A joint article was presented by Cyrus Shahabi, Yao-Yi Chiang, Kelvin Chung, Kai-Chen Huang, Jeff Khoshgozaran-Haghighi, Craig Knoblock, Sung Chun Lee, Ulrich Neumann, Ram Nevatia, Arjun Rihan, Snehal Thakkar, and Suya You of the Integrated Media Systems Center – Department of Computer Science, University of Southern California on how the increase in the availability of geospatial data has motivated the effort to seamlessly integrate the data into an information-rich and realistic 3D environment. These models are important because of their use by cartographers, military and national intelligence agencies, city planners, military simulations, and video games. The article on this topic can be found at: http://64.233.167.104/search?q=cache:MMeeHj63ekUJ:graphics.usc.edu/cgit/pdf/papers/Geodec06-icme.pdf+geospatial+decision+making&hl=en&ct=clnk&cd=1&gl=us
The main challenge in designing the system is how to accurately integrate and visualize all aspects of a geographic region, given the existence of diverse data sources that have varying degrees of accuracy and consistency. Moreover, the user should be able to query the system and get information about the location that can then facilitate decision making. Examples of information that could be used by decision-makers are: high-resolution imagery, maps, road networks, and 3D models of buildings. All of which have significant interest to military intelligence. In addition, the system has applications in several other domains, including urban planning, emergency response, online real-estate, simulation and training, and computer games. In a previous blog I discussed the need for geospatial decision making in logistics.
The paper by the team from the Department of Computer Science, University of Southern California proposed applying relevant techniques developed independently in the fields of databases, artificial intelligence, computer graphics and computer vision to developing a working geospatial DSS. The paper presented a general architecture for such a system and describes how the system would be implemented. The decision support system as it was envisioned would not only allows navigation through a 3D model, but also issue queries and retrieve information as the user navigates through the area. In particular, the system seamlessly integrates satellite imagery, accurate 3D models, textures and video streams, road vector data, maps, point data, and temporal data for a specific geographic location. In addition, users can intuitively interact with the system using a glove-based interface.
The uses of this system appear to have endless applications in military simulations. Troops can now fight in a computer generated simulation that depicts accurate terrain and geopolitical situations. Individual decision making can now be reinforced or expanded by providing the individual with the experience of armed conflict without the associated dangers. The result should be less inappropriate decisions on the real battlefield and more lives saved in the process.
In order to efficiently and accurately integrate a wide variety of information about a geographic location, the DSS utilizes a geographic data integration system called Prometheus. Prometheus organizes the available information sources in a domain hierarchy containing well-known domain concepts, such as, satellite image, map, or vector data. Moreover, Prometheus also models different types of integration operations and their effects. Examples of the integration operations include Overlay and Align. The Overlay operation may result in two information layers not aligning with each other while the Align operation improves the alignment between two data layers. When the DSS receives a request to retrieve the data for a geographic location, it can send a request to the data integration system to obtain different types of information (i.e. vector data, raster maps, and satellite imagery). Prometheus determines the relevant sources for the requested data, retrieves relevant data from the sources, performs necessary alignment or integration operations, and returns the integration information to the DSS which can then be displayed on the 3D model.
One of the key aspects of the DSS is to integrate temporal data with other available sources of information. The fusion of temporal data sources significantly enhances the ability of a user to visualize the dynamic nature of an environment over a given time period. In order to add this capability to the system, the DSS can tracks all tagged assets that move in or around the area covered by the DSS in real-time. From a military standpoint, this would allow the Commanding officer to see a 3-dimentional model of the battlefield and know the location off all of his assets in real time. Depending on the degree on intelligence available concerning the interior of buildings, etc. this DSS would provide unparalleled command and control capabilities.
The geo-coordinates of each vehicle or man would be transmitted to a server every 10 seconds via modems installed on the vehicles and by the men. Individual transmitters could be kept small by placing a central server on each vehicle and the individual’s data could be relayed to the central server from there. This would require the individual to only carry a small radio sized transmitter. The system can thus track their location in real-time and displays the information on the 3D model. Vehicle and personnel status information could also be relayed at the same time. In addition, the integration of the large assortment of standard military overlay information could be added to and displayed by the DSS to add to the system’s decision making capability.
A geospatial decision making capability would greatly enhance current military command and control systems. Under the present geopolitical situation it is unlikely that the U.S. will engage in hostilities with a “modern” world power. The current situation indicates a conflict with other third world adversaries is more likely. As a result the communication links vital to the successful operation of this system would most likely not be impaired by countermeasures (jamming), or exploited to ascertain the location of our forces. Therefore, I would recommend that the military pursue this technology not only for use in simulators but also for exploitation as a DSS or command and control system.
Thursday, May 1, 2008
Sunday, April 27, 2008
Decision Support in Logistics
Robert B. Stevens of TASC Inc. has written a paper entitled: “Product Support Decision Support System”. In the paper Mr. Stevens calls for the development of a decision support system to assist the military in planning its logistical support for various missions. Mr. Stevens also is of the opinion that the logistics field has been neglected by the U.S. Military when it comes to logistical influencing factors in combat simulations (wargames). The paper can be found at: http://www.google.com/search?hl=en&q=wargaming+and+decision+support+systems&btnG=Google+Search
The Army traditionally uses Modeling and Simulation (M&S) tools for planning their logistical operations in support of their command. Historically, these tools are used to support global logistic decisions, manage supplies, and test Research and Development (R&D) in the acquisition phase. The problem that is occurring is that as weapon systems become more sophisticated they also become more prone to systems failure. Mr. Stevens specifically points to the Multiple Launch Rocket System (MLRS) as a primary reason that the current systems support and decision making software needs to be overhauled.
The original MLRS (the M270) is about 20 years old. It entered service just before I got left. Since then their have been one system wide upgrade (the M270A1) and a new wheeled version of the launcher called the High Mobility Rocket System (HIMARS). This is a lighter version of the original system (the original system being a tracked vehicle). This makes the new launcher lighter and easier to carry via aircraft. Four payloads for the weapon system would take up the carrying capacity of a flatbed eighteen-wheeler. In addition to the logistical nightmare of having to keep the system supplied with ammo, there is the need to provide maintenance, gas, food for the crew, etc., and this is only one of the many combat and support systems in today’s Army.
Of the two problems expressed in Mr. Stevens, the easiest fix for the wargame simulation would be to include the rudimentary logistical issues into the scenario. The possible variables that could be included could be a Mean Time Between Failures, Mean Time to Repair, and Mean Time Between Replacements. In this way the battlefield commander will have to deal with maintenance shortcomings. The more that they push their troops, the greater the mechanical problems that they will face. Decisions made by the commander without these critical data elements in the wargame do not realistically portray the availability of the weapon systems or equipment.
Mr. Stevens stated that: “logistical systems do not analyze the function of supporting individual weapon systems. What present logistic simulations normally capture are the amounts of supplies and services needed to support organizations in a generic sense. Simulations presently do not capture logistic as a collection of activities associated with acquiring, moving, storing, and delivering supply chain commodities to the war fighter. More importantly, critical individual weapon systems are not managed closely in the logistics’ supply chain, including the necessities of manufacturing spares, retailing parts, transportation, distribution, warehousing, material handling, and inventory management.”
The logistical Decision Support System (DSS) that Mr. Stevens is proposing sounds remarkably like a Just In Time (JIT) system. Most of the variables with regards to mean time failures, repairs and replacement activities exist today. What doesn’t exist is the access to a worldwide database that includes all of the Army supply centers as well as all of their suppliers. If a system like this could be developed, it would save the government significant amounts of money. Keeping track of all existing supplies, their locations, transportation resources, and supplier capabilities would allow planners to develop long range support plans to support the armed forces operations.
This system doesn’t exist. The need for it is critical to saving money, time, and lives in a combat situation. Our weapon systems continue to be state of the art; it is a shame that we don’t have a state of the art support system to ensure that they can operate as designed. We need to be able to get the beans, bullets, and band aids where they are needed, before the troops need them. A JIT inventory control system and DSS could provide that capability.
The Army traditionally uses Modeling and Simulation (M&S) tools for planning their logistical operations in support of their command. Historically, these tools are used to support global logistic decisions, manage supplies, and test Research and Development (R&D) in the acquisition phase. The problem that is occurring is that as weapon systems become more sophisticated they also become more prone to systems failure. Mr. Stevens specifically points to the Multiple Launch Rocket System (MLRS) as a primary reason that the current systems support and decision making software needs to be overhauled.
The original MLRS (the M270) is about 20 years old. It entered service just before I got left. Since then their have been one system wide upgrade (the M270A1) and a new wheeled version of the launcher called the High Mobility Rocket System (HIMARS). This is a lighter version of the original system (the original system being a tracked vehicle). This makes the new launcher lighter and easier to carry via aircraft. Four payloads for the weapon system would take up the carrying capacity of a flatbed eighteen-wheeler. In addition to the logistical nightmare of having to keep the system supplied with ammo, there is the need to provide maintenance, gas, food for the crew, etc., and this is only one of the many combat and support systems in today’s Army.
Of the two problems expressed in Mr. Stevens, the easiest fix for the wargame simulation would be to include the rudimentary logistical issues into the scenario. The possible variables that could be included could be a Mean Time Between Failures, Mean Time to Repair, and Mean Time Between Replacements. In this way the battlefield commander will have to deal with maintenance shortcomings. The more that they push their troops, the greater the mechanical problems that they will face. Decisions made by the commander without these critical data elements in the wargame do not realistically portray the availability of the weapon systems or equipment.
Mr. Stevens stated that: “logistical systems do not analyze the function of supporting individual weapon systems. What present logistic simulations normally capture are the amounts of supplies and services needed to support organizations in a generic sense. Simulations presently do not capture logistic as a collection of activities associated with acquiring, moving, storing, and delivering supply chain commodities to the war fighter. More importantly, critical individual weapon systems are not managed closely in the logistics’ supply chain, including the necessities of manufacturing spares, retailing parts, transportation, distribution, warehousing, material handling, and inventory management.”
The logistical Decision Support System (DSS) that Mr. Stevens is proposing sounds remarkably like a Just In Time (JIT) system. Most of the variables with regards to mean time failures, repairs and replacement activities exist today. What doesn’t exist is the access to a worldwide database that includes all of the Army supply centers as well as all of their suppliers. If a system like this could be developed, it would save the government significant amounts of money. Keeping track of all existing supplies, their locations, transportation resources, and supplier capabilities would allow planners to develop long range support plans to support the armed forces operations.
This system doesn’t exist. The need for it is critical to saving money, time, and lives in a combat situation. Our weapon systems continue to be state of the art; it is a shame that we don’t have a state of the art support system to ensure that they can operate as designed. We need to be able to get the beans, bullets, and band aids where they are needed, before the troops need them. A JIT inventory control system and DSS could provide that capability.
Saturday, April 26, 2008
Decision Modeling and the Department of Transportation
I recently received a traffic ticket on my university’s campus for driving 35mph in a 20mph zone. The street on which this violation occurred is about 2 miles long. There is a single speed limit sign regulating the zone for the direction of travel I was taking. The sign is placed on the back-side of a blind curve (a hill blocks the view of the sign). The sign is on the left side of the roadway and on the downward slope of a hill that starts just after the curve is negotiated. In addition there are perpendicular parking spaces all along the right side of the road. The last speed limit sign prior to the 20mph sign at this location is a 35mph sign on the adjacent roadway. The 20 mph sign is also located approximately 1 mile into the two mile long road.
I realize that my opinion is probable biased concerning this matter, but it didn’t seem fair to me when I was issued the ticket. I traveled the roadway the next two days looking for a speed sing but couldn’t find one that is until the third day when I walked the entire two miles of the street. I started researching street sign placement regulations on the internet shortly there after. I was able to locate the “Manual on Uniform Traffic Control Devices for Streets and Highways” (2003 edition), U.S. Department of Transportation, Federal Highway Administration. The manual can be found at: http://mutcd.fhwa.dot.gov/sitemap.htm
The remarkable thing that I found during my research was how much of the material in my Decision Support Systems class and text was used in the design of streets and highway signage and the rules associated with their placement. One additional critical factor was also involved in the process of designing the system, and that was the time factor. Signage needs to be identified, understood and reacted upon. In this case, the manual states that the entire process takes about 6 seconds or as the manual refers to the statistic as “the six second rule”.
The rules regulating street sign placement directly compare with the decision support rules concerning user presentation. First of all, street signs should appear on the right side of the road. Similar to an individual always looking in the upper right hand corner of their window application for the minimize button. If a sign is placed on the left side of the roadway, then it is “out of place” and will take longer for a user to identify. This is why I never found the sign until I walked the street. A second rule concerning signage is that the bottom of the sign must be at least 7 feet above the pavement. In the case of the sign on this street it was only 5 feet. The reason for this is so that oncoming traffic or pedestrian traffic does not block the line of sight to the sign. As the sign was placed on the backside of a hill, this further exasperated the situation. An oncoming car of pedestrian on the sidewalk in front of the sign completely obscured its view. This would be similar to having a popup window cover the control features on a screen or a warning popup being called up behind another screen.
The last item that I found in researching this issue is that speed limit signs shall be located at the points of change from one speed limit to another. In a decision support system the sign placement in this situation would be akin to having a warning window activate after you have closed the application for which the warning was issued. In all, the placement of the only speed limit sign on this road left much to be desired. In fact, there isn’t just about anywhere else on the roadway where it could have been worse.
I intend to fight this ticket, not just because I feel that the speed limit sign was improperly placed as established by the “Manual on Uniform Traffic Control Devices for Streets and Highways.” No, I intend to fight this ticket because of the basic violations of the entire streets signage design as it compares to good decision support systems design. All hail Information Systems.
Whew – I’m glade I got that off my chest. Happy motoring.
I realize that my opinion is probable biased concerning this matter, but it didn’t seem fair to me when I was issued the ticket. I traveled the roadway the next two days looking for a speed sing but couldn’t find one that is until the third day when I walked the entire two miles of the street. I started researching street sign placement regulations on the internet shortly there after. I was able to locate the “Manual on Uniform Traffic Control Devices for Streets and Highways” (2003 edition), U.S. Department of Transportation, Federal Highway Administration. The manual can be found at: http://mutcd.fhwa.dot.gov/sitemap.htm
The remarkable thing that I found during my research was how much of the material in my Decision Support Systems class and text was used in the design of streets and highway signage and the rules associated with their placement. One additional critical factor was also involved in the process of designing the system, and that was the time factor. Signage needs to be identified, understood and reacted upon. In this case, the manual states that the entire process takes about 6 seconds or as the manual refers to the statistic as “the six second rule”.
The rules regulating street sign placement directly compare with the decision support rules concerning user presentation. First of all, street signs should appear on the right side of the road. Similar to an individual always looking in the upper right hand corner of their window application for the minimize button. If a sign is placed on the left side of the roadway, then it is “out of place” and will take longer for a user to identify. This is why I never found the sign until I walked the street. A second rule concerning signage is that the bottom of the sign must be at least 7 feet above the pavement. In the case of the sign on this street it was only 5 feet. The reason for this is so that oncoming traffic or pedestrian traffic does not block the line of sight to the sign. As the sign was placed on the backside of a hill, this further exasperated the situation. An oncoming car of pedestrian on the sidewalk in front of the sign completely obscured its view. This would be similar to having a popup window cover the control features on a screen or a warning popup being called up behind another screen.
The last item that I found in researching this issue is that speed limit signs shall be located at the points of change from one speed limit to another. In a decision support system the sign placement in this situation would be akin to having a warning window activate after you have closed the application for which the warning was issued. In all, the placement of the only speed limit sign on this road left much to be desired. In fact, there isn’t just about anywhere else on the roadway where it could have been worse.
I intend to fight this ticket, not just because I feel that the speed limit sign was improperly placed as established by the “Manual on Uniform Traffic Control Devices for Streets and Highways.” No, I intend to fight this ticket because of the basic violations of the entire streets signage design as it compares to good decision support systems design. All hail Information Systems.
Whew – I’m glade I got that off my chest. Happy motoring.
Monday, April 7, 2008
Modeling the Chaos of Battle
Booz Allen Hamilton, a leading global consulting firm, and has 19,000 employees serving clients on six continents. Booz Allen helps government and commercial clients solve their toughest problems with services in strategy, operations, organization and change, and information technology. The company boasts of a full range of consulting capabilities. One of the projects that the firm has assisted the U.S. military with is the development of a combat simulator (wargame), which went into active service in 1997. The firm’s web site describing this model can be found at: http://www.boozallen.com/capabilities/services/services_article/1440526?lpid=39070430
Booz Allen Hamilton has developed an Entropy-Based Warfare model that is used to create hypothetical wargaming situations reflecting the US military's vision of future warfare, in which the country's Armed Services increasingly embrace cutting-edge technologies to help achieve an advantage over adversaries. The entropy-based warfare model simulates the combined effects of friction, disruption, and lethality found in the modern battlefield in the adversary’s behavior. Developed jointly with the Office of the Secretary of Defense (OSD), and unveiled in 1997, it's a computerized tool capable of modeling the full range of military actions (including conflict in the air, sea, ground, space, and cyberspace domains) in a single, integrated manner that (according to the company) better represents future warfare.
Virtually all previous models, simulations, and wargames were fundamentally attrition based and lacked the ability to account for the effects of friction and disruption that could be achieved by various other means short of physical or lethal force. Analytically they often provided quantitative results that support one recommendation over another. But they did not account for many factors that affect the outcome. The few that did quantify factors like command, control, communications, computers, intelligence, surveillance, jamming, and reconnaissance lack an analytic construct to accurately account for their effects. They simply measured the influence of these factors as increases or decreases in attrition.
Currently most military conflict models continue to ignore such key factors in military strength as unit cohesion: esprit de corps, morale, morale influence, training, and discipline. One of the issues I see with the entropy-based warfare model developed by Booz Allen Hamilton is the degree of bias from the users who set the various levels of unit cohesion in the simulations. U.S. military planners have often over or under estimated an adversary’s qualities in these capabilities, often with tragic results.
The Booz Allen Hamilton entropy-based warfare model simulates the destruction or interference with an adversary’s C3CM (Command, Control, Communications, and Counter Measures) as having effect on an enemy unit in three crucial areas:
Maneuver derogation (slowing response time to adversary’s actions)
Disorganization (loss of unit cohesion – inability to coordinate actions, etc)
Critical function destruction (or attrition / destruction of assets)
A real word example of entropy-based warfare would be causing all three effects by destroying a unit’s command staff through combat action (physical destruction). Another means would be to isolate the command staff from coordinating their unit by systematically jamming all of their communications. Jamming is not currently considered in most warfare models as a variable. Some of the other variable not considered by most conflict models include: psychological operations and other information warfare techniques, stealth or camouflage, deception, signals intelligence, and reconnaissance activities. In essence, most models fail to account for what Clausewitz termed “the fog of war”.
The objective of the entropy-based warfare model is to teach command staff to again introduce the “fog of war” into the adversary’s concerns while keeping our own collection assets and command activities intact to ensure the effects of the “fog of war” are not felt by friendly forces. Previously existing military simulations failed to emphasize the importance of these force multipliers. The purpose of the new simulation is to reintroduce the effects of blitzkrieg into the modern battlefield. The use of the previously mentioned force multipliers can have the same paralyzing effects on an adversary’s forces as those witnessed by the allies in the opening moves of the Second World War. Some of the effect include: demoralization of the enemy, inducing the inability of enemy units to react to the changing situation, disrupting the enemy’s ability to coordinate activities which can lead to uncoordinated counter attacks and the encirclement of their forces, etc...
Previous wargaming models failed to take into account some aspects of the criticality of speed of operations. Modern computer supported command and control functions allow U.S. forces to maintain a far more rapid tempo of operations because of the speed in planning operations, and the dissemination of plans and intelligence. Modern precision munitions and stealth aircraft capabilities allows deep strikes against key enemy command and control assets with almost assured destruction. Jamming allows the isolation of those enemy assets whose command and control functions cannot be accurately located. The use of the entropy-based warfare model should help to reinforce our military commander’s use and reliance on these largely ignored assets.
The entropy-based warfare model captures neglected aspects of conflict that previous military simulations overlooked. Where previous attrition based models primarily emphasized quantity, the entropy-based warfare model creates a more balanced and advanced view of the modern battlefield and reinforces the use of the assets central to the success of U.S. forces. The new Booz Allen Hamilton simulation is a step forward in the advancement of military war-games. It fills a void in the use of several previously ignored force multipliers. The system still however does not simulate the need for other support services such as supply, medical evacuation, etc. For a command to properly function in combat, these variables also need to be addressed.
Booz Allen Hamilton has developed an Entropy-Based Warfare model that is used to create hypothetical wargaming situations reflecting the US military's vision of future warfare, in which the country's Armed Services increasingly embrace cutting-edge technologies to help achieve an advantage over adversaries. The entropy-based warfare model simulates the combined effects of friction, disruption, and lethality found in the modern battlefield in the adversary’s behavior. Developed jointly with the Office of the Secretary of Defense (OSD), and unveiled in 1997, it's a computerized tool capable of modeling the full range of military actions (including conflict in the air, sea, ground, space, and cyberspace domains) in a single, integrated manner that (according to the company) better represents future warfare.
Virtually all previous models, simulations, and wargames were fundamentally attrition based and lacked the ability to account for the effects of friction and disruption that could be achieved by various other means short of physical or lethal force. Analytically they often provided quantitative results that support one recommendation over another. But they did not account for many factors that affect the outcome. The few that did quantify factors like command, control, communications, computers, intelligence, surveillance, jamming, and reconnaissance lack an analytic construct to accurately account for their effects. They simply measured the influence of these factors as increases or decreases in attrition.
Currently most military conflict models continue to ignore such key factors in military strength as unit cohesion: esprit de corps, morale, morale influence, training, and discipline. One of the issues I see with the entropy-based warfare model developed by Booz Allen Hamilton is the degree of bias from the users who set the various levels of unit cohesion in the simulations. U.S. military planners have often over or under estimated an adversary’s qualities in these capabilities, often with tragic results.
The Booz Allen Hamilton entropy-based warfare model simulates the destruction or interference with an adversary’s C3CM (Command, Control, Communications, and Counter Measures) as having effect on an enemy unit in three crucial areas:
Maneuver derogation (slowing response time to adversary’s actions)
Disorganization (loss of unit cohesion – inability to coordinate actions, etc)
Critical function destruction (or attrition / destruction of assets)
A real word example of entropy-based warfare would be causing all three effects by destroying a unit’s command staff through combat action (physical destruction). Another means would be to isolate the command staff from coordinating their unit by systematically jamming all of their communications. Jamming is not currently considered in most warfare models as a variable. Some of the other variable not considered by most conflict models include: psychological operations and other information warfare techniques, stealth or camouflage, deception, signals intelligence, and reconnaissance activities. In essence, most models fail to account for what Clausewitz termed “the fog of war”.
The objective of the entropy-based warfare model is to teach command staff to again introduce the “fog of war” into the adversary’s concerns while keeping our own collection assets and command activities intact to ensure the effects of the “fog of war” are not felt by friendly forces. Previously existing military simulations failed to emphasize the importance of these force multipliers. The purpose of the new simulation is to reintroduce the effects of blitzkrieg into the modern battlefield. The use of the previously mentioned force multipliers can have the same paralyzing effects on an adversary’s forces as those witnessed by the allies in the opening moves of the Second World War. Some of the effect include: demoralization of the enemy, inducing the inability of enemy units to react to the changing situation, disrupting the enemy’s ability to coordinate activities which can lead to uncoordinated counter attacks and the encirclement of their forces, etc...
Previous wargaming models failed to take into account some aspects of the criticality of speed of operations. Modern computer supported command and control functions allow U.S. forces to maintain a far more rapid tempo of operations because of the speed in planning operations, and the dissemination of plans and intelligence. Modern precision munitions and stealth aircraft capabilities allows deep strikes against key enemy command and control assets with almost assured destruction. Jamming allows the isolation of those enemy assets whose command and control functions cannot be accurately located. The use of the entropy-based warfare model should help to reinforce our military commander’s use and reliance on these largely ignored assets.
The entropy-based warfare model captures neglected aspects of conflict that previous military simulations overlooked. Where previous attrition based models primarily emphasized quantity, the entropy-based warfare model creates a more balanced and advanced view of the modern battlefield and reinforces the use of the assets central to the success of U.S. forces. The new Booz Allen Hamilton simulation is a step forward in the advancement of military war-games. It fills a void in the use of several previously ignored force multipliers. The system still however does not simulate the need for other support services such as supply, medical evacuation, etc. For a command to properly function in combat, these variables also need to be addressed.
Monday, March 17, 2008
Tactical Decision Making Under Stress (TADMUS)
The Tactical Decision Making Under Stress (TADMUS) decision support system program was initiated by the U.S. Navy in response to the 1986 accidental shoot down of an Iranian Airbus aircraft by the U.S. Vincennes in 1986. The Vincennes was a state of the art guided missile cruiser with the Navy’s most advanced Combat Information Center (CIC), equipped with the AEGIS combat system. The AEGIS system was in itself a Decision Support System which should have help to prevent the accidental destruction of the Iranian aircraft. A paper concerning the development of the TADMUS can be found at: http://www.pacific-science.com/kmds/TADMUS_DSS.pdf
Aegis, which means shield, is the Navy’s most modern surface combat system. Aegis was designed and developed as a complete system, integrating state-of-the-art radar and missile systems. The missile launching system, the computer programs, the radar and the displays are fully integrated to work together. This makes the Aegis system the first fully integrated combat system built to defend against advanced air, surface, and subsurface threats. The AEGIS Combat System is highly integrated and capable of simultaneous warfare on several fronts -- air, surface, subsurface, and strike. Anti-Air Warfare elements include the Radar System AN/SPY-1B/D, Command and Decision System, and Weapons Control System.
According to the article, emotional stress might have had an effect on the decision making process that lead to the tragic incident described above. The TADMUS program was established to answer how stress might affect decision making and what might be done to minimize those effects. According to the article, “developing a prototype decision support system (DSS) that minimized the effects of stress was one of the goals of the TADMUS project. The approach taken in designing the DSS was to analyze the cognitive tasks performed by the decision makers in the shipboard Combat Information Center (CIC) and then to develop a set of display modules to support these tasks based upon the underlying decision making processes naturally used be the CO/TAO team.”
The results of the study showed that experienced decision makers were not particularly well served by the current systems in demanding missions. In 87% of the decisions made, the evaluators determined that information transactions associated with the tactical situation assessment involved the subjects trying to match the observed events in the scenario to those that they had previously experienced. In 12% of the cases, the subjects developed a novel hypothetical explanation to explain the events that they were observing. With regards to selecting their course of action to the events, 94% of the subjects applied their tactics based upon established rules of engagement, while the remaining 6% developed a strategy extrapolated from their previous experience.
The tests also showed that experienced decision makers were not well served by the current DSS in demanding missions. The teams experienced periodic loss of situation awareness, often linked to limitations in human memory and shared attention capacity. According to the article, “environmental stressors such as time compression and highly ambiguous information increased decision biases, e.g. confirmation bias, hyper-vigilance, task fixation, etc. Problems related to decision bias included: (a) carrying initial threat assessment throughout the scenario regardless of new information (framing error) and (b) assessing a track based on information other than that associated with the track (i.e. old intelligence data, e.g. confirmation bias).”
The prototype DSS was developed with the goals of: (a) minimizing the mismatches between cognitive processes and the data available in the CIC to facilitate decision making; (b) correcting the shortcomings of the current displays in imposing high information processing demands and exceeding the limitations of human memory; and (c) transferring data from a numeric form to a graphical representation wherever possible. Basically, the prototype DSS would use improved graphics to increase human recognition of the meaning of the data. The goal of the new displays was to reduce errors, reduce workload, and improve adherence to the rules of engagement.
The article is extremely interesting because it addresses two key issues. First, the need to constantly improve on an existing DSS due to changes in the environment which the DSS was designed to support. Secondly, the use of graphical displays to reduce the bias in the decisions reached by the systems users. The article also supports several of the observations reached in our text, to include the limitation on the amount of information that an individual can process at a given time, and how individuals process their information differently based upon their level of experience.
I have had the pleasure of being able to work with the original decision support system (AEGIS) while at the Naval Academy. The system required extensive use to become familiar with the various graphic symbols, input devices, automated functions, etc. The combat simulations that I dealt with were not what I would qualify as complicated. The use of graphics to help evaluate more complicated, target rich environments would be helpful. In the example given in the paper concerning the accidental shoot down of the Iranian Airbus, I would not have considered that particular scenario a difficult problem to track with the AEGIS system. The Iranian tragedy was most likely the result of stress.
The detection, evaluation, and prosecution decisions that must be made by both the DSS and the human users must be made in a matter of moments in some cases. Many systems have been automated, but few captains want to trust the safety of their commands to a software program. Failure to do so resulted in the USS Shark being struck by two French-built Exocet anti-ship missiles fired from an Iraqi fighter. The AEGIS system did automatically track and ID the incoming missiles as hostile and had the Vulcan anti-missile system been activated, the system would have engaged the threat and most probably shot both missiles down. Human error resulted in the ship being struck by both missiles, failure to trust the DSS system to properly perform its mission. Based upon historical data and personal observation, I would suggest further automation of several of the tasks which are currently being performed by humans to increase response time and eliminate bias on the part of the decision maker. In addition, the individuals who use these systems must be better trained and must be taught to trust the system to reach the proper decision and manage they ships weapon systems to deal with the various threats.
Aegis, which means shield, is the Navy’s most modern surface combat system. Aegis was designed and developed as a complete system, integrating state-of-the-art radar and missile systems. The missile launching system, the computer programs, the radar and the displays are fully integrated to work together. This makes the Aegis system the first fully integrated combat system built to defend against advanced air, surface, and subsurface threats. The AEGIS Combat System is highly integrated and capable of simultaneous warfare on several fronts -- air, surface, subsurface, and strike. Anti-Air Warfare elements include the Radar System AN/SPY-1B/D, Command and Decision System, and Weapons Control System.
According to the article, emotional stress might have had an effect on the decision making process that lead to the tragic incident described above. The TADMUS program was established to answer how stress might affect decision making and what might be done to minimize those effects. According to the article, “developing a prototype decision support system (DSS) that minimized the effects of stress was one of the goals of the TADMUS project. The approach taken in designing the DSS was to analyze the cognitive tasks performed by the decision makers in the shipboard Combat Information Center (CIC) and then to develop a set of display modules to support these tasks based upon the underlying decision making processes naturally used be the CO/TAO team.”
The results of the study showed that experienced decision makers were not particularly well served by the current systems in demanding missions. In 87% of the decisions made, the evaluators determined that information transactions associated with the tactical situation assessment involved the subjects trying to match the observed events in the scenario to those that they had previously experienced. In 12% of the cases, the subjects developed a novel hypothetical explanation to explain the events that they were observing. With regards to selecting their course of action to the events, 94% of the subjects applied their tactics based upon established rules of engagement, while the remaining 6% developed a strategy extrapolated from their previous experience.
The tests also showed that experienced decision makers were not well served by the current DSS in demanding missions. The teams experienced periodic loss of situation awareness, often linked to limitations in human memory and shared attention capacity. According to the article, “environmental stressors such as time compression and highly ambiguous information increased decision biases, e.g. confirmation bias, hyper-vigilance, task fixation, etc. Problems related to decision bias included: (a) carrying initial threat assessment throughout the scenario regardless of new information (framing error) and (b) assessing a track based on information other than that associated with the track (i.e. old intelligence data, e.g. confirmation bias).”
The prototype DSS was developed with the goals of: (a) minimizing the mismatches between cognitive processes and the data available in the CIC to facilitate decision making; (b) correcting the shortcomings of the current displays in imposing high information processing demands and exceeding the limitations of human memory; and (c) transferring data from a numeric form to a graphical representation wherever possible. Basically, the prototype DSS would use improved graphics to increase human recognition of the meaning of the data. The goal of the new displays was to reduce errors, reduce workload, and improve adherence to the rules of engagement.
The article is extremely interesting because it addresses two key issues. First, the need to constantly improve on an existing DSS due to changes in the environment which the DSS was designed to support. Secondly, the use of graphical displays to reduce the bias in the decisions reached by the systems users. The article also supports several of the observations reached in our text, to include the limitation on the amount of information that an individual can process at a given time, and how individuals process their information differently based upon their level of experience.
I have had the pleasure of being able to work with the original decision support system (AEGIS) while at the Naval Academy. The system required extensive use to become familiar with the various graphic symbols, input devices, automated functions, etc. The combat simulations that I dealt with were not what I would qualify as complicated. The use of graphics to help evaluate more complicated, target rich environments would be helpful. In the example given in the paper concerning the accidental shoot down of the Iranian Airbus, I would not have considered that particular scenario a difficult problem to track with the AEGIS system. The Iranian tragedy was most likely the result of stress.
The detection, evaluation, and prosecution decisions that must be made by both the DSS and the human users must be made in a matter of moments in some cases. Many systems have been automated, but few captains want to trust the safety of their commands to a software program. Failure to do so resulted in the USS Shark being struck by two French-built Exocet anti-ship missiles fired from an Iraqi fighter. The AEGIS system did automatically track and ID the incoming missiles as hostile and had the Vulcan anti-missile system been activated, the system would have engaged the threat and most probably shot both missiles down. Human error resulted in the ship being struck by both missiles, failure to trust the DSS system to properly perform its mission. Based upon historical data and personal observation, I would suggest further automation of several of the tasks which are currently being performed by humans to increase response time and eliminate bias on the part of the decision maker. In addition, the individuals who use these systems must be better trained and must be taught to trust the system to reach the proper decision and manage they ships weapon systems to deal with the various threats.
Wednesday, March 12, 2008
Military Decision Modeling
Doctors Ross, Klein, Thunholm, Schmitt, and Baxter have written an article entitled “The Recognition-Primed Decision Model”. The article can be found at: http://www.au.af.mil/au/awc/awcgate/milreview/ross.pdf. The authors discuss the US Army’s testing of an improved DSS system as a means of increasing it operations tempo (speed at which it conducts its military operations) and allow the army to act and react faster than the enemy.
The article details the army’s experimentation and tests of a new DSS called RPM (Recognition-Primed Decision Model) and compare the results of its use to the existing DSS system currently in use. The current decision support system MDMP (Military Decision Making Process) uses decision analytical rationale called multi-attribute utility analysis. The authors consider the MDMP to be too time consuming in reaching its conclusions and that the delay degrades the findings of the system.
The MDMP and the RPM both follow a series of four phases in the development of operational orders for the command. These phases are:
1. Identify the Mission (Conceptualize the Course of Action - COA)
2. Test/Operationalize the COA
3. Wargame the COA (For executors as well as planners)
4. Develop Orders (Operations Orders)
The RPM was designed to build on experience and expertise associated with the lessons learned with the army’s use of the MDMP. The RPM requires a more experienced user to successfully operate and it eliminates the previous systems requirement to establish 3 possible Courses of Actions and accept the original COA as proposed by the commanding officer. Studies showed that the first COA proposed was the one actually used in over 90% of the cases. If the COA proves enviable during the war gaming phase of the process, then a new COA would be proposed and the remaining steps repeated to test the new course of action. Under the current system (MDMP) all three COAs required under that system are processed simultaneously and consume a great deal of processing time.
The plausibility of a successfully chosen course of action is tested in the third phase of the process – war gaming. The third phase uses graphics and war gaming (confrontation models) to evaluate the COA prior to execution and allows the decision makers the ability to modify COA quickly from lessons learned.
The new system forces planners to communicate, especially the CO in discriminating his initial COA and reasons for his course of action. The newer system facilitates input from the staff officers who usually possess additional expertise in their specialty areas than does the Commanding Officer. The system allows them to modify aspects of the proposed COA to optimize the possibility of a successful outcome. The RPM also requires additional training in its use and usually requires a more experienced staff to operate efficiently.
An initial review of the system by the army officers who were testing the system indicated that they preferred the RPM over the existing MDMP but that additional testing and possible modifications were still potentially required before they would switch from their current system.
The article details the army’s experimentation and tests of a new DSS called RPM (Recognition-Primed Decision Model) and compare the results of its use to the existing DSS system currently in use. The current decision support system MDMP (Military Decision Making Process) uses decision analytical rationale called multi-attribute utility analysis. The authors consider the MDMP to be too time consuming in reaching its conclusions and that the delay degrades the findings of the system.
The MDMP and the RPM both follow a series of four phases in the development of operational orders for the command. These phases are:
1. Identify the Mission (Conceptualize the Course of Action - COA)
2. Test/Operationalize the COA
3. Wargame the COA (For executors as well as planners)
4. Develop Orders (Operations Orders)
The RPM was designed to build on experience and expertise associated with the lessons learned with the army’s use of the MDMP. The RPM requires a more experienced user to successfully operate and it eliminates the previous systems requirement to establish 3 possible Courses of Actions and accept the original COA as proposed by the commanding officer. Studies showed that the first COA proposed was the one actually used in over 90% of the cases. If the COA proves enviable during the war gaming phase of the process, then a new COA would be proposed and the remaining steps repeated to test the new course of action. Under the current system (MDMP) all three COAs required under that system are processed simultaneously and consume a great deal of processing time.
The plausibility of a successfully chosen course of action is tested in the third phase of the process – war gaming. The third phase uses graphics and war gaming (confrontation models) to evaluate the COA prior to execution and allows the decision makers the ability to modify COA quickly from lessons learned.
The new system forces planners to communicate, especially the CO in discriminating his initial COA and reasons for his course of action. The newer system facilitates input from the staff officers who usually possess additional expertise in their specialty areas than does the Commanding Officer. The system allows them to modify aspects of the proposed COA to optimize the possibility of a successful outcome. The RPM also requires additional training in its use and usually requires a more experienced staff to operate efficiently.
An initial review of the system by the army officers who were testing the system indicated that they preferred the RPM over the existing MDMP but that additional testing and possible modifications were still potentially required before they would switch from their current system.
Monday, March 3, 2008
Decision Making at Berkeley
While surfing the web looking for an article on decision making for this blog I happened upon the Career Services page from Berkeley University. The site is located at: http://career.berkeley.edu/Plan/MakeDecisions.stm. The page/site has broke decision making down into three basic areas: 1) Factors influencing the individual’s decisions, 2) Decision making styles, and 3) a Take Action: Decision Making Models section. The purpose of the site is to assist students in picking a career path.
The site describes three factors that affect our decisions. These factors include:
1. Information Factors
2. Decision-Making Experience
3. Personal Factors
Additional information is available on each of the previously listed factors via a link located on the site.
The site goes on to inform the reader that individual decisions will depend on the decision-making style and the importance associated with the outcome of the decision, indicating that different decision styles will generate different outcomes. The site recommends a planned decision-making style (a structured decision making process). This would indicate that the university considers all of its students to be novice decision makers.
The University of Berkeley site goes on to list three different styles of structured decision making processes: a Pros & Cons model, an Analytical Decision-Making Worksheet model, and an Imaginative-Visualization Experience model. The Pros and Cons model is an un-weighted decision making model as opposed to the Analytical Decision-Making Worksheet which does weight the various factors. Either of these two models would be ideal for the novice decision maker. The Imaginative-Visualization Experience model has the decision maker trying to imagine and experience the possible outcome of their decisions internally. This last model would most likely be utilized by an experienced decision maker. In addition each of these decision styles has time constraints associated with their use. Most college students will face the critical issue of time availability directly affecting the choice of their decisions based upon the limits in the amount of time that can be used to research a topic and the type of decision model that will be chosen.
I found that the site provided an interesting look into basic decision making models and a rudimentary version of a decision support system. The site is aimed at its student body and therefore largely inexperienced decision makers. The site isn’t designed so much as to help a student make a decision, but on what a student should look at when making a decision. As an example the site does not provide any drill down capabilities for helping a student pick a major or a career path.
The site describes three factors that affect our decisions. These factors include:
1. Information Factors
2. Decision-Making Experience
3. Personal Factors
Additional information is available on each of the previously listed factors via a link located on the site.
The site goes on to inform the reader that individual decisions will depend on the decision-making style and the importance associated with the outcome of the decision, indicating that different decision styles will generate different outcomes. The site recommends a planned decision-making style (a structured decision making process). This would indicate that the university considers all of its students to be novice decision makers.
The University of Berkeley site goes on to list three different styles of structured decision making processes: a Pros & Cons model, an Analytical Decision-Making Worksheet model, and an Imaginative-Visualization Experience model. The Pros and Cons model is an un-weighted decision making model as opposed to the Analytical Decision-Making Worksheet which does weight the various factors. Either of these two models would be ideal for the novice decision maker. The Imaginative-Visualization Experience model has the decision maker trying to imagine and experience the possible outcome of their decisions internally. This last model would most likely be utilized by an experienced decision maker. In addition each of these decision styles has time constraints associated with their use. Most college students will face the critical issue of time availability directly affecting the choice of their decisions based upon the limits in the amount of time that can be used to research a topic and the type of decision model that will be chosen.
I found that the site provided an interesting look into basic decision making models and a rudimentary version of a decision support system. The site is aimed at its student body and therefore largely inexperienced decision makers. The site isn’t designed so much as to help a student make a decision, but on what a student should look at when making a decision. As an example the site does not provide any drill down capabilities for helping a student pick a major or a career path.
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