB for effects-based operations is to evaluate the situation.  One of the outcomes of this evaluation is the determination of the key effects or decisions that will be the focus of the Influence net models that will relate causes (actions) to those key effects.   As was illustrated in Figure 1, several inputs are needed for this analysis.  These inputs include Blue’s estimate of the Borg intent, the guidance from the Federation National Command Authority, and the Guidance and Intent of the Federation Commander who will be responsible for conducting operations in the AOR.

In view of the Borg exercises, the situation analysts postulate that the Borg intent is to disestablish Blue and Alpha’s treaty obligations, causing Alpha to ask Blue forces to leave Alpha.  The Borg may launch an invasion of Alpha from their exercise.  Once established inside Alpha territory, the Borg hope to convince Alpha to sever alliances with Blue.  The Borg hope their actions are not severe enough to hurt trade and diplomatic relationship with Blue and the Federation in the long term.  The Borg possesses considerable military capability including dangerous Weapons of Mass Destruction (WMD).  These consist of the Death Star (DS) Weapon and several Assimilation Weapons (AW).  Borg use of these weapons has been held in check in part because Blue also possesses WMDs. 

In light of these developments, the Federation establishes a Federation Task Force composed of Federation forces.  Unfortunately, while the Task Force is being established, the Borg begin their invasion of Alpha territory.  The National Command Authority of Blue issues guidance to the Blue Commander to (1) Get the Borg out of Alpha territory (2) Re-establish existing boundaries (3) Reduce Borg’s inclination for military coercion (4) Maintain Federation access to the AOR and (5) Ensure that actions are consistent with maintaining stability in Federation relationships with Borg.  The commander of the Federation Task Force issues the following Commander’s Intent:

Purpose:

Prevent Borg from continuing the invasion of Alpha and assimilating inhabitants. 
Take action to get the Borg out of Alpha territory

Methods:

Deploy a grid of sensors to locate all forces 
Prevent Borg from using WMD
Conduct Counteroffensive operations to cause Borg to retreat from Alpha

End State:

End armed conflict in Alpha AOR
Establish boundaries as they were before Borg invasion
Ensure Alpha sovereignty
Alpha to continue to be open to Federation Presence
Enable conditions for Borg and Federation to cooperate
Ensure Freedom of navigation and movement in the entire AOR

Given this Commander’s Intent and the perceived intent of the adversary, two key decisions that may be made by the adversary are postulated as the focus of the Influence net.  The first is a decision by the Borg to stop the invasion and the second is a decision by the Borg to enter into negotiations.  Because of concern about the Borg’s possible use of the WMD, a third key decision by the Borg to use the Death Star is also considered for the Influence net model. 

Having settled on the key decisions to focus on with the Influence net, the analyst turns to the operational planners who are establishing the actions that will comprise Courses of Action (COA) that are commensurate with the NCA guidance and the Commander’s intent.  The planners are contemplating an Interceptive Disarmament operation to halt the Borg offensive and protect Federation Forces in the AOR.  At the same time, information operations, including potential political announcements by the President of the Blue nation are being considered.
 
To build the Influence net, the situation analysts use a combination of top down and bottom up approaches.  With the top down approach, the analysts estimate what events or beliefs the Borg would consider in making each of the three decisions.  For example, in deciding whether to agree to negotiations, the Borg will attempt consider if its military operations can be sustained and whether its national interests will be compromised.  The Borg may consider the use of the Death Star WMD to coerce the Federation to negotiate on Borg terms, however the Borg also considers that the Federation could use its own WMD if the Borg uses its WMD.  The analyst reviews each of these concerns and considers the major factors or concerns that the Borg will consider in their deliberations.  This analysis continues until concerns in the form of events, beliefs, or decisions are devised that can be affected by actions taken by the Federation.  Notice that this analysis is done from the point of view of the adversary.  A model is being created that represents Blue’s understanding of how the adversary thinks and what he considers to be important. 
 
To employ the bottom up approach, the analyst considers the actions that are being contemplated by the Federation operational planners and the effect those actions could have on the Borg’s thinking.  For example, the interceptive disarming operation could affect the Borg’s belief that the Borg’s invasion will have taught Alpha a lesson.  It can also effect the perception that the Borg regime will survive.  The objective of the analyst is to ultimately connect the actions that are either determined from the bottom up approach or the actions suggested from the top down approach with the key decision.  In doing this, the analyst uses knowledge of the adversary to analyze and specify interconnections between target system/centers of gravity to determine indirect effects of potential actions.  When so constructed, the Influence net can indicate the impact that actions that potentially comprise a COA, can have on each of the three key decisions through chains of cascading effects.

An Influence net model for this situation is shown in Figure 6.  The model was built using the Campaign Assessment Tool (CAT).  Each node represents an action, event, belief, or decision.  A declarative sentence in the form of a proposition is used to express the meaning of each node.  The directed arcs between two nodes mean that there is an influencing or causal relation between those nodes.  The truth or falsity of the parent node can affect the truth or falsity of the child node.  The Influence net has been arranged with actions on the left and the key decisions on the right.  This is to indicate visually that the effects of the actions are expected to propagate to intermediate effects over time until their impact reaches the key decisions.  The visual construct is that there is a time scale associated with the propagation of effects between nodes of the Influence net that moves from left to right.  There are six actionable events on the left side of the Influence net as shown in Table 1.   These are candidate actions (or results of actions) that can comprise a COA that can impact the three Borg decisions of interest.


Figure 6:  Influence Net in CAT

[Anti_Illuminati]

Table 1. Actionable Events in the Influence Net


In developing the Influence net, the modelers must incorporate two types of knowledge about the Borg.  The first involves the actions, events, beliefs, and decisions and the relationships between them. This knowledge is captured in the structure of the influence.  The second type of knowledge is estimates of the “strength” of the influences represented by the arcs.  There are several ways that this strength factor can be incorporated.  One of the more analyst-friendly ways is based on an algorithm implementing the Casual Strength (CAST) logic developed by George Mason University.  This algorithm has been implemented both in SIAM and CAT.  The algorithm requires the specification of two parameter values for each arc in the Influence net plus a single “baseline” probability for each node in the Influence net.  The algorithm uses these values to generate a complete conditional probability specification for each node in the net.  The result is an approximation of conditional probability values needed to perform the probability propagation to evaluate the effects of the actions in the Influence net. 

Once the Influence net has been completed, it can be used to evaluate the impact of actions on the effects (decisions) of interest.  This can be accomplished in several ways.  The first, and simplest method, is by setting the probabilities of the actionable events to either zero or one, depending on whether the action is planned or not, and evaluating the Influence net.  Algorithmically, this means that the tool propagates these probabilities until all effects are accounted for at the nodes with no parents.   These nodes are the key decision nodes.  In the variant of CAT used to create the example model, the results of this evaluation are visually shown by the color of each node and by providing marginal probability values of each node in a small circle on the lower left corner of the node.  In the color scheme, dark red means that the declaration in the node is false, dark blue means the declaration is true, gray means the probability of the declaration is true is near 0.5.  Lighter shades of red mean that the probability of the declaration is between zero and 0.5 and lighter shades of blue mean that the same probability is between 0.5 and 1.0.  This colorization helps with the visualization of the complete impact of a COA that includes the actions that have been given a probability of one. An analyst can experiment with the Influence net by changing the probabilities of one or more of the actionable events and seeing what the effect is on the key decision nodes.

To assist in analysis of the effect of the actionable events, both CAT and SIAM have built in Sensitivity Analysis routines.  After selecting a node, usually the key decision nodes, the routine will indicate the effect each actionable event will have on the selected node, if the probability of the actionable event is set to either zero or one.  This analysis can indicate which actionable events increase or promote the likelihood of the key decisions and which actionable events decrease or inhibit that likelihood.  It also indicates the magnitude of the affect each actionable event has by itself on the situation.

The Sensitivity Analysis output for the first key decision node of the Influence Net of Figure 6 is shown in Table 2.  The Sensitivity Analysis Table indicates that the current probability of the decision node “Borg decides to negotiate end of conflict” is 0.48.  This is based on all of the actionable events not occurring (Marginal Probability = 0.0).  The second numerical column of the table indicates the probability of the decision node if the probability of each actionable event is zero.  In this case, it is the same as the current marginal probability node of 0.48. The third numerical column shows the marginal probability if each actionable event occurred by itself (and all the others remained unchanged).  This column shows that four of the six actionable events increase the likelihood of decision and two of the actionable events decrease its likelihood.  Furthermore, the first and last actionable events can cause the greatest increases while the Interceptive Disarming (ID) Operation causes the greatest decrease.  The same conclusions can be drawn from the fourth numerical column of the table that contains the sensitivity coefficients.  Positive coefficients mean that the action promotes the decision and negative coefficients means that the action inhibits the decision.  Comparing the magnitude of the coefficients can indicate which actions have the greatest promoting or inhibiting effect. 

Table 2.  Sensitivity Analysis Output from CAT


In an Influence net with multiple nodes with no children (multiple key decision nodes), sensitivity analysis needs to be conducted for each such node.  This is done to determine if actions that affect one key decision in the desired way have an adverse affect on other key decisions.  For example, convincing the Borg that the Federation has not taken steps to full war may decrease the likelihood of using the WMD weapons even though it decreases the likelihood of negotiations.  If such conflicts are detected, they can be examined to determine whether or not the action is a candidate for the COA.

The Influence net models can focus attention on conflicts in another way.  In this example, the sensitivity analysis shows that the Interceptive Disarming Operation has a negative affect on all three key decisions.  This can bring into question the value of this aspect of the COA.  Recall, that the operational planning staff prefers the Interceptive Disarming Operation.  Their analysis is from the operator’s perspective and shows that the operation is necessary to protect forces and assure access to the AOR.  This is a legitimate argument for the operation.  The Influence net points out that his operation could have unintended consequences.  The Influence net also indicates other actions that can be taken to mitigate these consequences.

Once the analysis of the Influence net has been completed and the actionable events for the COA have been selected, the operational planners assess the availability of resources to carry out the tasks that will result in the occurrence of the actionable events.  The resultant plan will indicate when each actionable event will occur.  The next step in the COA evaluation process is to convert the Influence net to an executable model so that a temporal analysis of the COA can be performed.  Using the executable model, the analyst is able to generate the probability profiles that show the marginal probability for any node in the net as a function of time.  These profiles can indicate how long it will take for the effects of the actionable events to affect various nodes in the Influence net.  The analyst will most likely concentrate on the probability profiles of the key decision nodes, the nodes with no children. 

To create the executable model, the analyst has to add two more types to knowledge to the model.  The analysts ha to indicate the time delay that will exist between each node in the Influence net and that node’s children.  This time delay represents processing and communication time delays that slow the propagation of knowledge about events, actions, beliefs, or decisions that are represented by a parent node to the child nodes.  The second type of knowledge that is needed is the timing information of the actionable events.  This information will initially come from the operational planners.  With the CAESAR II/EB tool, the conversion to the executable model is an automated process.  In CAESAR II/EB, the executable model is a Colored Petri (CP) Net, which is a generalized form of a Discrete Event System Model.  In CAESAR II/EB, the CP net is hidden from the user.  Instead of working with the CP net, the analyst interacts with the model through a web browser interface such as Netscape or Microsoft Explorer.  To create this executable, the CAT tool automatically exports a special file that contains all the information needed to generate the CP net.  This file is placed in a special folder on the web site that hosts the CAESAR II/EB tool.  The analyst then uses a browser to access the web site and create the CP net.

The home page of the web site is shown in Figure 7.  The analyst initiates the conversion of the Influence net to the CP net by typing the name of the net in the window of the home page and clicking on the “Make Delay File” button.  The web site will automatically bring up the page shown in Figure 8.  To create the CP net from the Influence net, the analyst types in the time delay information into the appropriate boxes in the browser form shown in Figure 8.  The analyst also indicates nodes in the Influence net for which probability profiles will be generated.  Once the form is completed, the analyst clicks on the run button, and in a few seconds, the executable model is generated and automatically placed in a folder in the CAESAR II/EB web site so it can be used to perform temporal analysis of the COAs. 


Figure 7:  Home Page of the CAESAR II/EB Web Site

Having created the executable model, the analyst can generate the probability profiles for any COA composed of the actionable events and the time each is expected to occur.  To do this, the analyst fills in a COA form on the web page found at the CAESAR II/EB web site as shown in Figure 9.  The form automatically lists all of the actionable events and provides boxes where the probability of the actionable event and the time of the probability can be entered.  Note that the executable model is initialized with all actionable events set to a probability of zero to represent the condition that none of the actionable events has (yet) occurred.  The analyst also checks the boxes that indicate the nodes for which probability profiles will be generated.  After filling out the COA form, the analyst clicks the run button and in a few seconds the probability profiles are generated and displayed.
 
In the particular example of the Federation versus the Borg war game, the operational planners provided initial timing information for the actionable events.  The COA specified that the Interceptive Disarming Operation would occur successfully at the beginning of the campaign (D Day).  It is set to time zero.  This action is synchronized with a pledge by the Federation NCA not to use WMD.   An aggressive Information Operations plan will be executed to convince the Borg that the Federation has not taken steps to full war.  The goal is to convince the Borg by Day 2.  On Day 1, the NCA will give a follow up speech offering cooperation with the Borg if they will terminate hostilities and withdraw.  One Day 2 the NCA will issue the terms for a negotiation with the Borg.   By this time, the Federation will maneuver its forces within striking range of the WMD capability of the Borg as well as its other military forces.  These times are used to fill out the COA form.  The probability profiles shown in Figure 10 were generated for the COA proposed by the planners.  The annotations have been added to indicate the three separate probability profiles. 

Figure 10 is a composite plot of three probability profiles generated by CAESAR II/EB. It shows the marginal probability of each of the three key decision notes as a function of time.  The vertical axis is probability value and ranges between 0.0 and 0.8.  Time is on the horizontal axis and ranges from 0 to 6.  The time scale is in days.  A review of the composite probability profiles indicates that the COA is probably acceptable in terms of the three key Borg decisions.  Collectively the actionable events nudge the Borg toward negotiation while dissuading them from using their WMD.  The likelihood of negotiation begins to decrease slightly at Day 3 but begins to increase at Day 5 and reaches its highest value at Day 8.  The propensity to use WMD starts with a probability of 0.37, decreases to 0.2 at Day 3 and increases slightly before falling to close to zero by Day 5 as the combination of influences convinces the Borg not to use WMD.  The major driver is the positioning of the Federation force within striking range of these weapons. The plot also shows the Borg are likely to continue hostilities right up to the time that they decide to negotiate.  Indeed, while COA moves the Borg in the desired direction on two of the key decisions, these actions also increase the Borg desire to continue to fight.


Figure 8:  CAESAR II/EB Web Page Form to Specify Time Delay Information

3. OBSERVATIONS FROM WARGAME PARTICIPATION

Participation in Global 2000 provided an invaluable opportunity to test the theories, tools, and techniques in an operationally realistic environment.   This section provides observations and lessons that were learned from that experience.  The section begins with the objectives for the war game.  An assessment of whether the objectives were achieved is provided followed by discoveries and findings from the game.


Figure 9:  CAESAR II/EB COA Generation Form

There were four objectives for the use of CAESAR in Global 2000:

•   Determine if CAESAR II/EB has the potential to enhance the assessment and decision making process in Military Operations

•   Gain insight into how the tool is used in a game cell 

•   Identify changes that should be made to CAESAR II/EB to enhance its usefulness

•   Contribute to Blue decision making by providing critical insight into the effects of actions and their timing on desired outcomes as well as unintended and undesirable consequences. 

The first three objectives were achieved.  CAESAR II/EB appears to have utility in support of model-driven COA development and evaluation.  Sufficient insight was gained to create an operational concept for the future use of CAESAR II/EB and to develop an operational architecture view.  As a result of the extensive modeling, before and during the game, several fixes and enhancements both to the tool and the concepts were identified.  The fourth goal was partially achieved. 
 

Insight into the key issues of the use of WMD as an effect of several Blue actions was highlighted through the CAESAR modeling effort.  Faster model turn around time was needed to reach full potential. 


Figure 10. CAESAR II/EB Generated Composite Probability Profiles

One of the first decisions that had to be made at the war game was where the CAESAR II/EB tool suite and the staff that operate it should be located.  Initially, it was thought that the best location was in the command cell of the Federation Joint Task Force.  However, the organizational structure of Global 2000 had a special entity called Blue’s Borg Cell.  This cell was separate from the standard intelligence cells and had the function of providing quick reaction analysis of the adversary’s potential actions and reactions to the Blue COA.  This cell reported directly to the command cell.  Because the cell was populated with numerous subject matter experts (SMEs) from intelligence, information operations, WMD effects, and nodal analysis, it was decided within the first hour of the game that this was the most appropriate location for CAESAR II/EB. 

As the game progressed, the operational concept for the use of CAESAR II/EB evolved to the four major steps:  creating the Influence net, converting the Influence net to the discrete event model, evaluating COAs, and preparing explanations of the results and findings.  The first step, creating the Influence net requires the assistance of SMEs and is the most challenging step in the process.  Accomplishing this step provides immediate assistance to the Blue team by helping structure the
conceptualization of the adversary’s potential reaction to contemplated Blue actions.  While this step does not address any timing issues, it does assist analysts in formulating their analysis and answers to “what if” questions presented by Blue.  Three models were created with CAESAR II/EB, one before the game and two during the game, and each contributed to the overall output of Blue’s Borg Cell via this mechanism.   

The probability profiles provided by CEASAR II/EB help highlight the timing aspects of Blue actions and potential Borg responses.  Even though the third model was not available until late in the game, the questions it answered were still relevant to Blue’s Borg Cell and the decision makers the cell supported. 

4. RESULTS AND CONCLUSIONS

CAESAR II/EB embodies a non-traditional concept for supporting Effects Based Planning and Operations.  One of the key findings from the Global experience is that its use requires the concerted effort of specialist from different disciplines:  intelligence, operational planning (current and future), information operations, and command.  At Global 2000, CAESAR II/EB was used in support of Blue’s Borg Cell because this cells mission was most closely aligned with CAESAR’s capabilities.  The composition of Blue’s Borg Cell provided a rich combination of SMEs that assisted the CAESAR II/EB developer/operator in model construction and analysis.  The developer/operator was able to take textual documents produced by the SME’s and convert them to Influence nets.  The SME’s then reviewed the Influence net and made corrections as necessary to “validate” the model.
 
To be effective, SMEs from both intelligence and operations must be available and work together to build and evaluate the right kinds of model.  This was highlighted in the game.  A better job could have been done in determining the actions that should be incorporated in the Influence net models.  The domain experts (SME’s) about the adversary assisted in the creation of a major portion of the models, but the set of optional actions that can be taken to provide the influencing stimuli to the adversary needed to be provided by military operators including the information operations specialist.  This type of expertise and information was available during Global, but the SMEs with the operational expertise were not located in the Blue’s Borg Cell for direct consultation and dialog.  The limited number of people on the CAESAR team prevented adequate interaction with operational planners to extract that information.
 
Better methods for presenting the results and recommendations from the use of the CAESAR II/EB tool need to be developed.  Presenting probability profiles to command and staff who are not familiar with the CAESAR II/EB or the models does not convey the information needed to make informed decisions.  Appropriate visualization is needed to show decision makers in an intuitive way the results and the reasons for them. A one-page narrative format was used at Global and was posted on the appropriate web page.

The authors believe that the contribution of CAESAR II/EB can be significantly enhanced in war games if it is used during the development of the campaign plan.  It is during campaign planning that the basic COAs are developed.  Models developed using CAESAR II/EB can provide the rationale to the staff and the commander for the actions in the COA.  But there is a more important reason for the early use of the tool.  During game execution, it is not very likely that the game players will use recommendations derived from the analysis of the tool if they are not familiar with the tool, the models, or the format of presentation of the results.   By introducing these concepts to the command and staff early and obtaining their comments and buy-in, they will be able to quickly assimilate recommendations derived from the tool during the game.

An approach to Course of Action development and selection for effects based operations that integrates Information Operations with conventional operations has been presented and CAESAR II/EB, a decision support tool prototype, has been described and an example has been used to illustrate the operation. By participating in the Global 2000 war game, the CAESAR II/EB team has gathered important information that will be used to develop an operational concept for the use of CAESAR II/EB and support effects based operations. 

ACKNOWLEDGMENTS

This work was supported by the Office of Naval Research under grant no. N00014-00-1-0267.

REFERENCES

1.   A. H. Levis,  “Course of Action development for information operations,”Phalanx, The Bulletin of Military Operations Research, Vol. 33, No. 4, December 2000.

2.   L. W. Wagenhals, I. Shin, and A. H. Levis,  “Creating executable models of Influence nets with Coloured Petri nets,”Int.
J. STTT, Springer-Verlag, Vol. 1998, No. 2, pp. 168-181.

[zafada]

Good old Powerpoint Engineering, as we called it at one defense contractor I worked at.

The US Gov spends $100's of Billions on this type of work.

Now that I've learned Flash, maybe I should ask for a pay raise.

lol you should learn Director.  So much better, you can even do physics simulations now in 3d.  I'm working on a dozen games right now so if you ever do get around to learning it (which is really damn easy, especially if you know javascript and flash actionscript is like a c language), let me know.

[lordssyndicate]

The above documents are the George Mason University Documents exposing the following key points.

A. They have a "system" that has been designed to incorporate "Viral Intelligence" into a system that is able to make predictions using "Fractal equation" based statistics paired with an OODA loop generation system. This system is used to predict all of the outcome of their plans in realtime with 99.9% accuracy. It also automatically gathers and incorporates data from realtime events in order to maintain battle field supremacy 99.9% of the time.

B. This system removes  ALL command and control decisions from the hands of the CO (even if the CO is a General or higher....) giving this control instead completely  to the computer itself. Like SKYNET in TERMINATOR.

C. Lastly -- This system was put in place prior to 9/11!!! Oklahoma City, Branch Dividian, and finally 9/11 were all tests of placing this system in complete control of all of our forces under NCOIC control.  9/11 was the day they pulled the plug on ALL human intervention and gave this thing CAESAR II/EB and it's correlating support systems control. So, it could continually engineer and carry out more perfct false flags learning from every instance and every piece of data it collects from the populace in realtime!

THE PEOPLE WHO BUILT THIS AND GAVE IT CONTROL OVER EVERYTHING ARE TREASONOUS PSYCHOPATHS THEY MUST BE BROUHT TO JUSTICE NOW. WE NEED WARRANTS OUT FOR MR. LEVIS AND HIS MASTER DR. RUTH A. "DEATH" DAVID  NOWWWWWWWWWWWW!!!!

[zafada]

Yeah, we can demand it, but it'll never happen.  Face it, people in power are usually corrupt psychopaths.  The humans that don't lust after power are usually compassionate towards living beings.

[Dig]

Yeah, we can demand it, but it'll never happen.  Face it, people in power are usually corrupt psychopaths.  The humans that don't lust after power are usually compassionate towards living beings.

We are not demanding it, we are exposing it. They rule by stealth.  This thread and others like it are the anti-cloaking devices required to expose the genocide caused by the New World Order.  If 305 million American Citizens understood that 99% of the shit on TV is part of a Bullshit wargame dreamed up and executed by psychopaths, they would be immune to its effects and could demand the enforcement of the constitution.

[Anti_Illuminati]

[Anti_Illuminati]

[Anti_Illuminati]

[Unintelligable Name]

All I can say is:

I told you so.

Information is power.

[lordssyndicate]

The Above documents just posted by Anti Illuminati show that the Generals nor any other CO nor even the Commander In Chief are in control of the Military it has been handed over to this big computer they've named CEASAR!

You get it --they all get their orders from CAESAR!


This is worse than terminator.

Because  basically this is a super computer AI telling humans what to do...
This document shows every time a human was in command things f**ked up like bosnia and kosovo but every  time this thing CAESAR was in control they WON!!!!

They HAVE HANDED ALL OF NATO AND THEIR ENTIRE FORCES TO CAESAR!  CAESAR IS THE GiG -- for the  GiG is the Body of CAERSAR!

Anti Illuminati is about to also post a case study showing they used CAESAR to plan and execute both wars and they set this system about the task of doing so as far back as 1984!!!!!!!!!!!!!!!!

YOU GET THEIR SICK JOKE?
ROCKEFELLER, ROHSCHILD all of their Leggets and their minions -- ALL TAKE THEIR ORDERS FROM THEIR ROMAN PAGAN EMPORER CAESAR!

[Anti_Illuminati]

[Anti_Illuminati]

[Anti_Illuminati]

[lordssyndicate]

And Anti Illuminati does it again the document showing a case study proving that when they give CAESAR control they win ..... Also as I said before it shows they started planning the Iraq wars in 1984!

[Dig]

A few movies that may help simplify things...

WarGames (with the WOPR)
Tron
Eschelon Conspiracy

But unlike the movies, AI got the actual documents of the actual systems.

[New Whirled Order]

My step mother works for Booz Allen Hamilton in San Antonio, Texas.  She's real low level though....  An office manager type position.