Washington Post, 18 June 2004
Summary:
The Department of Homeland Security (DHS) has developed a special program designed to think creatively as to how terrorists may attack the US. Known as the Analytic Red Cell office, DHS sought out various professionals to form a "Red Team" that would serve to "think outside the box" as to new ways we may be targeted by terrorists. Various team members were "futurists, philosophers, software programmers, a pop musician and a thriller writer"
According to the article, "Typically the Red Cell team assembles 20 or so participants for a day-long session at leased offices in the Washington area. Each session divides into smaller groups and takes up a different question, such as: If you were a terrorist, how would you target the G-8 economic summit, held last week in Georgia? Another recent topic was: Why haven't terrorists hit the United States since Sept. 11, 2001?" After the Red Cell comes to its conclusions, the final reports are forwarded to intelligence analysts throughout the Intelligence Community (IC), who vet the results and compare them to actual threats and information. "Most Red Cell reports note they are 'alternative assessments intended to provoke thought and stimulate discussion'."
This technique is not new to the IC; the CIA and Pentagon have used the method since the Cold War to expand their thinking on on how the Soviets and other foreign militaries may attack the US. The reasoning behind the method is to get non-intelligence professionals to tackle intelligence related issues. According to Brad Meltzer, a thriller writer, when he was appraoched, "They said, 'We want people who think differently from the ones we have on staff.' "
Saturday, April 11, 2009
Friday, April 10, 2009
Seeing Red: Creating a Red-Team Capability for the Blue Force
Seeing Red: Creating a Red-Team Capability for the Blue Force
By Colonel Gregory Fontenot, U.S. Army, Retired
Military Review, September-October 2005
Summary:
In response to the difficulties the US Army was having in the Operational Environment in Operation Iraqi Freedom, Colonel Gregory Fontenot suggested that Red Teaming could better prepare the Army for the challenges they faced. He states that red teaming is “uniquely suited” for critical analysis when “executed by trained, educated, and practiced team members with access to relevant subject matter expertise.” Red teaming will also provide the soldier with a better understanding of the adversary through the adversary’s cultural lens.
Click on image for a more-clear view
Red Team Best Practices:
By Colonel Gregory Fontenot, U.S. Army, Retired
Military Review, September-October 2005
Summary:
In response to the difficulties the US Army was having in the Operational Environment in Operation Iraqi Freedom, Colonel Gregory Fontenot suggested that Red Teaming could better prepare the Army for the challenges they faced. He states that red teaming is “uniquely suited” for critical analysis when “executed by trained, educated, and practiced team members with access to relevant subject matter expertise.” Red teaming will also provide the soldier with a better understanding of the adversary through the adversary’s cultural lens.
Click on image for a more-clear view
- Political and military cultures must embrace Red Teaming
- Embracing criticism is foremost among the internal cultural challenges
- Political and military organizations must prize intellectual assessments and value intellectual preparation as seriously as physical preparation
- All services must institutionalize red teaming by way of a doctrinal foundation and organizational support structure
- Leaders must provide the top cover to protect and mentor red teamers, charter the red team and the organization to solve problems, and encourage robust interaction between red and blue (in which blue learns).
- Leaders must balance red team independent action with accountability to the command
- Red teaming must be employed throughout the decision making process but with calculated application – not too heavy, not too light – so promising ideas can thrive without prejudging
- Red teams must be chartered to continue to learn and adapt
- Red team members must be highly qualified experts in their fields and have sound reputations and even temperaments
- Individuals and teams must be educated, trained, and certified in the context of doctrine on a recurring basis
- The red team member presenting the opposing or alternate view must be credible, perceptive, and articulate
- Red team members must be intellectually honest with a heavy dose of ego suppressant
Red Teaming for Law Enforcement
Red Teaming for Law Enforcement
By Michael K. Meehan, Captain, Seattle Police Department
Summary:
Michael Meehan posits that, just as the military and private industry use red-teaming techniques to discover abilities, vulnerabilities, and limitations; the law enforcement community can do the same in order to reduce threats and improve responses to issues of homeland security. The author states that red teaming refers to a variety of exercises, but the “most basic level of red teaming is to conduct peer review of plans and policies to detect vulnerabilities or perhaps to simply offer alternative views of scenarios.” Meehan also lists a variety of definitions given by other experts and organizations including the DHS Exercise and Evaluation Program which states that red teaming is, a “group of subject matter experts with various appropriate disciplinary backgrounds, that provides an independent peer review of plans and processes, acts as a devil’s advocate, and knowledgably role-plays the enemy using a controlled, realistic, interactive process during operations planning, training, and exercising."
The role of the red team is to “evaluate a target or tactic, but not the likelihood that a particular target will be attacked. Red team members are strategists who identify what to attack and domain experts who identify how to attack.” They are adaptive to the strategies of the blue team, allowing the blue team to engage in both prevention- and protection-related activities.
The role of the blue team is to “think about how surprise attacks might occur, identify indicators and warnings of those attacks, collect intelligence on those indicators, and adopt defenses against the most likely possibilities or at least provide early warning.”
Meehan describes two very common types of red teaming – analytical red teaming and physical red teaming. Analytical red teaming “provides a potential adversary’s view of threats, vulnerabilities, and countermeasures. Without testing the physical limitations of antiterrorism measures, analytical red teaming can challenge prevailing views, prevent surprise, allocate resources, and expand the bounds of imagination. Analytical red teaming can occur as part of a discussion-based exercise or as a standalone activity.”
Physical red teaming involves the physical portrayal of an actual adversary executing the tactics and strategies carried out by enemies.
Strengths:
Weaknesses:
How to:
By Michael K. Meehan, Captain, Seattle Police Department
Summary:
Michael Meehan posits that, just as the military and private industry use red-teaming techniques to discover abilities, vulnerabilities, and limitations; the law enforcement community can do the same in order to reduce threats and improve responses to issues of homeland security. The author states that red teaming refers to a variety of exercises, but the “most basic level of red teaming is to conduct peer review of plans and policies to detect vulnerabilities or perhaps to simply offer alternative views of scenarios.” Meehan also lists a variety of definitions given by other experts and organizations including the DHS Exercise and Evaluation Program which states that red teaming is, a “group of subject matter experts with various appropriate disciplinary backgrounds, that provides an independent peer review of plans and processes, acts as a devil’s advocate, and knowledgably role-plays the enemy using a controlled, realistic, interactive process during operations planning, training, and exercising."
The role of the red team is to “evaluate a target or tactic, but not the likelihood that a particular target will be attacked. Red team members are strategists who identify what to attack and domain experts who identify how to attack.” They are adaptive to the strategies of the blue team, allowing the blue team to engage in both prevention- and protection-related activities.
The role of the blue team is to “think about how surprise attacks might occur, identify indicators and warnings of those attacks, collect intelligence on those indicators, and adopt defenses against the most likely possibilities or at least provide early warning.”
Meehan describes two very common types of red teaming – analytical red teaming and physical red teaming. Analytical red teaming “provides a potential adversary’s view of threats, vulnerabilities, and countermeasures. Without testing the physical limitations of antiterrorism measures, analytical red teaming can challenge prevailing views, prevent surprise, allocate resources, and expand the bounds of imagination. Analytical red teaming can occur as part of a discussion-based exercise or as a standalone activity.”
Physical red teaming involves the physical portrayal of an actual adversary executing the tactics and strategies carried out by enemies.
Strengths:
- Offers an element of surprise
- Tests the fusion of policy, operations, and intelligence
- Highlights deviations from doctrine
- Improves blue team capabilities through practice
- Improves information sharing
Weaknesses:
- Preparation needed to plan scenarios
- Interpretation , distribution, reception of lessons learned can vary
How to:
- Determine the objectives or desired results
- Communicate with government and private partners
- Determine the scale and type of exercise, the type of scenario, the method of evaluation, and the documentation plan
- Develop the scenario
- Identify and train the appropriate participants
- Conduct and evaluate the exercise
- Prepare thorough documentation
- Evaluate the performance
- Develop the improvement plan
- Make required and desired improvements
- Exercise again
Thursday, April 9, 2009
The Role And Status Of DoD Red Teaming Activites
Defense Science Board Task Force
September 2003
Sections I-III,VI
Summary
Red teams can be a powerful tool to understand risks and increase options. Their purpose is to reduce an enterprise’s risks and increase its opportunities. Red teaming can be used at all three levels of an enterprise: strategic, operational, and tactical. The Defense Science Board, however, found that the use of red teams within the Department of Defense is mixed at best.
Despite this mixed record, the Defense Science Board concluded that the use of red teams is especially important given today’s climate. Adversaries are tough targets for intelligence (compared to the Cold War). Red teaming can both complement and inform intelligence collection and analysis. Aggressive red teams challenge emerging operational concepts in order to discover weaknesses before real adversaries do. Red team also tempers the complacency that often follows success (referenced to the time period following Desert Storm).
Red teams come in many varieties and there are different views about what constitutes a red team. The Defense Science Board defined the term broadly, including not only playing the adversary, but also playing devil’s advocate and related roles. While differing in some respects, these activities all have in common the challenging of an organization’s norms. A red team is comprised of individuals selected for their special subject matter and expertise, perspective, imagination, or critical analysis. The red team itself is only one element in a red teaming process. Elements of the process include who the red team reports to, how the red team interacts with the management of the enterprise, and how the enterprise considers the use of the red team’s products.
Although red teaming is important, it is not easy and rarely done well. Typical causes for red team failure include the read team not taking their tasking seriously, the red team loses its independence, and the red team becomes removed from the decision making process. Conversely, attributes of an effective red team include an environment that values internal criticism, “top cover” or the support of upper level management to raise issues that may be unpopular, and proper staffing on the red team.
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September 2003
Sections I-III,VI
Summary
Red teams can be a powerful tool to understand risks and increase options. Their purpose is to reduce an enterprise’s risks and increase its opportunities. Red teaming can be used at all three levels of an enterprise: strategic, operational, and tactical. The Defense Science Board, however, found that the use of red teams within the Department of Defense is mixed at best.
Despite this mixed record, the Defense Science Board concluded that the use of red teams is especially important given today’s climate. Adversaries are tough targets for intelligence (compared to the Cold War). Red teaming can both complement and inform intelligence collection and analysis. Aggressive red teams challenge emerging operational concepts in order to discover weaknesses before real adversaries do. Red team also tempers the complacency that often follows success (referenced to the time period following Desert Storm).
Red teams come in many varieties and there are different views about what constitutes a red team. The Defense Science Board defined the term broadly, including not only playing the adversary, but also playing devil’s advocate and related roles. While differing in some respects, these activities all have in common the challenging of an organization’s norms. A red team is comprised of individuals selected for their special subject matter and expertise, perspective, imagination, or critical analysis. The red team itself is only one element in a red teaming process. Elements of the process include who the red team reports to, how the red team interacts with the management of the enterprise, and how the enterprise considers the use of the red team’s products.
Although red teaming is important, it is not easy and rarely done well. Typical causes for red team failure include the read team not taking their tasking seriously, the red team loses its independence, and the red team becomes removed from the decision making process. Conversely, attributes of an effective red team include an environment that values internal criticism, “top cover” or the support of upper level management to raise issues that may be unpopular, and proper staffing on the red team.
Wednesday, April 8, 2009
Summary of Findings: Decision Trees (3 out of 5 stars)
Definition:
A visual representation exploring all possible courses of action and the resulting consequences to aid in the decision making process. Decision trees are comprised of nodes (decisions/consequences), branches (links between nodes), and probabilities. The resulting form resembles a tree.
How to:
Strengths:
Experience:
In order to get a feel for decision tree analysis, the group, while sitting in the bar across from the intel building, decided to conduct a decision tree analysis on whether or not we should do a decision tree experience exercise. This was the general consensus of decision tree analysis based on that exercise.
A visual representation exploring all possible courses of action and the resulting consequences to aid in the decision making process. Decision trees are comprised of nodes (decisions/consequences), branches (links between nodes), and probabilities. The resulting form resembles a tree.
How to:
- Begin from the top or the left-hand side as outcomes may flow from either left-to-right, or from top-to-bottom.
- Define the problem/original decision visually represent this by using a rectangle (or box) around the decision to be made. This original decision is referred to as the "decision node."
- Identify all possible courses of action that stem from that decision. The courses of action must be mutually exclusive and exhaustive. Each course of action should have a "branch" stemming out from the decision node.
- Identify "chance nodes" (represented with circles) that represent the possible outcomes of the courses of action. Different outcomes should stem out from this chance node.
- Sometimes branches emanating from decision and chance nodes can lead to other decision nodes - repeat steps 2 & 3 if this occurs, this will effectively “overgrow” the tree.
- Indicate the associated probability (likelihood) that a particular outcome stemming from a chance node will occur. Probabilities are quantified with a value ranging from zero to 1. Therefore a probability of 0.6 would be the equivalent of a 60% chance. Use your experience and knowledge, as well as any conclusions from literature or other supporting data to assign a probability value.
- The sum of the probabilities of all outcome branches stemming from a single chance node must equal 1.
- When final consequences are identified, use a filled-in circle to represent that consequence.
- Start “pruning” the decision tree by eliminating leaves and branches that are not (or are less) probabilistic.
Strengths:
- Structured, allowing for transparent recognition and interpretation of the constructed model
- Visual representation – good tool for presenters and audiences
- Provides an audit trail for the decision maker
- Applicable to multiple disciplines
- Produces quantifiable estimates
- More organized than a mind map
- High volume of quantitative data requires high level of mathematics capability
- Risky when choosing the "correct" variable to subdivide data
- Susceptible to "blind spots"
- Assigning probabilities may entail guesswork
- Susceptible to bias
Experience:
In order to get a feel for decision tree analysis, the group, while sitting in the bar across from the intel building, decided to conduct a decision tree analysis on whether or not we should do a decision tree experience exercise. This was the general consensus of decision tree analysis based on that exercise.
- It was difficult to assign probabilities without prior data that specifies what the likelihood is
- It is subjective and open to analysts' bias
- Can get messy if using pen and paper
- Open to wild cards and cognitive blindspots
- Does make the analyst think through the decisions and think outside of the box
- The visual representation makes the choices and consequences clear
- It does produce an estimation
Monday, April 6, 2009
An introduction to decision tree modeling
http://www.udel.edu/chemo/SDB/~pdf_papers/JChemo_18_275_2004.pdf
Summary:
The article offers a critique of decision tree mapping as a technique used to generalize data sets.
"In its simplest description, decision tree analysis is a divide-and-conquer approach to classification. Decision trees can be used to discover features and extract patterns in large databases that are important for discrimination and predictive modeling." It most common use is when using exploratory data and predictive modeling applications.
Decision mapping's advantages include recognizing the interpretability of the constructed model, as well as determining inter-dataset relationships. It takes the form of a hierarchical model formed by decision rules represented by nodes. The first node is reffered to as the branch node, with subsequent nodes reffered to as leaf nodes.
The general consesus is to overgrow a decision tree by incorporating all relevent criterion. The tree can then be "pruned" to reduce complexity. Generalizability is enhanced by incorporating ensemble methods such as bagging (randomly selecting samples) or boosting (reweighting criterion).
Summary:
The article offers a critique of decision tree mapping as a technique used to generalize data sets.
"In its simplest description, decision tree analysis is a divide-and-conquer approach to classification. Decision trees can be used to discover features and extract patterns in large databases that are important for discrimination and predictive modeling." It most common use is when using exploratory data and predictive modeling applications.
Decision mapping's advantages include recognizing the interpretability of the constructed model, as well as determining inter-dataset relationships. It takes the form of a hierarchical model formed by decision rules represented by nodes. The first node is reffered to as the branch node, with subsequent nodes reffered to as leaf nodes.
The general consesus is to overgrow a decision tree by incorporating all relevent criterion. The tree can then be "pruned" to reduce complexity. Generalizability is enhanced by incorporating ensemble methods such as bagging (randomly selecting samples) or boosting (reweighting criterion).
The Incident Decision Tree: Guidelines for Action Following Patient Safety Incidents
http://www.ahrq.gov/downloads/pub/advances/vol4/Meadows.pdf
Summary:
An Incident Decision Tree (IDT) was formulated to address patient safety issues arising in the United Kingdom. The IDT was developed by the National Health Service (NHS) in response to the alarming number of suspensions incurred by medical staff when patient safety issues arose.
The IDT provides human resource decision makers with a tool for determining the correct line of action when dealing with personnel in the wake of a patient safety issue. The overwhelming course of action before the IDT's implementation was a suspension for the health practitioner. The IDT proposes several different options for managers to consider after working their way through the tree. Applying the IDT allows the manager to implement fair and consistent actions resulting from patient safety mishaps.
The decision maker must think through the system and organization variables in the management of error from the prompts displayed throughout the tree. There are four consecutive trees a manager must consider about the situation. The first is the deliberate harm test (harm is intended), the incapacity test (the practitioner is sick, on medication, or has substance abuse problems), the foresight test (uncertain about protocal, protocal is unstated or poorly adhered to), and the substitution test (an equal would have acted differently). Each subsequent test is applied after the previous test was deemed inappropriate.
The pilot use of the IDT resulted in fewer suspensions for health care practitioners. Also, this lessened to a certain extent the "blame culture" associated with health care providers about self-reporting patient safety issues. Managers focused more on the "what" of the situation, rather than the "who."
A certain weakness was noted when human resource decision makers used the paper version of the IDT. Some decision makers chose suspension as the course of action and then worked backwards through the tree to justify their course of action.
Summary:
An Incident Decision Tree (IDT) was formulated to address patient safety issues arising in the United Kingdom. The IDT was developed by the National Health Service (NHS) in response to the alarming number of suspensions incurred by medical staff when patient safety issues arose.
The IDT provides human resource decision makers with a tool for determining the correct line of action when dealing with personnel in the wake of a patient safety issue. The overwhelming course of action before the IDT's implementation was a suspension for the health practitioner. The IDT proposes several different options for managers to consider after working their way through the tree. Applying the IDT allows the manager to implement fair and consistent actions resulting from patient safety mishaps.
The decision maker must think through the system and organization variables in the management of error from the prompts displayed throughout the tree. There are four consecutive trees a manager must consider about the situation. The first is the deliberate harm test (harm is intended), the incapacity test (the practitioner is sick, on medication, or has substance abuse problems), the foresight test (uncertain about protocal, protocal is unstated or poorly adhered to), and the substitution test (an equal would have acted differently). Each subsequent test is applied after the previous test was deemed inappropriate.
The pilot use of the IDT resulted in fewer suspensions for health care practitioners. Also, this lessened to a certain extent the "blame culture" associated with health care providers about self-reporting patient safety issues. Managers focused more on the "what" of the situation, rather than the "who."
A certain weakness was noted when human resource decision makers used the paper version of the IDT. Some decision makers chose suspension as the course of action and then worked backwards through the tree to justify their course of action.
Sunday, April 5, 2009
Risk Based Methodology For Scenario Tracking, Intelligence Gathering, and Analysis For Countering Terrorism
Horowitz, Barry M. and Haimes, Yacov Y.. Systems Engineering, Vol. 6, No. 3, 2003, p. 152-169, 17 p.
Summary:
The authors of the paper apply a variety of methods to the problem of countering terrorism. One of the methods they apply is the use of Multiple-Objective Decision Trees (MODT). The use of MODT allows the intelligence community to make informed decisions under conditions of uncertainty. The authors propose using the method as the final step in a process that utilizes other methodologies, such as Bayesian analysis. "Finally, a decision-making mechanism is needed that can utilize the added knowledge derived from newly-discovered intelligence and make use of Bayesian analysis. In particular, the noncommensurate objectives—effort (cost and time) and risk—must be addressed in the multiobjective tradeoff analysis. Follow-up actions could vary from calling for special new information, to calling in experts to further evaluate the data, to initiating interception of the anticipated terrorist activity."
The authors state that the problem with traditional decision trees as applied to intelligence analysis is that the problem of the intelligence analyst is often too broad to be effectively represented by a decision tree. "In particular, an optimum derived from a single-
objective mathematical model, including that derived from a decision tree, often may be far from
representing reality, and thereby may mislead analysts as well as decision-makers." Instead, the authors propose MODTs as a effective methodology to assess decision making when their are multiple objectives, such as countering terrorism (thwart a plot, interdict the terrorists, dismantle their finances, kill the terrorists, etc.). Through the use of the MODT, decision makers and analysts begin with all objectives formulated, and then make trade-offs based on which set of decisions is most desirable for the given situation.
Comment: This paper further demonstrates the mathematical equations of MODT as opposed to traditional single objective decision trees. I did not include the equations in this summary, please see the article for the mathematics behind the methodology, as well as introductions to other methodologies that the authors propose to better your counterterrorism analysis.
Summary:
The authors of the paper apply a variety of methods to the problem of countering terrorism. One of the methods they apply is the use of Multiple-Objective Decision Trees (MODT). The use of MODT allows the intelligence community to make informed decisions under conditions of uncertainty. The authors propose using the method as the final step in a process that utilizes other methodologies, such as Bayesian analysis. "Finally, a decision-making mechanism is needed that can utilize the added knowledge derived from newly-discovered intelligence and make use of Bayesian analysis. In particular, the noncommensurate objectives—effort (cost and time) and risk—must be addressed in the multiobjective tradeoff analysis. Follow-up actions could vary from calling for special new information, to calling in experts to further evaluate the data, to initiating interception of the anticipated terrorist activity."
The authors state that the problem with traditional decision trees as applied to intelligence analysis is that the problem of the intelligence analyst is often too broad to be effectively represented by a decision tree. "In particular, an optimum derived from a single-
objective mathematical model, including that derived from a decision tree, often may be far from
representing reality, and thereby may mislead analysts as well as decision-makers." Instead, the authors propose MODTs as a effective methodology to assess decision making when their are multiple objectives, such as countering terrorism (thwart a plot, interdict the terrorists, dismantle their finances, kill the terrorists, etc.). Through the use of the MODT, decision makers and analysts begin with all objectives formulated, and then make trade-offs based on which set of decisions is most desirable for the given situation.
Comment: This paper further demonstrates the mathematical equations of MODT as opposed to traditional single objective decision trees. I did not include the equations in this summary, please see the article for the mathematics behind the methodology, as well as introductions to other methodologies that the authors propose to better your counterterrorism analysis.
Saturday, April 4, 2009
Decision Tree Analysis
Vanguard Software Corporation
Summary:
"Decision trees are used to select the best course of action in situations where you face uncertainty." Decision tree analysis allows the analyst to find the best solution to a problem, or come to an estimation, although he lacks some information.
As an example of such a situation, the site outlines a simple game. How much would you pay to bet on the outcome of a coin toss ? If the coin comes up heads, you win $100. If it comes up tails, you lose. The game would look like this:
From a business perspective, you choose the option with the highest Expected Monetary Value, or EMV. The EMV of this game is $50, due to the probabilistic value of the game, i.e. you would statistically win 50% of the time. The formula for the EMV is :
To complicate the decision, another layer is added. Suppose your friend offers you $40 not to play the game. What should you do?
Now we have our decision tree. The square represents the decision (to play or not to play), whereas the circle represents the event, or outcome. Through decision tree analysis, you would come to the conclusion that it would be worth it for you to play, as you would statistically win at least $50.
But what if you had to pay $40 to play? That would look something like this:
This decision tree now takes into account your net loss or gain for playing the game. As you can see, it would still pay to play, however you can only expect to profit $10 rather than $50. By using decision tree analysis, we can outline all the various outcomes of a set of decisions, and then choose which decision is the most desirable.
Comment: This site is for decision tree software, DecisionPro, which calculates decisions automatically using various formulas for economic data. Although the example was developed for the business model of decision tree analysis, the same principles can be applied to intelligence analysis in order to "reduce uncertainty for the decision maker."
Summary:
"Decision trees are used to select the best course of action in situations where you face uncertainty." Decision tree analysis allows the analyst to find the best solution to a problem, or come to an estimation, although he lacks some information.
As an example of such a situation, the site outlines a simple game. How much would you pay to bet on the outcome of a coin toss ? If the coin comes up heads, you win $100. If it comes up tails, you lose. The game would look like this:
From a business perspective, you choose the option with the highest Expected Monetary Value, or EMV. The EMV of this game is $50, due to the probabilistic value of the game, i.e. you would statistically win 50% of the time. The formula for the EMV is :To complicate the decision, another layer is added. Suppose your friend offers you $40 not to play the game. What should you do?Now we have our decision tree. The square represents the decision (to play or not to play), whereas the circle represents the event, or outcome. Through decision tree analysis, you would come to the conclusion that it would be worth it for you to play, as you would statistically win at least $50.But what if you had to pay $40 to play? That would look something like this:
This decision tree now takes into account your net loss or gain for playing the game. As you can see, it would still pay to play, however you can only expect to profit $10 rather than $50. By using decision tree analysis, we can outline all the various outcomes of a set of decisions, and then choose which decision is the most desirable.Comment: This site is for decision tree software, DecisionPro, which calculates decisions automatically using various formulas for economic data. Although the example was developed for the business model of decision tree analysis, the same principles can be applied to intelligence analysis in order to "reduce uncertainty for the decision maker."
Friday, April 3, 2009
Scale Management And Risk Assessment For Deepwater Developments
Collins Ian, World Oil, May 2003, Vol. 224, Issue 5
In this article Collins discusses the issues of managing risks of inorganic scales in deepwater operations. One of the most common forms of inorganic scales are the build-up of calcium carbonate and barium sulfate. These compounds negatively affect drilling equipment, and it is extremely expensive to mitigate against their affects.
According to Collins, British Petroleum developed a process within microsoft excel, based on decision tree analysis to assess the risk of scale development and the potential damage it could cause to a deepwater operation. Two parameters are quantified in assessing scale risk, first the probability of scale formation and second the consequences. The probability of scale damage is quantified with a number between zero to one. Zero meaning no chance of happening and one meaning its certain to happen. After a probability and consequence are paired, then a value is assigned to the risk it represents.
Once the level of risk is established then a decision tree is presented and the branches are given a cost benefit analysis based on the risk they grew out of.
In this article Collins discusses the issues of managing risks of inorganic scales in deepwater operations. One of the most common forms of inorganic scales are the build-up of calcium carbonate and barium sulfate. These compounds negatively affect drilling equipment, and it is extremely expensive to mitigate against their affects.
According to Collins, British Petroleum developed a process within microsoft excel, based on decision tree analysis to assess the risk of scale development and the potential damage it could cause to a deepwater operation. Two parameters are quantified in assessing scale risk, first the probability of scale formation and second the consequences. The probability of scale damage is quantified with a number between zero to one. Zero meaning no chance of happening and one meaning its certain to happen. After a probability and consequence are paired, then a value is assigned to the risk it represents.
Once the level of risk is established then a decision tree is presented and the branches are given a cost benefit analysis based on the risk they grew out of.
Countering Terrorism: Integration of Practice and Theory
Countering Terrorism: Integration of Practice and Theory
An Invitational Conference
FBI Academy, Quantico, VA
Feb 28, 2002
Sponsored by the FBI Behavioral Science Unit
Summary:
The FBI's Behavioral Science Unit identifies decision tree techniques and data mining as "highly efficient methods for processing large volumes of data." However, they add that they need to be "tailored to the unique cultures in which they would be used," and that their use depends on a cooperative effort by those that design such methods with those who have to analyze them. The decision tree decision-making methodology is recognized by the FBI as one method to standardize responses to threats and to understand the seriousness of those threats.
After collaboration efforts to receive and organize incoming information have been established with a technical advisor and a decision tree has been created, using the decision tree can be broadly applied due to it's low technical skill demands. The FBI also adds that decision trees should serve only to report information to a decision maker, reinforcing the idea that decision trees should be suggestive - not to actually state the decision to be made.
Appendix 6 gives the most useful information regarding decision tree analysis. In this appendix, it defines decision trees as a tool to aid decision making. "The idea is to concretely identify the choice points and map the sequence of decisions from beginning to end."
Steps:
The FBI identifies the procedure for choosing a decision as backward induction analysis. In order to do this first assign a number that represents the worth or utility of the final consequence (filled-in circle). Use a 0.0 to 1.0 scale to identify worth. Next, assign a sum to each event node (the circles) that represents the expected utility of the node - this is the weighted average utility for that event node. "Finally, each decision node is a assigned a number that is the maximum value of the nodes that branch out from it."
According to the Behavioral Science Unit the benefits of using decision tree analyses are: 1) that the possible choices are explicitly made; 2) the choices are evaluated by the importance of the outcome as well as quantified with the probability for that outcome; and, 3) displays communication flow.
The FBI, once again, states that "decision trees can be used to guide decisions, not make them. The final decision is left up to the operator."
An Invitational Conference
FBI Academy, Quantico, VA
Feb 28, 2002
Sponsored by the FBI Behavioral Science Unit
Summary:
The FBI's Behavioral Science Unit identifies decision tree techniques and data mining as "highly efficient methods for processing large volumes of data." However, they add that they need to be "tailored to the unique cultures in which they would be used," and that their use depends on a cooperative effort by those that design such methods with those who have to analyze them. The decision tree decision-making methodology is recognized by the FBI as one method to standardize responses to threats and to understand the seriousness of those threats.
After collaboration efforts to receive and organize incoming information have been established with a technical advisor and a decision tree has been created, using the decision tree can be broadly applied due to it's low technical skill demands. The FBI also adds that decision trees should serve only to report information to a decision maker, reinforcing the idea that decision trees should be suggestive - not to actually state the decision to be made.
Appendix 6 gives the most useful information regarding decision tree analysis. In this appendix, it defines decision trees as a tool to aid decision making. "The idea is to concretely identify the choice points and map the sequence of decisions from beginning to end."
Steps:
- Started with a decision that must be made - the FBI uses the example of whether or not to arrest a suspect. Represent this decision with a square. This should be drawn on the left-most side of the paper/screen.
- Using lines drawn outward and to the right, identify each possible solution. Write each solution on each line.
- At the end of each line, the results are considered. Use a circle is drawn at the end of the line to identify if choices are available
- If another decision is possible, draw a square with that decision listed.
- If there is a final consequence, a solid dot is drawn with a filled-in circle at its end.
The FBI identifies the procedure for choosing a decision as backward induction analysis. In order to do this first assign a number that represents the worth or utility of the final consequence (filled-in circle). Use a 0.0 to 1.0 scale to identify worth. Next, assign a sum to each event node (the circles) that represents the expected utility of the node - this is the weighted average utility for that event node. "Finally, each decision node is a assigned a number that is the maximum value of the nodes that branch out from it."
According to the Behavioral Science Unit the benefits of using decision tree analyses are: 1) that the possible choices are explicitly made; 2) the choices are evaluated by the importance of the outcome as well as quantified with the probability for that outcome; and, 3) displays communication flow.
The FBI, once again, states that "decision trees can be used to guide decisions, not make them. The final decision is left up to the operator."
The CHAID Analysis
http://www.smres.com/CHAIDAnalysis.pdf
Summary
CHAID stands for Chi Square Automatice Interaction Detection. Its utility for business analysts is visualizing relationships between data with categorized values, with a tree image. According to this article can be especially useful for analyzing surveys, customer profiling and customer targeting.
The article provides an image of "Potential Customer Indexing," for customer targeting. The study is about penetrating a market for "residential services." The first node contains information about existing contracts in the market and the percentage of households in the target market, included in the study. The next step is selecting a variable that will sub-divide data. The variable in this study is highrise buildings. The areas in the target market are then broken down by how many high rise dwellings they have. From there they predict where they will have the most increase in business.
Summary
CHAID stands for Chi Square Automatice Interaction Detection. Its utility for business analysts is visualizing relationships between data with categorized values, with a tree image. According to this article can be especially useful for analyzing surveys, customer profiling and customer targeting.
The article provides an image of "Potential Customer Indexing," for customer targeting. The study is about penetrating a market for "residential services." The first node contains information about existing contracts in the market and the percentage of households in the target market, included in the study. The next step is selecting a variable that will sub-divide data. The variable in this study is highrise buildings. The areas in the target market are then broken down by how many high rise dwellings they have. From there they predict where they will have the most increase in business.
Decision Tree Analysis: Drawing Some of the Uncertainty Out of Decision Making
Decision Tree Analysis: Drawing Some of the Uncertainty Out of Decision Making
by William E. Marsh, PhD
Summary:
This article was written primarily to describe the benefits and how-to's of using decision trees (which Marsh commonly refers to as 'decision analysis') to make decisions in the livestock business - particularly swine veterinary practice. Although Marsh's target audience is obviously for those who either own swines or practice medicine on them, his article draws out the basics information needed to conduct a decision tree analysis; and he does so in a easy-to-understand and practical manner. For the purposes of this blog post, and out of respect of my targeted audience, I will leave out all swine references, examples, and jokes.
Marsh essentially defines a decision tree as a visual representation that logically depicts a time-sequenced flow of events with the purpose of informing a decision maker with the probability of various outcomes. It is a structured approach to making decisions when uncertainty exists that helps us to quantify and "consider the effects of chance on the outcome of a given decision." Marsh bluntly states that, "In using decision analysis, it is important to understand that the objective is not to make a prediction...[but rather, it] uses probabilities...to provide a guide for what should be done."
Steps to conducting a decision tree analysis:
by William E. Marsh, PhD
Summary:
This article was written primarily to describe the benefits and how-to's of using decision trees (which Marsh commonly refers to as 'decision analysis') to make decisions in the livestock business - particularly swine veterinary practice. Although Marsh's target audience is obviously for those who either own swines or practice medicine on them, his article draws out the basics information needed to conduct a decision tree analysis; and he does so in a easy-to-understand and practical manner. For the purposes of this blog post, and out of respect of my targeted audience, I will leave out all swine references, examples, and jokes.
Marsh essentially defines a decision tree as a visual representation that logically depicts a time-sequenced flow of events with the purpose of informing a decision maker with the probability of various outcomes. It is a structured approach to making decisions when uncertainty exists that helps us to quantify and "consider the effects of chance on the outcome of a given decision." Marsh bluntly states that, "In using decision analysis, it is important to understand that the objective is not to make a prediction...[but rather, it] uses probabilities...to provide a guide for what should be done."
Steps to conducting a decision tree analysis:
- Define the problem - what is it that we are trying to make a decision about. This will be represented visually using a rectangle (or box) around the decision to be made. Marsh refers to this as the "decision node."
- Identify a "mutually exclusive, exhaustive list of all possible courses of action to address the problem." Each course of action should have a "branch" stemming out from the decision node.
- Create a "chance node" (represented with circles) that represent the possible outcomes of a course of action. Different outcomes should stem out from this chance node.
- Sometimes branches emanating from decision and chance nodes can lead to other decision nodes - repeat steps 2 & 3 if this occurs.
- Indicate the associated probability (likelihood) that a particular outcome stemming from a chance node will occur. Probabilities are quantified with a value ranging from zero to 1. Therefore a probability of 0.6 would be the equivalent of a 60% chance. Use your experience and knowledge, as well as any conclusions from literature or other supporting data to assign a probability value.
- The sum of the probabilities of all outcome branches stemming from a single chance node must equal 1.
Working Smarter With Decision Trees
Summary
This article provides an introduction to using and creating a decision tree to visually demonstrate the process of a particular decision. This tool allows the user to create a model of the relationship of a decision, strategy, and possible outcomes. Decision trees are not only a useful asset to analysts, but are also good tools for presenters and audiences.
At the most basic level, decision trees begin with a goal and the strategy that is most likely to achieve it. The analyst should begin by formulating the main problem, then logically structuring alternative strategies and their projected results. Once the problem and the alternatives are determined, it is now time to construct a decision tree diagram.
There are four main components used in creating a decision tree.
Outcomes may flow from either left-to-right, or from top-to-bottom, so when creating a decision tree, begin from the top or the left-hand side. The first step is to identify the trigger event, or decision statement, and place it on the top or left side of the page. The article uses a Product Line Decision as a sample for this exercise and "Extend or Consolidate" as the decision statement. Since there are two separate options for this decision, both pathways branch out from the statement. The decision paths then run into subsequent decision points, resulting in the development of more options. The decision points continue to fan out all probable pathways until they reach the leaf or terminating node. Here is the Product Line Decision Tree Example:
This article provides an introduction to using and creating a decision tree to visually demonstrate the process of a particular decision. This tool allows the user to create a model of the relationship of a decision, strategy, and possible outcomes. Decision trees are not only a useful asset to analysts, but are also good tools for presenters and audiences.
At the most basic level, decision trees begin with a goal and the strategy that is most likely to achieve it. The analyst should begin by formulating the main problem, then logically structuring alternative strategies and their projected results. Once the problem and the alternatives are determined, it is now time to construct a decision tree diagram.
There are four main components used in creating a decision tree.
- Trigger Event or Decision Statement (Nodes)
- Decision Paths
- Chance Points or Nodes
- Leaf or Terminating Nodes
Outcomes may flow from either left-to-right, or from top-to-bottom, so when creating a decision tree, begin from the top or the left-hand side. The first step is to identify the trigger event, or decision statement, and place it on the top or left side of the page. The article uses a Product Line Decision as a sample for this exercise and "Extend or Consolidate" as the decision statement. Since there are two separate options for this decision, both pathways branch out from the statement. The decision paths then run into subsequent decision points, resulting in the development of more options. The decision points continue to fan out all probable pathways until they reach the leaf or terminating node. Here is the Product Line Decision Tree Example:
Use a decision tree analysis to systematically arrive at your smartest choice
Summary
Decision tree analysis is a structured, systematic method used in complex decision making problems. It consists of a diagram of the decisions, external events, and likely outcomes involved in a decision.
The article provides a schematic of the basic parts of a decision tree diagram:
In this diagram, the squares represent the possible decisions, with the extending lines representing the opposing options at the point of decision. Circles represent the external events, or points of uncertainty. The lines extending from circles indicate external, uncontrollable events and/or circumstances. The analyst can choose to list a probability above these lines to indicate the likelihood of the outcomes.
The article recommends assigning a quantitative measurement of the perceived benefit of each outcome (desirability). Once all of the outcomes are listed and contain a measure of value, it is time to evaluate the decision. Add the benefit measures of the end outcomes that trace back to a particular choice via a specific path. The preferred pathway is that which results in the highest level of desirability.
In the event where a tree has more than one decision point, it is best to calculate the latest stages first. "Identify the choice that gives the highest overall desirability and leave only that branch (removing the decision point). Do the same with the remaining squares, working your way to the left (to the first decision point in the sequence)."
Decision tree analysis is a structured, systematic method used in complex decision making problems. It consists of a diagram of the decisions, external events, and likely outcomes involved in a decision.
The article provides a schematic of the basic parts of a decision tree diagram:
In this diagram, the squares represent the possible decisions, with the extending lines representing the opposing options at the point of decision. Circles represent the external events, or points of uncertainty. The lines extending from circles indicate external, uncontrollable events and/or circumstances. The analyst can choose to list a probability above these lines to indicate the likelihood of the outcomes.
The article recommends assigning a quantitative measurement of the perceived benefit of each outcome (desirability). Once all of the outcomes are listed and contain a measure of value, it is time to evaluate the decision. Add the benefit measures of the end outcomes that trace back to a particular choice via a specific path. The preferred pathway is that which results in the highest level of desirability.
In the event where a tree has more than one decision point, it is best to calculate the latest stages first. "Identify the choice that gives the highest overall desirability and leave only that branch (removing the decision point). Do the same with the remaining squares, working your way to the left (to the first decision point in the sequence)."
Thursday, April 2, 2009
Decision Trees
Edwin Greenlaw Sapp
CIA Historical Review Program
22 September 1993
Summary
The decision tree is a prototype for the preponderance of logic diagrams. It is a linear means of representing the alternatives, objectives, and consequences of a series of decisions. The decision tree, essentially, is an algorithm for the analysis of complex sequential decision problems.
Decision trees can be used to depict a series of true-false sequences (a deterministic way) or to display subjective likelihoods and their relationships (a probabilistic use). The technique is simple:
1. Identify the strategies available, and the possible states of nature (chance events) that might occur.
2. Draw the tree skeleton.
3. If probabilities are being expressed, enter the economic or statistical data and associated (subjective) probabilities.
4. Finally, analyze the tree to determine the best course of action.
For a rudimentary example, suppose you would prefer to hold a party on your patio, but there is a 40 percent chance of rain and the party cannot be moved once the decision has been made. You have only two strategies: outside and inside. The chance event is rain or no rain. The tree would look like this (no lines were included):
Now assess the subjective value of the ultimate alternatives: there are four, so on an ascending scale, outside-no rain-comfort would rate "4," while outside-rain-disaster is last and least.
You also have a quantified probability to add into the chance event: 60-40 against rain. When you have multiplied the subjective value by the probability of the alternative, the completed tree looks like this:
There is, then, a slight quantified edge (2.8 vs. 2.4) to holding the party outdoors. You, as decision maker, have been told something subjective by me as an analyst. By means of a simple graphic device, you not only know where I have been subjective, but what impact that subjectivity has had on the recommended outcome. In short, you have no misunderstanding about my reasoning and weighting processes.
There are few cut and dried means of assuring the inclusion of all alternatives, and the best advice seems to be to build a likely model and then study the results, seeking the impact of certain alternatives and the relationship among alternative courses of action. If it is possible to assign appropriate probabilities to the various branches, the result is both a decision-making tool and an effective vehicle for the communication of analysis.
Author's Note: This summary only includes the information from the article that is most applicable to our discussion of decision trees.
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CIA Historical Review Program
22 September 1993
Summary
The decision tree is a prototype for the preponderance of logic diagrams. It is a linear means of representing the alternatives, objectives, and consequences of a series of decisions. The decision tree, essentially, is an algorithm for the analysis of complex sequential decision problems.
Decision trees can be used to depict a series of true-false sequences (a deterministic way) or to display subjective likelihoods and their relationships (a probabilistic use). The technique is simple:
1. Identify the strategies available, and the possible states of nature (chance events) that might occur.
2. Draw the tree skeleton.
3. If probabilities are being expressed, enter the economic or statistical data and associated (subjective) probabilities.
4. Finally, analyze the tree to determine the best course of action.
For a rudimentary example, suppose you would prefer to hold a party on your patio, but there is a 40 percent chance of rain and the party cannot be moved once the decision has been made. You have only two strategies: outside and inside. The chance event is rain or no rain. The tree would look like this (no lines were included):
Now assess the subjective value of the ultimate alternatives: there are four, so on an ascending scale, outside-no rain-comfort would rate "4," while outside-rain-disaster is last and least.
You also have a quantified probability to add into the chance event: 60-40 against rain. When you have multiplied the subjective value by the probability of the alternative, the completed tree looks like this:
There is, then, a slight quantified edge (2.8 vs. 2.4) to holding the party outdoors. You, as decision maker, have been told something subjective by me as an analyst. By means of a simple graphic device, you not only know where I have been subjective, but what impact that subjectivity has had on the recommended outcome. In short, you have no misunderstanding about my reasoning and weighting processes.
There are few cut and dried means of assuring the inclusion of all alternatives, and the best advice seems to be to build a likely model and then study the results, seeking the impact of certain alternatives and the relationship among alternative courses of action. If it is possible to assign appropriate probabilities to the various branches, the result is both a decision-making tool and an effective vehicle for the communication of analysis.
Author's Note: This summary only includes the information from the article that is most applicable to our discussion of decision trees.
Wednesday, April 1, 2009
Decision Tree Analysis: Choosing Between Options By Projecting Likely Outcomes
MindTools.com
Summary
Decision Trees are useful for helping decision-makers to choose between several courses of action. Their structure allows decision-makers to explore their options and investigate the possible outcomes of those options. Additionally, Decision Trees help decision-makers to form a balanced picture of the risks and rewards associated with each possible course of action. To aid in the decision making process, Decision Trees provide a framework to quantify the values of outcomes with the probabilities of achieving them.
How To Make A Decision Tree:
1.) Draw a small square to represent a decision to be made on the left hand side of a large piece of paper, half way down the page.
2.) From this box, draw lines out towards the right for each possible solution, and write a short description of the solution along the line.
3.) At the end of each line, consider the potential/likely results. If the result taking that particular decision is uncertain, draw a small circle. If this results in another decision needing to be made, draw another square. Squares represent decisions and circles represent uncertain outcomes.
4.) Starting from the new decision square, draw lines out representing the potential options that could be selected. From the circles, draw lines representing possible outcomes.
5.) Repeat steps 2-4 until all possible decisions and outcomes are illustrated.
To begin the decision making process, start by assigning a score to each possible outcome. This score is an estimate of how beneficial the result is. Next, look at each circle and estimate the probability of each outcome.
Then, calculate the value of the uncertain outcomes (multiply the value of the outcomes by their probability). Start on the right hand side of the Decision Tree and work back towards the left. Only record the result to each respective square or circle from each set of calculations.
To evaluate each decision node, write down the cost of each option on each decision line. Then subtract the cost from the outcome value previously calculated. This value represents the benefit of that decision. Once all calculations are complete, choose the option that has the largest benefit (the final decision).
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Summary
Decision Trees are useful for helping decision-makers to choose between several courses of action. Their structure allows decision-makers to explore their options and investigate the possible outcomes of those options. Additionally, Decision Trees help decision-makers to form a balanced picture of the risks and rewards associated with each possible course of action. To aid in the decision making process, Decision Trees provide a framework to quantify the values of outcomes with the probabilities of achieving them.
How To Make A Decision Tree:
1.) Draw a small square to represent a decision to be made on the left hand side of a large piece of paper, half way down the page.
2.) From this box, draw lines out towards the right for each possible solution, and write a short description of the solution along the line.
3.) At the end of each line, consider the potential/likely results. If the result taking that particular decision is uncertain, draw a small circle. If this results in another decision needing to be made, draw another square. Squares represent decisions and circles represent uncertain outcomes.
4.) Starting from the new decision square, draw lines out representing the potential options that could be selected. From the circles, draw lines representing possible outcomes.
5.) Repeat steps 2-4 until all possible decisions and outcomes are illustrated.
To begin the decision making process, start by assigning a score to each possible outcome. This score is an estimate of how beneficial the result is. Next, look at each circle and estimate the probability of each outcome.
Then, calculate the value of the uncertain outcomes (multiply the value of the outcomes by their probability). Start on the right hand side of the Decision Tree and work back towards the left. Only record the result to each respective square or circle from each set of calculations.
To evaluate each decision node, write down the cost of each option on each decision line. Then subtract the cost from the outcome value previously calculated. This value represents the benefit of that decision. Once all calculations are complete, choose the option that has the largest benefit (the final decision).
Summary Findings: Argument Mapping (3 Stars Out Of 5)
Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the 12 articles read in advance of (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst College on 1 APR 2009 regarding Argument Mapping
specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.
Definition:
Strengths:
Weaknesses:
How-To:
Experience:
specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.
Definition:
Strengths:
Weaknesses:
How-To:
Experience:
.
Monday, March 30, 2009
Cultivating Expertise In Informal Reasoning
http://www.philosophy.unimelb.edu.au/reason/papers/CJEP_van_Gelder.pdf
Summary:
Argument mapping technology was introduced in a quasi-study of informal reasoning of first year university students in Australia. Operating under the premise that informal reasoning "appears at quite an early age and continues to develop through secondary and tertiary education...few people manage to become highly proficient." Informal reasoning was likened to a social tennis player: without dedicated practice and guidance, the athlete will never reach full potential. Thus, after initial training "experience in a domain is not related to the level of expertise."
Van Gelder argues informal reasoning skills are achieved to the extent that one engages in large amounts of deliberate practice...to go beyond ordinary competence. He postulated an increase in informal reasoning practice would have a positive effect on overall reasoning skills. To test his postulation, he utilized argument mapping software as a method to used in conventional logic instruction. The main benefit of using argument maps was to "enhance the quantity and quality of feedback" in reasoning activity.
The results of his study indicate students made substantial gains in informal reasoning skills. Participants gained approximately as much over one semester as they would have over 3-4 years of university study. Also, the amount of gain was positively to amount of practice.
The author does admit the results do not establish that deliberate practice is necessary for advanced expertise in informal reasoning.
Summary:
Argument mapping technology was introduced in a quasi-study of informal reasoning of first year university students in Australia. Operating under the premise that informal reasoning "appears at quite an early age and continues to develop through secondary and tertiary education...few people manage to become highly proficient." Informal reasoning was likened to a social tennis player: without dedicated practice and guidance, the athlete will never reach full potential. Thus, after initial training "experience in a domain is not related to the level of expertise."
Van Gelder argues informal reasoning skills are achieved to the extent that one engages in large amounts of deliberate practice...to go beyond ordinary competence. He postulated an increase in informal reasoning practice would have a positive effect on overall reasoning skills. To test his postulation, he utilized argument mapping software as a method to used in conventional logic instruction. The main benefit of using argument maps was to "enhance the quantity and quality of feedback" in reasoning activity.
The results of his study indicate students made substantial gains in informal reasoning skills. Participants gained approximately as much over one semester as they would have over 3-4 years of university study. Also, the amount of gain was positively to amount of practice.
The author does admit the results do not establish that deliberate practice is necessary for advanced expertise in informal reasoning.
Analyzing Framing Processes by Means of Logical Argument Mapping
http://smartech.gatech.edu/bitstream/1853/22232/1/wp36.pdf
Summary:
The article begins by discussing 'framing' as a concept. Each individual has a certain way of cognitively limiting the issue he/she is arguing for/against. Hoffman directly quotes Schon and Rein by stating "frames must be constructed by someone, and those who construct frames...do not do so from positions of unassailable frame-neutrality." In other words, Schon and Rein state arguers bring their own cognitive bias into the topic he/she argues for/against.
Hoffman's goal is to show that logical argument mapping (LAM) use as an analytical tool centers on "the analysis of framing processes as they are visible in texts, narratives, and communication." The analysts using this tool is required to impose logical consistency to the argument in question to prevent premature simplification, as well as understanding the arguer's implicit beliefs. This allows the analyst to think along the lines of "if p then q."
LAM is useful for detecting core beliefs when ad hoc hypothesis are presented. Hoffman states ad hoc hypothesis "main function is to keep systems of belief consistent without changing core assumptions." Such hypothesis are formulated when a piece of contradictory evidence is introduced which challenges core assumptions.
Hoffman then provides a process for conducting a LAM analysis. First the analyst must locate the central claim for a position. This represents the conclusion of an argument. Next, the analyst must extrapolate true and logical reasons which support the central claim. The analyst then must form a warrant, a statement which "justifies the step from a reason to a claim" so that "the conclusion must necessarily be true."
The warrant is the most important step in LAM for three reasons. First, it reveals the arguer's core implicit beliefs. Second, it is used to scrutinize the overall argument's soundness. Last, the warrant is used to justify or reject controversial arguments.
LAM's weaknesses are presented as well. First, the arguer must produce reasons for their claims, troublesome in verbal interactions. Also, the analyst must be able to interpret the argument, however this is more of a language usage issue.
Summary:
The article begins by discussing 'framing' as a concept. Each individual has a certain way of cognitively limiting the issue he/she is arguing for/against. Hoffman directly quotes Schon and Rein by stating "frames must be constructed by someone, and those who construct frames...do not do so from positions of unassailable frame-neutrality." In other words, Schon and Rein state arguers bring their own cognitive bias into the topic he/she argues for/against.
Hoffman's goal is to show that logical argument mapping (LAM) use as an analytical tool centers on "the analysis of framing processes as they are visible in texts, narratives, and communication." The analysts using this tool is required to impose logical consistency to the argument in question to prevent premature simplification, as well as understanding the arguer's implicit beliefs. This allows the analyst to think along the lines of "if p then q."
LAM is useful for detecting core beliefs when ad hoc hypothesis are presented. Hoffman states ad hoc hypothesis "main function is to keep systems of belief consistent without changing core assumptions." Such hypothesis are formulated when a piece of contradictory evidence is introduced which challenges core assumptions.
Hoffman then provides a process for conducting a LAM analysis. First the analyst must locate the central claim for a position. This represents the conclusion of an argument. Next, the analyst must extrapolate true and logical reasons which support the central claim. The analyst then must form a warrant, a statement which "justifies the step from a reason to a claim" so that "the conclusion must necessarily be true."
The warrant is the most important step in LAM for three reasons. First, it reveals the arguer's core implicit beliefs. Second, it is used to scrutinize the overall argument's soundness. Last, the warrant is used to justify or reject controversial arguments.
LAM's weaknesses are presented as well. First, the arguer must produce reasons for their claims, troublesome in verbal interactions. Also, the analyst must be able to interpret the argument, however this is more of a language usage issue.
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