Monday, October 31, 2005

IBM Casual Day

In 1994 I began working with IBM in Northern Virginia. In that geographic area, it is customary to wear a dress shirt and tie to work every day. At IBM, it was the same – along with a preference for white shirts and red ties. For many IBM people (technical, not sales) they also chose to wear suits to work on a typical day. After working with them for several days, they mentioned that I would be at their campus on Friday, which was “dress down day”. They were all allowed to choose to dress more casually on Friday. Being from a small company about 5 miles from their campus, I thought this was overly stringent. At our facility, each of use chose what to wear and there was great variety among the staff. But, it was one small consolation that they could relax on Friday.

I showed up at their campus on Friday – casual day. Sure enough, everyone was dressed down. Our main contact was wearing a nice polo shirt, a pair of docker pants, and slip-on loafers. As we walked around the offices, I noticed other people dressed similarly. In fact they were dressed identically to our host. Everyone was wearing the polo-docker-loafer combination. Even on dress down day they all had to wear the same uniform of casual clothes.

I joked with our host about this and he did not find it funny at all. He felt that it would not do at all for people to wear just anything on dress down day. He thought standardized dress down was the best idea.

No one was wearing a t-shirt, blue jeans, sneakers, flannel shirt, boots, or any other variation. The conformance of middle and high school had carried into the office and held its iron standards there as well.

Sunday, October 30, 2005

Future Homemakers of America

My wife and I were talking about our high school experiences and mentioned clubs like 4-H, FFA, and FHA. Where does the Future Homemakers of America (FHA) fit in today’s world. In the 1970’s it meant teaching young women to sew, cook, do crafts, shop for groceries, and handle all of the domestic chores that have traditionally been assigned to women. In the 21st century, how to you run an organization like that? You don’t. It can’t still exist. So I got on the Internet and searched for FHA. I found a curious site called FHAHERO that contained the Future Community Leaders of America. A little research at Britannica led me to understand that FHA had transformed itself into an organization for community leaders. They take men and women and teach responsibility and leadership.

On the web site you will still find the people wearing the red blazers that they were known for in the 1970’s. Those are ugly, but they are a connection to the past. It is good to see an organization that has been a part of the lives of generations of Americans to find a new niche to keep on working with people.

Google was not done yet. It also showed me that Laurie Graham had written a novel called Future Homemakers of America. It looks like a history piece on the lives of women in WWII. (FCCLA) Future Homemakers of America-Home Economics Related Occupations (A Novel)

(Hmmm ... that was really all I could come up with on this topic. I thought there might be more ideas there.)

Friday, October 28, 2005

Modeling Approach

[NOTE: Freewriting first draft for the "Military Modeling" chapter of the upcoming book, Dynamic Modeling, edited by Paul Fishwick, to be published by CRC Press in 2006.]

In the previous section we discussed many patterns of relationships that exist between multiple models and talked in very general terms about what would be represented in those models. However, we did not explore specific mathematic or logical algorithms that would be used in those models. In practice, the number of techniques, algorithms, and equations that are used in military models is close to uncountable. It is not possible to describe all of them or even those that might be considered “the best”. So many different problems are studied with military models that there is no “best” approach that can applied universally when representing a specific vehicle, human, or unit. However, the techniques that are used do fall into distinct categories. In this section we will discuss four categories of modeling dynamics that are often used in military simulation systems.


Physics-based models are most often found in engineering and virtual simulation systems. For example, a missile pursuing a target would be represented by the physics of motion, momentum, mass, and aerodynamics. Changes in the fin positions would drive aerodynamic equations and change the vector of the missile based on the forces at work on the mass of the missile. Similarly, the seeker head in the missile would scan the environment electronically using the same pattern, revisit rates, and sampling rates of the real missile. This behavior would allow the simulated missile to collect data about a target in the same way that the real missile does.

Physics-based models are most often used to analyze the behavior of an existing weapon or to assist in the design of a new weapon. Understanding exactly how the pieces of the system will behave is an important part of exploring the design space to find optimum capabilities and combinations of capabilities that are optimum for the entire system.

Physics models require a great deal of data and mathematics. The data must be available for the system being modeled, the environment in which it is operating, and any other objects that it will interact with. Mathematics are required to represent a number of different behaviors of the system, interactions that occur within the system, and interactions that occur with other objects. Given this need, it is not sufficient to collect data and equations only for the missile that is to be studied. The model builders must do the same for the environment and for any objects that will interact with the missile.

Because of the volume of data, and the number and complexity of the equations that are required, physics models are necessarily reserved for smaller scenarios that involve only a few objects. Once constructed, the models can be computationally intensive. This means either purchasing a number of high-powered computers or accepting extremely long simulation times. The budget of the project limits the former and the schedule limits the latter. The project is literally a compromise of what the project can afford in time, money, and skilled staff. These limitations are one of the primary causes of the diversity in military modeling solutions. Constraints have forced generations of modelers to create unique representations of their problem.


Stochastic processes, probability and statistics, are most often found in virtual and constructive models. As simulation systems grow larger in their scope of representation, there is a need to capture many more activities and interactions in models. Lacking the detailed knowledge, breadth of expertise, access to data, time to build, and compute power to run a pure physics-based system, modelers have often resorted to a statistical representation of objects and interactions. In this case the models capture the behavior of many iterations of an event and represent individual event results using a probability function and the results of a pseudo random number generator. This type of modeling was introduced to the military modeling community by Stanislaw Ulam when he was working on the design of atomic weapons during World War II (REFERENCE). Ulam encountered a number of problems for which the specific physical behaviors were not known, but where the pattern of outcomes had been measured. Therefore, he chose to use the statistical properties of the event and rely on multiple simulation runs to arrive at an accurate behavior for the entire system.

The previous missile example lends itself well to stochastic models. Instead of representing all of the minute physical interactions, a modeler could choose to represent the outcome of a missile engagement given a limited number of input variables governing each event and recourse to a probability distribution. The use of a pseudo random number in decision-making means that no one engagement contains all of the details of the event as in the physics model above. However, if the model is run a number of times, the randomness of multiple models will blend together and arrive at an accumulated result that is representative of the system behavior that emerges from all of the interacting models.

Stochastic modeling has proven to be extremely useful because it allows modelers to study problems that were previously beyond the limitations of physics models. This has led to the creation of very large simulation systems capable of representing hundreds or thousands of events and objects on a battlefield. However, these models also require that their creators understand both the physical behavior of the system and the statistical aggregation of those behaviors in order to create accurate stochastic models.

Logical Process

Physics and stochastic models are not appropriate for representing the processing of information that is carried out in a computer. These activities are better represented as a sequence of logical steps that make up a defined process. Within the missile there are controllers and computers that process information and make specific decisions based on that stimuli. A model of the missile may best serve the needs of a study by replicating that logic to control the missile’s reaction to maneuvering targets or its response to control signals from an aircraft.

Logical models may also be used to capture the core rules of combat, or the steps that are followed by automated objects in carrying out their mission. These objects may be aircraft, ground vehicles, weapons, sensors, or any other battlefield object. When an object is controlled by a simulation system rather than a human operator, most of the time it is following a logical set of defined processes. These instructions tell it when to move, which direction to go, how fast to proceed, which objects to focus on, and which to ignore. These may be very complex processes, but they do not involve equations of physics or random decision points. In situations when an object should follow some form of “textbook” operation, the logical models are an excellent method of encoding this.

Finite State Machines (or Automata) (FSM) are often used to assist in organizing very complex sets of behaviors. FSM allow the modeler to capture hierarchical behaviors, triggers for changing from one behavior to another, encapsulated behaviors that can be reused in multiple FSM, and deterministic behavior that can be mapped and validated. Military systems that are known as Computer Generated Forces (CGF) or Semi-Automated Forces (SAF) systems often contain a large number of FSM logic models. CGF systems are used to provide automated control of several dozen or hundred objects. A human may provide the overall mission and direction, but the CGF will supplement this with detailed control of movement and engagement through the use of FSM. These systems are not limited to logical models, but may integrate models of all the types described in this section. CGF have proven extremely useful in reducing the number of humans necessary to control simulated battlefield activities by moving detailed control from the hands of the human controller to the FSM logic.

Artificial Intelligence

Artificial intelligence also encompasses logic process models like FSM and production systems, but it is broader than that. In military modeling, these techniques are used to represent the behavior of humans, groups, and objects that are controlled by humans. The focus is on replicating the decisions that are made under a specific set of stimuli. To accomplish this, modelers and researchers have turned to FSM, expert systems, case-based reasoning, neural networks, means-ends analysis, constraint satisfaction, learning systems, and any other technique that shows promise in accurately capturing the complex reasoning process of humans.

The missile guidance and navigation example that we have been using is not ideal in this area. Though a missile model may use a FSM to model its behavior, it is not attempting to create an artificial representation of intelligence, rather it represents a logical process that is followed robotically. If the missile were being controlled remotely by a human who is viewing the target on a computer screen, then the behavior of the human might be represented using an AI technique. A neural network may represent the human’s ability to discriminate a target in the scene and means-ends analysis may represent the humans decision process in selecting a target, leading its position, and switching from one target to another opportunistically.

AI techniques usually focus on processing information in a human-like manner. Using databases or rule sets, the algorithms attempt to make deductions that lead to behavior selection. The deductive process may be deterministic or stochastic (Russell and Norvig, 2000).

Wednesday, October 26, 2005

Model Dynamics

[NOTE: Freewriting first draft for the "Military Modeling" chapter of the upcoming book, Dynamic Modeling, edited by Paul Fishwick, to be published by CRC Press in 2006.]

To this point we have focused on defining and categorizing military modeling according to its application. Those categorizations were meant to illustrate the unique situations, problems, and interests of the developers and customers for military models and simulation systems. In this section we will describe the most dominant forms of dynamic modeling that are used in the community. Because military systems and problems are so diverse and such a large investment has been made in exploring them, there are many more unique forms of dynamic modeling than can be captured in a single chapter or an entire book. However, the forms that are described here are some of the most commonly used. They are also presented as general categories that cover a number of unique implementations.

Dynamic modeling is military simulation often focuses on activities like:
· Movement,
· Perception,
· Exchange,
· Engagement,
· Reasoning, and
· Dynamic Environment.

In this section we describe the dynamics that are included in each of these categories. This is followed by a section that explores multiple approaches to modeling these dynamics.


Dynamic representation of movement captures the change in an object’s position over time. Models may represent position as a coordinate in two-space, three-space, or a velocity vector. Two-space coordinates usually include a position in X and Y, such as latitude and longitude. For models that represent only ground-based vehicles like trucks, tanks, and foot soldiers, this can be sufficient. The object may have no variation in elevation, or the elevation may come from the underlying elevation of the terrain on which it sits. Position may also include orientation, which in two-space would be limited to a 360 degree angle around the vehicle. A common reference system for this angle is with the zero point being aligned with true north and proceeding clockwise with 90 degrees being east, 180 being south, and 270 being west.

In three-space, the coordinate system includes a representation of elevation. This third dimension may be height above the local terrain, elevation above mean sea level, or distance from the center of a sphere that represents the Earth. The latter measurement evolved during the creation of distributed heterogeneous simulation systems. When networking multiple simulations, differences in the terrain representation within each system led to significant differences in vehicle position with respect to the terrain. Therefore, a non-terrain referenced coordinate system was needed to overcome these differences. When a three-space orientation is added to this model, it includes the pitch, roll, and yaw of the object, creating a six degree-of-freedom (6-DOF) model. When represented as a vector, this may also include the velocity of the vehicle along the axis of orientation.

In their basic form, movement models change these position and orientation coordinates according to a logical or physical representation of movement, as described in the next section. However, most implementations go further to include the effects that movement has on the object and the environment. The movement model may be linked to a model of the fuel consumption of the vehicle. This adds a limiting factor that can stop movement when the fuel is depleted. The inclusion of a fuel model leads to the need for the system to represent a process for replenishing the fuel consumed. Otherwise, the objects in the simulation will eventually grind to a halt. In military modeling, the addition of these details leads to the need for many more models to drive the additional variables that were added. Systems can grow far larger than can be developed, funded, or hosted on a computer through the poor management of these modeling details. Many authors have warned against this gradual creep in features that leads to the eventual failure of the system being developed (Law and Kelton, 1991). This type of growth is not limited to movement modeling, but can occur throughout the system if the designers do not control it.

A movement model may also calculate the number of hours of operation that the object has been used. This information is the root of most system failure and maintenance models. This drives a mean time between failure (MTBF), repair (MTBR), or other similar models.

The interaction of object movement with the terrain can generate environmental changes that trigger yet another model, such as the generation of smoke or dust clouds in the wake of a vehicle. If these changes to the environment are represented, then they call for specific environmental models that can calculate the size and density of the cloud created, as well as its drift and dispersion over time.


Military objects move about the environment in order to interact with other objects. One of the first steps in this interaction is to perceive or detect other objects. This is the process of applying a sensor to detect the existence, position, and identification of the other object. Sensor models capture the signatures of those objects. A visual sensor will capture reflected light from an object to the sensor. In most cases, the sensor model does not actually represent the path of a light vector, but instead considers the range and orientation between the target object and the sensor and calculates whether the target is potentially detectable based on the effective range and field-of-view of the sensor. A sensor model may also include information about the environment in which the detection is being attempted. For a visual sensor, atmospheric factors like the presence of smoke, dust, fog, and lighting may be used to diminish the possibility of detection. Also, environmental features like hills, trees, and buildings may be interposed between the target and the sensor and impact the detection of an object. The physical characteristics of the target may also be considered. Its size, contrast with the background, movement, and composition may significantly impact its detectability. Larger targets may be easier to see than smaller ones. Targets may have a higher or lower degree of camouflage, changing the ability of the sensor to separate them from the background image.

In military simulations, visual sensors are just one of a large variety of sensors that are available. Many systems include sensor models that collect signature information in the infrared spectrum, sound, emitted radio and radar signals, magnetic properties, and movement and vibrations. Models of each of these can be constructed at a number of different levels of detail, but each must determine whether to include the properties of the sensor, sensing platform, paired geometry, environment, target, and external interference. As illustrated earlier, as the sensor model becomes more complex, it drives the complexity of the entire system. Including all of the categories just listed would trigger the need for additional detail in the sensor model, but also the need for additional details in all target objects and the environment. Often the limitation in creating a high-fidelity sensor model is not driven by our understanding of the sensor, but, rather, by our ability to represent the characteristics of the target and environment that are needed to create such a model. In a military simulation system, the detail included in a model may be limited both by the needs of the customer and by the desire to keep the entire system balanced, not allowing one model to drive all of the others into become larger and more detailed (Pritsker, 1990).


After moving and detecting, models are needed to allow objects to exchange materials and information with each other. Battlefield operations often lead to the depletion of materials like fuel, ammunition, food, medical supplies, and people. A logistics model may be used to represent the ability of the military to constantly deliver these materials to objects in operations. Such models are often based on an understanding of the rates of consumption, the pre-deployment of supplies to locations that are close to the operation, and the constant replenishment of supplies through a network of supply nodes. Replenishing supplies within n object on the battlefield is the culminating model of a much more complex representation of the logistics infrastructure that can stretch across an entire country or even around the world. The logistics model must also include mistakes and interference that cause it to breakdown and deprive the military objects of the supplies that keep them operating. A logistics model may be driven by textbook ratios of consumption or it may be include specific messages from the military objects about the levels of supplies consumed. In the latter case, a communications model is needed to carry information about what materials that are being consumed, by whom, and where they are located.

Communication is another model of exchange. The thing being exchanged is information rather than physical items. In the modern military, the amount of information that is carried around in a physical form, such as a book, letter, or paper map, is quite small compared to the amount that is transmitted in digital form. Therefore, modern models focus on communications in the form of digital computer and analog radio networks. A model of radio communications, like that of a sensor, may include the characteristics of the transmitter, transmitting platform, environment, the receiver, the geometry between the sender and receiver, and interference by other objects. Details in the representation of the radio or the signal it generates call for corresponding details in the receivers, environment, and countermeasures.

Military models of digital computer communications are similar to the tools used to study Internet traffic. They represent the senders, receivers, relay nodes, interference from competing traffic, multiple paths for the information to travel, and the loss of a message or the failure of a network. Modeling how people, objects, and units respond to the receipt of this information is included in the section on reasoning.


Engagement is strictly a form of exchange. The item being exchanged is a weapon and the effect is the degradation of the operational capabilities of the target. Most military simulations perform movement, perception, and exchange specifically so they can put themselves in a position to engage an enemy target. Engagement has historically been the pivotal centerpiece of a simulation system and one of the most important models in the system. Certainly, not all objects engage the enemy, but those that do not are often referred to as support elements whose mission is to make engagement possible for combat equipped units (Smith, 2000).

An engagement model typically includes the exchange of weapons or firepower from a shooter to a target. This exchange decrements the capability of the shooter by expending ammunition in one of its many forms (e.g. bullets, missiles, bombs, rockets, grenades, artillery rounds). Just as in the perception and communication models described above, this exchange is usually impacted by the geometry between the shooter and the target. The engagement may also be mitigated by the environment and characteristics of the target. Trees, terrain, water, and buildings may interfere with the optimal delivery of the weapon and reduce its impact on the target. The target may also contain defensive systems that counter the effects of the engagement. A defensive model may represent the effects of flares or chaff in deceiving and misleading a guided missile or the protective effects of armor to deflect the weapon.

If the weapon successfully impacts the target and is powerful enough to overcome any interference or defenses, then a level of attrition must be calculated for the target. Different approaches to modeling attrition are described in the next section. Attrition is usually directed at the model state variables that control its ability to perform its primary functions. These may include health or strength, fuel levels, communications capabilities, and mobility. Models may also make a binary decision about whether a vehicle, human, or unit it completely destroyed or not.

The attrition model may be linked to communications and medical models. Communications models propagate the outcome of an engagement so that units or operators are aware that an engagement has occurred. These communications may trigger a medical model that will attempt to conduct extraction and provide medical treatment to simulated humans that are wounded. It may also trigger the logistics model to extract and repair vehicles.


Within large military simulation systems, there are usually many models of human decision-making and behaviors. These have become more prevalent as systems have grown in both the breadth of coverage and the depth of detail of the battlefields that are represented. Representing human thinking and even some computer reasoning are one of the most challenging parts of the current practice of military modeling. This type of information processing is largely not understood and general approximations and simplifications are captured in models.

Reasoning models often rely on the techniques developed within the Artificial Intelligence field. Techniques like finite state machines, expert systems, rule-based systems, case based reasoning, neural networks, fuzzy logic, means-ends analysis, and others are used to organize information and create decisions that are similar to those of living humans. FSM are currently the most widely used technique for both military models and those inside of commercial games. These reasoning models are challenged to perform a wide array of operations, to include commanding subordinate units, decomposing and acting on commands from higher level units, reacting to enemy attacks, selecting maneuver routes, identifying threats and opportunities for engagement, fusing information, and extracting meaning from intelligence reports. Each of these functions can be extremely complicated and require significant computing resources to execute. Reasoning models must balance their level of realism between robotic reactions to stimuli and detailed consideration of the situation prior to selecting an action.

The variety of reasoning models that are required on a battlefield cannot be fit to a single modeling technique. In practice, multiple techniques are required, each applied to a reasoning problem for which it is best suited.

Dynamic Environment

Earlier we described the evolution of the simulated environment from static state structures to dynamic representations of features and their interactions with military objects. Military objects interact with the environment both through direct intention and through accidental collocation. An engineering unit may be tasked to destroy a bridge or a road. This is an operation in which the effects on the environment are the specific intent of the action. In another case, an aircraft may bomb a convoy of trucks moving on a road. In this case, the trucks are the primary targets, but the road may sustain damage because of its collocation with the trucks.

Until recently, military simulations seldom included impacts on the environment. However, with the current focus on precision operations, there is much more interest in destroying specific buildings, roads, bridges, communications equipment, and pieces of the social infrastructure. Since this data is usually found in the environmental database, models that accurately modify environmental information are needed.

For decades, military organizations have worked on models that accurately represent the engagement that takes place between two tanks, airplanes, or ships. It is becoming necessary for those models to also impact the trees, terrain, and roads in the vicinity of these engagements. This means that information on the effects of weapons on trees is necessary, as well as their effects on buildings, roads, bridges, and a host of other types of surrounding terrain.

Even though these models are making the environment a dynamic part of the simulation system, the type and level of damage done to a tree is seldom the focus of the experiment or exercise that is being conducted. Therefore, the detail in these models is not as critical that in the models that govern the dynamic changes to other military objects.

Tuesday, October 25, 2005

Simulation Categories

[NOTE: Freewriting first draft for the "Military Modeling" chapter of the upcoming book, Dynamic Modeling, edited by Paul Fishwick, to be published by CRC Press in 2006.]

Over the past several decades, a number of different types of models have been developed for representing a military system or mission. These have gradually converged into commonly recognized categories of representation. These categories have significantly improved the ability of military modelers to communicate with each other and to exchange models without misunderstanding the differences between the products being created.


Engineering models focus on the details of what a system does. These capture the physical properties of materials, liquids, aerodynamics, servomechanisms, and computer control of specific systems. They also include interactions between two physical objects or between an object and its environment. An engineering model attempts to understand the physical capabilities of the system at a level that is accurate enough to be used to design the system. Historically, physical prototypes were used to conduct these experiments. However, advanced computer technologies and modeling techniques have allowed us to create digital models of systems that are nearly as predictive as are live physical tests. These models offer many advantages over their physical counterparts. They are almost infinitely malleable so that experiments can be conducted on many thousands of variations rather than just a few physical prototypes. They are nearly infinitely instrumentable. It is possible to collect data from all points in space and time around the event of interest. When using physical prototypes we are often limited by our ability to place sensor, communication, and recording equipment at the precise place and time of interest.


A “virtual model” often refers to a three-dimensional representation of a system that is operating in a digital three-dimensional environment. The focus is usually on the visual appearance of the object and the environment, more than on the properties of physics that were the focus of engineering models. Because of its visual focus, the objects most often represented are military vehicles and humans that would appear on a battlefield. This category is closely aligned with the more popularly recognized term, “virtual reality”.

A virtual model and environment are usually constructed to simulate individual soldiers who are immersed in a system that generates visual, aural, and tactile stimuli. The goal is usually to train, test, or measure the ability of the human to respond in a desirable manner to the stimuli. Flight simulators are the most popularly recognized form of these models and systems.


A “constructive model” represents an accumulation or aggregation of a number of objects, behaviors, and properties. In order to deal with the incredibly large and complex missions of the military, a very structured organizational hierarchy has evolved. To represent the information that is available at the different levels in this hierarchy and to represent the functions of the hierarchy itself, constructive models have been created.

A constructive model may represent a flight of four aircraft as a single item in the simulation. It may also group several hundred vehicles, humans, and equipment into a single object model. This model must then represent the aggregated behaviors of its many different constituent parts. There are a number of motivations for this type of modeling. First, it allows the simulation system developers to capture the operations of a much broader battlefield in a form that can be run on a reasonable computer suite. Second, in many cases the behavior of groups of objects are not understood at the engineering or virtual level, but can be represented as a higher-level aggregate. Third, this type of model mimics the organization, representation, and information that are used in the real military organizational hierarchy.

Very basic constructive models of military operations can be seen in many board and computer games, such as Chess, Stratego, and Risk. Constructive simulation systems differ from virtual systems in that the human operator or player is often positioned outside of the battle. Engagements are not usually targeted at the human player, so they are in a position to think more strategically about the situation and are not required to react to individual events that appear to threaten them personally, as would occur in a virtual system.


Though a “live model” appears to be an inappropriate description, the term has been adopted to refer to activities in which live humans, vehicles, and equipment engage in mock combat. The combat events do not involve real munitions and attempt to avoid situations that could have lethal outcomes. Using computer, communication, navigation, and laser technologies, training areas have been constructed in which combatants can use their real weapons in a form that is as physically realistic as possible. Laser beams often replace bullets and computer messages indicate where bombs are dropped.

Live modeling allows humans to train in the real environment, to experience the physical hardships of traversing rough terrain, operating in the desert sun, and experiencing the effects of dirt and water on the equipment. The humans and vehicles become living models in a living simulation. In many cases, these live participants are also supplemented with virtual and constructive models to enrich the entire training experience.


The model of the environment has historically been a static representation of terrain, vegetation, roads, rivers, wind, clouds, rain, ocean waves, salinity, ocean bottom, and any number of other features. This environment has provided a medium within which the above models operated. The environment impeded the movement of objects, obstructed sensor visibility, and changed the outcomes all types of operations. However, in the midst of all of this activity, the environment itself remained static and unchanged. A bomb dropped on a truck may destroy the truck, but make no change to the underlying terrain or the surrounding vegetation.

Recently, this has been changing. Military simulation systems have included dynamic models of the interaction between military systems and environmental features. Simulated objects are able to knock down trees, crater roads, dig holes, build barriers, and destroy buildings. To support this, a new form of environmental model has evolved which understands the physical effects of vehicles and weapons on dirt, trees, and masonry block structures. Environmental modeling is no longer limited to static data structures, but includes dynamic models that respond to military operations.

Monday, October 24, 2005

Military Simulation

[NOTE: Freewriting first draft for the "Military Modeling" chapter of the upcoming book, Dynamic Modeling, edited by Paul Fishwick, to be published by CRC Press in 2006.]

The United States military has made its own unique definitions of the terms “modeling” and “simulation.” For their purposes, modeling is often defined as, “a descriptive, functional, or physical representation of a system” (NSC, 2000). These representations may take the form of a mathematic equation, a logical algorithm, a three-dimensional digital image, or a partial physical mock-up of the system. Models are applied so widely that the variety of systems of interest is almost without bounds. In these systems military weapons systems are usually very prominently represented, to include land, air, and sea vehicles; communications and radar equipment; hand-held weapons; and individual soldiers. But models also represent the decision-making process and automated information processing that occur inside the human brain and within battlefield computers. They extend to representations of the environment that is made up of terrain, vegetation, cultural features, the atmosphere, ocean, and RF environment. Different combinations of these are needed in order to accurately represent potential military situations.

One military definition of simulation is, “a system or model that represents activities and interactions over time. A simulation may be fully automated, or it may be interactive or interruptible” (NSC, 2000). This definition attempts to encompass human-in-the-loop simulators for training, as well as systems that serve as analytical tools for computing outcomes without the aid of a human participant.

The official categorization of the use of models and simulation within the military is to divide them into three large application groups.

The first is for use in “requirements and acquisition”. In these applications, models are used to provide insight into the cost and performance of military equipment, processes, or missions that are planned for the future. These use scientific inquiry to discover or revise facts and theories of phenomena, followed by transformation of these discoveries into physical representations.

The second category is in exploring “advanced concepts”. These models present military systems and situations in a form that allows the military to conduct concept exploration and trade studies into alternatives. These trade studies often explore multiple variations on a new weapon or tactic and attempt to measure the effectiveness of each of them. The result is a general appreciation for the different options available and some rough measure for ranking them. The models may be used to understand physical weapons or equipment, but they may also explore different processes for organizing and executing a mission. These require an understanding of processes and the interactions that occur between different steps in their processes. The models assist the military in creating its doctrine of operations, constructing its internal organization, and selecting materials for acquisition.

The third category is in “training and development”. Models that are embedded in a simulation system are used to stimulate individuals and groups of personnel with specific military scenarios. The goal is to determine the degree to which they have learned to execute the doctrines they have been taught. It also gives them the opportunity to experiment with new ideas and to determine how useful these might be in a real warfighting situation. All of this can be done in a controlled environment that is free of life threatening situations that are part of real combat operations.

Finally, it should be noted that military modeling and simulation has always been the basis for a large segment of entertainment products. Many of the modeling concepts behind paper board-wargaming in the 1950’s were developed simultaneously by the RAND Corporation for serious military training and by Charles Roberts at the Avalon Hill game company for popular entertainment (Perla, 1990). This trend has continued for over fifty years and can be seen today in comparing realistic three-dimensional military training systems and the very active computer gaming industry. Systems like America’s Army provide an environment for experimentation and training in the military, a device to enhance Army recruitment and education about the military lifestyle, and a game for use by anyone looking for a little excitement in their free time.

Sunday, October 23, 2005

Hurricane Wilma

The news shows what terrible things hurricanes can be. Well, one is headed here right now. It should pass by tomorrow. We lived through 3 hurricanes last year. This year there have been 2 close ones. For tomorrow, the government has closed the schools. This is mostly a precaution against tornadoes (which are thrown off by hurricanes) and heavy rain. The hurricane is predicted to be pretty far south of us and we are only getting thunderstorms and rain. It looks like 2-4 inches of rain in a couple of hours. That is not bad for Florida, we can absorb that. There may also be high winds, 40-60 MPH. This is not bad. Last year we have 110 MPH winds at our house. All trees that were coming down are already down. The worst it can do is nock down the fence … again.

Hurricanes are terrible and can destroy your house. If you have terrible luck or terrible judgment, then they can also kill you. But for most people, we emerge after the storm and start cleaning up. Last year it took about a week to clear the trees and debris from our yards. We moved it to the curb and waited between 3 and 10 weeks for the FEMA contractors to come and get it. I caught a small snake among the debris, but it did not make a good pet. The state put all of the trees through a chipper and announced that anyone could have as much free mulch for their yards as they could carry away … very smart.

Tomorrow will not be too bad. It will just require some adjustments. No school, working from home, most businesses closed. On Tuesday everything should be back to normal. Unfortunately, the people at Punta Gorda, FL look like they are going to be hit again. They bore the brunt of Hurricane Charley last year and are getting it from Wilma this year.

Orlando is about as safe as you can get in the state of Florida. We are the high ground that the rest of the state evacuates to. Leaving Orlando means things are really bad – like a direct hit from a Category 5.

I always console myself by thinking of the 100 year-old wood frame houses that are still standing in Key West. If those have made it this long, the hurricanes are not going to totally wipe out a city.

Saturday, October 22, 2005

Mixed Methods

This week’s case study illustrated the different strengths of qualitative, quantitative, and mixed methods of research. In the process of extracting a purely qualitative and quantitative approach to the problem it became clear that real organizations are faced with complex problems that interweave both qual and quant issues. I believe that for all practical purposes, mixed methods are necessary to understand and solve real business problems. It is not sufficient to understand the financial problems of the company, without also understanding how these are impacting the morale of the employees.

Businesses are social structures that handle money. They are not financial structures that use people as parts. Though an organization may report its progress and activities in financial terms, all of that can only be accomplished through the application of human effort that is willingly applied. Money is a motivator to buy effort, but you cannot pay for all of the effort that is available from a person, and you cannot afford it even if you could. You must create an organization that generates social and psychic payment for services as well as financial payment. In this sense you pay people by the way you treat them and make them feel. This generates additional effort beyond 40 hours/week.

Mixed research methods provide the flexibility necessary to be able to explore all areas of the complex social structure known as a business. Measuring finances or production rates is difficult. Measuring the human dynamic of the organization is also difficult. Finding a way to combine the two and identify a relationship between them is much more difficult. Figuring out how to convert revenue into emotional satisfaction is a form of alchemy and the formula for the conversion is different in every organization.

Friday, October 21, 2005


Regression is the creation of a model that predicts dependent variables from independent ones. We have been doing a lot of linear regression, assuming that there is a linear relationship between all of the variables involved. This is a trend that I see in other works as well. When a relationship is not linear, there are sometimes techniques to transform the variables into a form that is linear.

Nonlinear regression is not as strictly guided as is linear. It requires that the analyst look for a number of different patterns and equations that could fit the data.

Regression is also something that hypnotists do to take a subject into their past and examine a situation that has been forgotten or obscured. When I was a teenager, hypnosis was all the rage. I have not seen or heard anything of it in years. I am not at all certain that it still exists in a credible form. There was a time when people were hypnotized and claimed to recall their past lives. On the surface this looks cool, but you have examine what is really happening here. Is hypnosis really any different than sleeping? If it is similar that perhaps what we are discovering is a semi-conscious dream. I have dreamed a lot of things and very seldom expected any of it to be a true reflection of reality.

Regression is something that people do themselves when they fall back or retreat back to a previous state. They lose or give up some capability – returning to a child-like state or just forgetting a new task they have learned. My daughter regresses in her violin skills occasionally.

I am writing along and I digress in topic, take myself from one point to another until I am away from the original subject. If I regress while writing, does that mean that I fall back and write the same thing over again? Does it mean that I fall back and write the same things over again? I am digressing from the original regression. What is digressive regression or regressive digression?

Thursday, October 20, 2005


Epistemology is the study of knowledge or truth. In this class I begin to see that the methods of determining what is true had to be created just like many technologies were. I think that “belief” was one of the first sources of knowledge, and still one of the largest. People needed an authoritative source and knowledge had not been preserved in an objective form yet. But it was being preserved in the form of legends and beliefs. So these became the foundation of what a person should believe. However, once a more scientific method was created and knowledge could be preserved, it was inevitable that this would supplant belief and the primary foundation or source of knowledge.

Perhaps it is not true that science overthrew religion because the former was objective and the latter objective. Perhaps Satanic forces were not a factor at all. Instead, people and society needed an effective source of working knowledge. Science provided new information that “worked”. It lead to better crop production or animal husbandry. There was not really a war between religion and science, but rather between productivity and nonproductivity.

I saw a pictures where knowledge was portrayed as the intersection between truth and belief. This is an interesting picture. It means that knowledge resides in our heads, arrives through one of the two conduits, and must simply satisfy both constraints in order to be working knowledge. However, I think people also retain some knowledge that is simply belief without, or perhaps in spite of, truth. People are not completely logical. Everyone has their own favorite beliefs. They all want some things to be true in spite of the facts. These bits of knowledge must often be kept private or masked as “fun” in order to be preserved and practiced.

Epistemology is the study of knowledge. Understanding why we know things is an interesting field. But it is hard to imagine evolving into such a creature. And is such a person even useful? Do they actually create new gateways to truth and knowledge? Or do they just validate the gateways that are found by other people? I don’t really know. No one in my neighborhood is an epistemologist … or can even spell it.

Wednesday, October 19, 2005

Management Consulting

Business ranks are filled with managers, some of them very well trained. So why is there such a big market for management consultants? Perhaps all of the practicing managers are so swamped with the small details of company operations that no one has the time, energy, or brain cells left to look at the big picture and see that situation it is in, or to look at the competition and see what the future looks like. How many people on a company’s staff are assigned to study the competition, and nothing else? How many people are free of daily responsibilities to look at strategy?

Perhaps, management consulting is not a business that addresses weakness in a company. Perhaps they are part of the intentional structure of the company. If you do not need this person full time it might be much more economical, as well as effective, to use an outsider. You only have to pay them for the time used, then you can cut them loose. If you do not like someone, you can send them back to the consulting company, no questions asked, no lawsuits.

But, the consultants themselves cannot bill their services as “we do what you could do yourself if you would just someone on staff.” They have to present themselves as filled with special talents and knowledge. They build an image and mystique that implies that they are beyond mere managers.

As consultants they also get to develop experience dealing with problems from the outside, prescribing solutions, and dealing with company leaders. They do not have to do this part-time while diluting their experience with daily management tasks. So they should be better at consulting on a problem and moving on.

Interesting … so it is a business born out of the need to cut internal spending and to escape legal restrictions.

Tuesday, October 18, 2005

Alligator Snapping Turtle

Yesterday we were returning from a business lunch and saw a large turtle crossing the road in the business park. This is a common occurrence in Orlando and, when possible, there are many people who stop to help the turtle get out of the road – rather than waiting to see it smashed into a pancake. In Florida there are a number of types of turtles that easily grow up to a foot or more in diameter. It is not uncommon to find a 20 or 30 year-old turtle in the road about to be demolished.

The common procedure is to stop in front of the turtle, turn on your emergency flashers, and get out of the car. Then you give the turtle a nudge with your foot and most of them will retreat into their shells. You can then push them to the curb and lift them over. Then you just wait for the turtle to emerge and heard it back into the woods. Simple, safe, and rewarding.

We did all of this for yesterday’s turtle as well. But when we got out of the car, I noticed that this turtle had a six-inch tail that had upright scales running across the top like a dinosaur. This was the first clue that it was not an ordinary turtle. When we got closer we could see that the turtle had an extremely heavy shell, armored legs, a triangular head, and a mouth shaped like a parrot’s beak. Second clue. This was not a nice docile turtle, it was an alligator snapping turtle. These animals are tough, mean, and disease carrying (don’t touch them). They act a lot like an alligator that lives in a shell.

In spite of this the rescue mission carried on. I nudged the turtle with my dress shoe to get him to close up. But, alligator snapping turtles to not retreat from a threat like this. Instead, this 18-inch long turtle (perhaps 20 pounds), shot out its neck opened its jaws, hissed like a snake, and started jumping toward me. This is where the rescuers jumped back in surprise and fright. Hmmm … maybe this turtle will have to get smashed. So we got a long stick and a small army shovel out of my truck and tried to push him around with these. This provoked even more vicious attacks. This is one tough turtle. As we circled around behind him he turned to keep his attacking face toward us. He snapped at the three-foot dowel stick like an alligator and bit down one time.

By this time several cars had slowed down to watch the show before moving on. I got around behind him and used the dowel stick to push him to the curb, while he hissed, jumped, and threatened us the whole time. Seth tried to use the shovel to scoop him up and lift him over the curb. The turtle was so aggressive that he would not stay on the shovel. Our close proximity also gave us a good whiff of his odor. This turtle stunk like a sewer. The top of his shell was not clean and smooth. It was rough like an alligator’s back and covered with moss and black slime.

Finally, Seth was able to get the shovel under him and flip him unceremoniously up and over he curb. We had figured out that we did not have to be quite so gentle, this was one tough turtle. Once in the grass the turtle stood up a good two inches and walked briskly back into the swamp from which he had come. Rescue accomplished.

Don’t mess with alligator snapping turtles. They are really an alligator that is mad for being trapped in a turtle’s body.

Saturday, October 15, 2005


The old saying that you can prove anything with statistics is plain wrong. What is really happening is that statistics are illustrating how complex a problem or question really is. When you apply measures and confidence to a problem, you are immediately limited to that part of the problem that you have quantified. Suddenly it becomes very clear that the problem has many, many facets and that there are complex interactions between them. The world that looked so simple unveils some of its complexity when you are required to describe it numerically. Mathematics in general is complex because it is an expression of the real world, which is far more complex that mathematics.

In college classes, statistics is presented as a tool that can be applied to most real world problems. In truth, statistics can be applied to very few real world situations. The real world is so complex and misbehaved that the equations we have at our disposal are insufficient for describing or understanding it. That is why real problems have to be decomposed into smaller parts. Our equations can describe these. But, once decomposed we can only make claims or predictions about those smaller pieces. Explaining the many combinations and limitations that emerge from this is where statistics gets its reputation for being criminally manipulable. Mathematicians and statisticians are constantly pushing the field into more complex equations in an attempt to create equations that can represent more of the world.

Statistics are not trusted by most people because they are too complex for them to understand. There is no quick and easy way to understand statistics. You have to start at the beginning and work your way through the complex maze. No matter how many USA Today surveys and graphs you read, you will never learn statistics from those. There are just some fields that are best left to the professionals – quantum mechanics, electrodynamics, statistics, and auto repair.

So academics in all fields find that they must master some level of statistics (but not auto repair). It is taught is most departments and is embraced and abhorred by everyone. Since it is so broadly used, it has to be broadly talked about and everyone has an opinion on it. I have no opinion on quantum mechanics (good, bad, or useful) because I do not ever think about it. But every academic has an opinion on statistics.

Friday, October 14, 2005


Wikipedia is a wonderful source of information. The world at large has come together to contribute articles on a million topics. It is like a huge collaborative encyclopedia, but it contains articles that are obscure, cultural, and entertaining. Unlike Britannica, Wikipedia has no limit on what can be posted to it. There may be an entry on the space shuttle mission that launched 30 minutes ago or one on Hispanic heroes of television. The Internet has given us some marvelous research and reading tools – Wikipedia and Google are two of the most popular. For the first time information on every imaginable topic can be found in a few moments.

But, is Wikipedia a reliable research source? Currently, the academic answer is “no”. Since there is no formal and controlled process for limiting what is posted, the academic world does not feel that it is reliable. I have seen this unreliability myself. In one of Wikipedia’s daily-featured articles on the Cat’s Eye Nebula, some contributor edited the content (perfectly normal) and replaced astronomic facts for profane sentences of a sexual nature. I was surprised to find the graffiti, and captured a screenshot of it. However, I also continued to monitor the page to see how long the article would remain corrupted. Within 9 minutes someone in the world spotted the corruption and fixed it. Clearly, there is an editorial process at work on Wikipedia. It is not a formal board of editors. Instead the editors who must approve an article are the entire world. Potentially, any articles posted will be edited and approved by 10,000 people. Possibly Wikipedia articles are subject to more critical review that anything in a journal or newspaper.

However, this informal editing process does not guarantee the quality of all million articles in Wikipedia. Some topics may have many, highly qualified people editing them, while others have few interested contributors and even fewer who are well qualified. In addition, there is no way to identify the qualifications of these contributors. Wiki does keep track of which subscribers have edited an article and when, but there is no info on who they are.

Wikipedia is like USA Today and the television news shows. It will become the primary source of information for most of the world. It has already far surpassed Britannica as the leading reference source in the world. But, the nature of its content generation will prevent it from being a major academic source … in its current form. Wiki has launched a number of specialized services. One of these could be an academic research site where the contributors register their CV’s and their contributions are labeled. This could easily evolve into an authoritative source.

Wednesday, October 12, 2005


For this entry I cheated. Rather than complete freewriting, I read the Wikipedia entry for “doctor” to prime my mental pump (

Why are we so fascinated and desirous of the title doctor? Since childhood we have heard one of the most respected members of the community called “doctor”. He may be rich or poor, he may be a close friend or a stranger, but in all cases he is respectfully called doctor. I think this creates an image of respect that we are eager to obtain. In a profession, it also indicates the highest level of learning. It is like the black belt of education.

It was interesting to learn that the title emerged in reference to educators, not in reference to medical practitioners. It was later adopted in medicine and could be used by people who had achieved only a Bachelor’s degree in medicine or surgery. By that standard I hold several doctorate titles (as opposed to degrees) already. This was later supplemented by the requirement to take a postgraduate test to attain the title – similar to the PE or CPA exam.

As I understand it, the legal profession has also begun awarding the doctor title at the completion of a master’s equivalent of study – Doctor of Jurisprudence. In graduate school I lived in a complex filled from law students and can attest that they may genuinely have earned their doctorate in those 3 years that they studied. As hard as I was working to get my MS in Statistics, they were working a lot harder for their degree, and a year longer. I suspect that medical doctors go through the same. So I would not begrudge them the title. But I would maintain that the two degrees indicate a very different understanding and practice of research. A Dr. of most subjects must learn to explore a field in a new way and make a unique contribution. I think this creates a different kind of thinking than diagnosing a disease or a legal situation.

I am looking forward to completing this degree, but I may be a little too reserved to parade the title and degree out in front of all of my colleges. It will go directly on my business cards, wall, and maybe an occasional signature line. But, it will not become part of my personal identity. Perhaps, I think the achievement is more private than public, but certainly something of professional and financial value. I work with a number of Dr.’s and find them to be a great breed of people. Smart, inquisitive, filled with interesting thoughts, and generally self-assured enough to be helpful more than boastful.

After a few centuries, I am really surprised that a higher degree has not emerged. As more people achieve Dr., I wonder if a Master-Doctor or Dr.Dr. will appear? Could it be useful? I know people doing post-doctorate research. But these are usually just students who are in transition between their studies and their professorship. It is a way to begin working in a desirable institution before being placed in a professorial track. It is also a good way to put Dr.’s to work in a research center, but without the requirements to handle classes. The growth of such positions also indicates that there are getting to be more Dr’s than are needed to carry on the educational mission of the country.

I encourage all of my new hires to get a master’s degree. It is no longer the rarity it once was. It is becoming a useful ticket in moving up an organization. Most of them are listening and some of the older workers have noticed it enough to return to earn their own MS after 10+ years in the industry.

Since medical and legal doctorates seem to have been an honorific title adopted by a profession, I think we would do well to extend this practice to mathematics or computer science, it would certainly shorten my path to the title.

Tuesday, October 11, 2005


Richard Dawson: Survey says!

You can make millions of dollars with a simple idea like this. So what can Family Feud and USA Today turn the simplest surveys into multi-million dollar franchises, and expert surveyors can barely break into the middle class? Well, mostly it is the topic of what you are asking about. People are hungry to know whether other people are interested in the same TV shows they like, shoes they wear, favorite ice cream, and best looking movie star. They will pay for that same information over and over again in the form of magazine subscriptions, television shows, cable subscriptions, Internet sites, mail flyers. But you want to understand the impact loneliness on the elderly or of stress on teachers, well that is not so great information. Everyone wants to know more about “me”. How much are other people like me? The lonely elderly person in the nursing home … nope, nothing like me, so I don’t care. The teacher who is stressed out over child discipline … nope, not my problem, should have chosen a different career. I want to hear about how many people out there like chocolate chip ice cream over vanilla. I want to know whether they prefer diet coke or diet pepsi, now that is the important stuff.

It seems people can never get enough validation. Is everyone on a giant inferiority train? Why do they have to be constantly reassured that it is ok to buy the lowest priced toilet paper and the highest priced ice cream? Who cares? We have these choices because we are lucky and fortunate in the time and place we live. Why can’t we just enjoy it, because billions of other people never get a chance to those like that.

Surveys … asking other people what they experience, like, know, feel, did, wish … blah, blah, blah. This is all very interesting. But can we start asking important questions. Can we start to understand how people are trying to improve their lives or those of others? Can we ask about how to reduce pollution or child abuse? If you can make a million dollars on a survey, it is almost guaranteed that you are asking the most meaningless questions possible. Important questions don’t pay so well.

Monday, October 10, 2005


Ennis discusses the importance of the credibility of sources. He talks a little about academic/printed sources. But he talks more about the credibility of individuals in an interview or a testimony situation. The ideas he presents are pretty obvious. Credibility comes from expertise developed from experience or training. Credibility comes from direct experience – as in a witness who actually observed an event. Credibility comes from a long reputation of being trusted or accurate. Credibility comes from having the information checked out.

He also points out that we accept information from sources all around us every day. We accept credibility in many different forms. Why do news anchors wear suits? Credibility. We are more inclined to believe someone who is wearing a suit than a shirt and tie or a polo shirt. Grave an serious information should be delivered by a person who is dressed in a serious manner. Notice that the news anchor must wear a suit, but the weatherman and the sports caster can dress down to shirt and tie. Their news is less serious. They are reporting information that comes from nature or entertainment.

Credibility comes from neighbors we trust, coworkers that we have observed, family members who raise us. There needs to be some reason to believe the person. In daily contact there is no opportunity for double-checking information. The checks come from the relationship, history, or a nice suit.

Once lost, credibility is a tough thing to earn back. How many times can a source be wrong before they are totally discounted … 1, 2, 3? Not many more than that. How many times does a source have to be right before they are trusted … 5, 10, 100?

Anonymity is a great eraser. In a small town everyone has you pegged. In a big town, you can just move your operation a few miles and start with a clean slate. Hence people moving to New York, LA, or Nashville to start with a new name and a new chance at something different. In the new mobile society, the more people move, the more opportunities they have to take another shot at their dreams or ambitions. Staying in the same old town brands you with the same old reputation. You have to leave, become an unknown, reinvent yourself, and build a new reputation if you want to move up. Credibility comes from anonymity.

America is full of fresh starts. Maybe that is what makes us different from other countries. You can leave Europe, move to America, and become a new person. You are not son of-son of-son of, going back generations. You are a new and fresh individual. I would expect the same in Australia.

Sunday, October 09, 2005


Scientifically an example is not relevant. It is a single instance, a sample of one. Such a small data point is not reliable. It cannot point to a characteristic of a population. An example is anecdotal in many cases, subject to exaggeration and interpretation. It is not solid.

But, it is interesting, that most of our memories and the prototypes of our beliefs and behaviors are all in the form of examples. The instance of one comes to represent a concept for an entire population for every situation that a person encounters. Much of the Biblical New Testament is a series of examples … stories of events that illustrate a moral of behavior or a characteristic of society. In many ways, region can be based on examples.

Scientifically we ask people to conduct experiments. Collect data on large populations. Look for trends that span many more than one instance. Even a qualitative researcher will interview multiple people in an attempt to identify and generalize a principal that is present across a population. They use examples to illustrate, but the example is not the foundation of the study.

I remember relatives, my own past, and significant events in the form of examples … snippets of brain-video that carry images, sounds, feelings, and meaning. What is the file type of this memory? A JPEG file carries graphic information. An MPEG carried graphics with sound and text. What format carries feelings and links to other snippets?

Principles of truth are almost always stored as a part of an example. There does seem to be a rulebook in my head that is like a textbook without pictures. But it is not written in words. It is written in symbols … some form of YES, NO and MAYBE are encoded to pop up when I think of an idea. Perhaps a stop sign is similar to the symbols in my head. When I see a snake, the rulebook says stop. Once this is accomplished, then the brain begins to consider what it should do about the situation. I might compare the situation to previous examples in my brain and find one that suggests the best reaction to this.

Life appears to be made up of examples. The non-scientific literature is all about examples and illustrations. Notice that the management advice books provide an example of a situation and suggest that this example will stand you well for all similar situations.

So if our minds are full of examples, then they are unscientific … but still effective. Our ability to function should be improved by creating a more scientific approach to problem solving. In fact, in the field of system dynamics, that is one of their main premises. They notice that intuitive reactions to stimuli or information are often wrong. Only by studying relationships over time can you really understand the effects of specific actions taken. Is it possible that the world will evolve into higher levels of complexity and our minds must adopt a systemic way of thinking merely to make daily decisions? This begs the question … In general, is the world evolving to higher levels of complexity? Certainly we all feel that our world is becoming more complex. But we must not trust our feelings on this. They may confuse a higher volume of simple information as a higher level of complexity.

Saturday, October 01, 2005

Week in Arizona ... On Paper

This week’s journal was done in paper while I was vacationing in Arizona. The topics discussed were:
· Exploration
· Intellectual
· Serendipity
· Service
· Patience

The last was really the only one that was any good.