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.

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."

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.

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:

1. 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.
   
2. Using lines drawn outward and to the right, identify each possible solution. Write each solution on each line.
3. 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
   
4. If another decision is possible, draw a square with that decision listed.
   
5. If there is a final consequence, a solid dot is drawn with a filled-in circle at its end.

Evaluating the decisions:

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.

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:

1. 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."
   
2. 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.
   
3. 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.
4. Sometimes branches emanating from decision and chance nodes can lead to other decision nodes - repeat steps 2 & 3 if this occurs.
5. 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.
   
6. The sum of the probabilities of all outcome branches stemming from a single chance node must equal 1.

Marsh does not clearly identify any cons to conducting a decision tree analysis, however, it is quite obvious that he is a strong proponent of using this technique to strengthen the decisions he makes.

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.

1. Trigger Event or Decision Statement (Nodes)
2. Decision Paths
   
3. Chance Points or Nodes
   
4. Leaf or Terminating Nodes

**For each node, the article recommends using a different shape or color to distinguish between decisions, chance points, and leaf 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 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.

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).

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:

Argument mapping (AM) is an analytic modifier that can be used to examine the logic behind the development of a particular conclusion and/or hypothesis. The product of AM is a visual representation (typically a box-and-line diagram) of the reasons that support and oppose the claim. Constructing a visual depiction of a complex argument reduces the level of abstraction in evaluating a decision.

Strengths:

Provides Audit Trail

Breaks down prose arguments and visualizes arguments for decision makers.

Shows strengths and weaknesses of argument

Improves critical thinking

Reduces cognitive bias and blindspots

Weaknesses:

Not estimative in nature.

The method does not explicitly seek out contradictory evidence.

The language specific to argument mapping is not uniform from scholar to scholar.

Dependent on the quality of presentation of original argument

The technique does not consider the impact of deliberately deceptive information

How-To:

* Must locate a central claim for a position (this is the conclusion of an argument)

--conclusion indicators include: therefore, thus, so, hence

* extrapolate true and logistical reasons and objections which support or refute the central claim.

--rewrite statements as individual sentences

--Reasons must answer the question: "How do we know that [insert claim] is true/warranted?"

* include all premises for reasons - leave nothing to be implied

--premise indicators include: since, because, for, given that

* With objections, list any possible refutations underneath the objection

* Form a 'warrant' - a statement that justifies the step from a reason to a claim

--warrants reveals an arguer's core implicit beliefs

--used to scrutinize the soundness of an argument

--used to accept or reject controversial arguments

*Use arrows to indicate which premises or co-premises support claims; or premises refute oppositions.

*indicate premises that need to be combined in order to support a conclusion, and premises that are each seperate reasons to believe a conclusion

*Use a color-coding scheme (if possible) to help visualize the arguments

--Supporting claims - green

--opposing claims - red

--nuetral claims - gray or no color

Experience:
We attempted to argument map a one page NY times editorial. The comments below were generated as a result of that exercise.

Argument mapping is difficult to synthesize from prose that is not written in a BLUF (Bottom Line Up Front) format.

The ability to effectively argument map is too dependent on quality of the articles given.

People will disagree on structure of argument and levels of relevance of data.

Argument mapping is not appropriate to use to develop an analytic estimate, rather it can be best implemented as a modifier to validate an already determined estimate.

Argument mapping would be an excellent modifier to test the validity of a conclusion/forecast. Would be an excellent supplement to ACH - suggest conducting an ACH to find a claim that is most likely, then test that claim for validity using Argument Mapping. This will help to reduce cognitive blindspots and biases, as well as test the reasoning behind the conclusion.

Extrapolating the main claim of the argument was easier once all of the premises supporting it were laid out.

The original format of the 'argument" really matters when it come to the ease or difficulty of applying argument mapping to the text.

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.

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.

Decision Mapping Easier Than Argument Mapping?

timvangelder.com

Summary:
The explores the differences between decision mapping and argument mapping, two very similar techniques with a fundamental difference. Whereas argument mapping is applied to an argument, decision mapping is used to determine "choices between multiple possible actions." As an example of each, the author draws both a decision map and an argument map based on a recent New York Times article by Col. Muammar Qaddafi proposing a peace plan for the Middle East.

After applying both techniques to Col. Qaddafi's proposition (which was the creation of "Isratine" - a joint Isreali/Palestinian state), the author found that the decision map was easier to develop than the argument map. Part of this was due to the nature of the piece being analyzed : an article in someone else's words. "The translation from prose to decision map was much more straightforward than the translation from prose to argument map. In the latter case, there seemed to be far more discretion about how to do it, and hence a much higher level of effort and expertise was required to determine which of the approaches would be 'right' or best. "

The author expresses his surprise that the decision map was easier to develop, although he concedes that the subject of the exercise may have had alot to do with that. If decision mapping is, in fact, generally easier than argument mapping, he states that the following would be true as well:

• Decision mapping should find faster and wider uptake than argument mapping
• From a pedagogical or training perspective, decision mapping should be introduced first, with argument mapping treated as a more advanced subject.

In a comment to the blog, a reader notes that he, too, finds decision mapping to be easier to use than argument mapping. He points out "For some reason argument mapping seems to be so much more dependent on language and context and is perhaps more prone to making mistakes." He explains that it is possible to mis-represent the author of an argument when constructing the argument map due to the tendency to want to "close the gaps automatically even if the original text doesn’t necessarily support it. " He further states that the first step of the decision mapping process is the most crucial, i.e. "sizing up the situation appropriately." The author responds, stating that that first step is critical, although he has no advice as to how to make sure you go about it correctly.

No Computer Program Required: Even Pencil-And-Paper Argument Mapping Improves Critical Thinking Skills

Harrell, Maralee. Teaching Philosophy, Dec2008, Vol. 31 Issue 4, p351-374, 24p

Summary:
The author explains that after she "stumbled across" Tim van Gelder's argument mapping software Reason!Able, she became intrigued as to how little she truly understood her own arguments she made while teaching philosophy at Carnegie Mellon University. According to the author, "I found, to my surprise, that I did not understand these arguments as well as I thought I had, and that the mapping was forcing me to analyze and synthesize in a way that I had never done before." The author explains how, especially in philosophy, arguments are not linear, and by applying argument mapping techniques it is possible to analyze different areas of the argument that one may not have seen previously and identify area that are not as well developed.

The author notes several rules to be used in the development of argument maps. First, look for premise and conclusion indicators. Examples include "since, because, for, given that; and some
common conclusion indicators are: therefore, thus, so, hence." Second, rewrite statements as individual sentences. Third, make sure to include all premises and conclusions; leave nothing to be implied. Lastly, "clearly indicate the difference between premises that need to be combined in order to support a conclusion, and premises that are each separate reasons to believe a conclusion."

The author, after incorporating argument mapping via paper-and-pencil diagrams, saw a marked improvement in her students critical thinking abilities and analysis skills. In order to further test the validity of the method, argument mapping techniques were taught in 5 out of 9 introductory philosophy courses at Carnegie Mellon, whereas those methods were not taught in the remaining 4. The goal was twofold: "The first is that all of our students, no matter how they are taught, are gaining argument analysis skills by taking our introductory course. This is important to know if we are then going to inquire which students gained more. This hypothesis implies that, on average, all students in introductory philosophy will exhibit significant improvement on argument analysis tasks over the course of the semester. The second hypothesis is that the ability to construct argument maps that accurately reflect the text they are analyzing is a considerable aid for improving students’ argument analysis skills (more of an aid that being able to represent an argument some other way). This second hypothesis implies that students who are able to construct argument diagrams and use them during argument analysis tasks should perform better on these tasks than students who do not have this ability." Although some students did use computer software, most used the paper-and-pencil method.

The first hypothesis was confirmed: all students in the study, whether they used argument mapping or not, increased to some degree their argument analysis abilities by virtue of being in the class. The second hypothesis was confirmed as well: students who used argument mapping techniques, did in fact, improve their argument analysis abilities to a greater degree then those that did not employ the technique.

The author concludes by stating that although many tools are available to conduct argument mapping, the method itself is effective no matter what tool is used. In fact, various tools were used during the study at the discretion of the student and instructor. "In our course, instructors and students are free to use any kind of medium they prefer to build argument maps. Some instructors use computer software, while others use the chalk board or overhead slides; similarly some students use one of a variety of drawing programs, while others use just pencils and paper. Our results show that the argument mapping skill, no matter how the maps are produced, is an important part of the gains in argument analysis abilities our students achieve. While on average all of the students in each of the lectures improved their abilities on these tasks over the course of the semester, the most dramatic improvements were made by the students who were able to construct argument maps." Thus, the visual representation of the argument itself was more important than the tool used to develop that image.

Teaching Critical Thinking: Some Lessons From Cognitive Science

Tim van Gelder College Teaching Vol. 53/No. 1

This article discusses six lessons learned from teaching critical thinking. The lessons are: Critical thinking is hard; practice in critical-thinking skills themselves enhances skills; the transfer of skills must be practiced; some theoretical knowledge is required; diagramming arguements (argument mapping) promotes skill; students are prone to belief preservation.

Lesson 1: Critical Thiking Is Hard
Van Gelder cites author Deanna Kuhn who in her the book The Skills of Argument concludes that most most people cannot demonstrate basic skills in making arguments and reasoning. Gelder goes on to explain how humans never evolved to be critical thinkers. Critical thinking is a complicated skill that is built out of simpler skills. Gelder conlcudes lesson one by comparing the difficulty of critical thinking with the difficulty of learning second language.

Lesson 2: Practice Makes Perfect
Because critical thinking is a skill, it is not enough to learn about theories and concepts. Students must take part in activities with the intention of improving their critical thinking skills, and these activities along with feedback must be continuous.

Lesson 3: Practice For Transfer
The problem of transfering a skill to multiple diciplines is difficult in all fields. Gelder believes that critical thinking skills are especially susceptable to the problems of transfer because of its generalist nature. The solution according to Gelder is to teach transfer of critical thinking from one subject to another as a skill in critical thinking.

Lesson 4: Practical Theory
Gelder opens this lesson by discussing how if most beer drinkers knew more about the elements of what is in beer and how it is made, they would have more appreciation for beer. Similiar to greater appreciation of beer, someone who knows more about the theory of critical thinking is more likely to appreciate it. Gelder beleives that students do not receive enough instruction about theory in critical thinking, however, he does consider it a mistake to think that a student can develop critical thinking skills exclusively through the study of theory.

Lesson 5: Map It Out
Arguments are presented in spoken or written words. Evidence supporting an argument can be broken down into hierarchical structures. It is these structures that can be diagrammed. The more complicated the argument, the more useful a visual representation can be. Gelder backs this assertion from studies comparing students of critical thinking who used argument maps and those who did not. Students using visual argument maps showed greater improvement in crtitical thinking skills.

Lesson 6: Belief Preservation
Gelder discusses how cognitive bias and "blindspots" either from evolution or societal influence, "...corrupt our thinking and contaminate our beliefs." He likens awareness of these features as being as important to critical thinkers as adjusting aim for windage is for archers. According to Gelder belief preservation is the most prominent form of bias. People will lower the status of evidence in their minds if it contradicts their beliefs. A good critical thinker must be aware of these bias.

Creation Of A National Institute For Analytic Methods

Steven Rieber and Neil Thomas

Article Summary
Rieber and Thomas begin the article with criticism of recent commissions from the Senate Select Committee on Intelligence and the Special Presidential Commission on Iraq Weapons of Mass Destruction. Both commissions recommended creating positions for "mission managers" or subject matter experts. Rieber goes on to explain and give examples of how expert opinions are often wrong despite years of experience and that the only way to know if "conventional wisdom" is correct, there must be in depth scientific study of the subject matter. Both commissions mentioned above advocate greater use of devil's advocacy in intelligence analysis however they do not cite any research validating the method. Rieber cites the book Groupthink by Irving Janis who argues that devil's advocacy can create a false sense of comfort that their decision making is sound just because they considered an opposite viewpoint and that their original policy choice has stood-up to scrutiny.

Rieber and Thomas argue that "The first element in improving the process of improving analysis is to find out what the existing scientific research says." From their research they identify argument mapping as one of the most promising method to "improve human judgment."

The way a method should prove itself is by being subjected to scientific studies that to control for outside influences, point out causation from correlation, and reveal significant facts. It also needs to determined to what extent the method improves analytical judgements and in which domains (political, economic, military,). The method needs to be teachable, and analysts must be willing to use the method.

The article continues by advocating for the creation of a National Institute for Analytical Methods (NIAM). The institute's function would be similar to that of the National Institute of Health (NIH). The NIH conducts its own research as well as funds research. With the right funding and staffing a NIAM could provide continued evidenced based insight about various methods of analysis that could show potential for the intelligence community.

Author's Note: At the time of this article's publication in 2005 Steven Rieber was a scholar at the Kent Center for Analytic Tradecraft. He co-wrote this article with Neil Thomas who is a lecturer in Philosophy of Science and History at the University of Melbourne. It is important to note that Tim van Gelder is Principal Fellow in the Philosophy Department at the University of Melbourne and is an instrumental force in the development of critical thinking and argument mapping teaching methods and software. His work is cited several times in this article.

During a class discussion Professor Wheaton asked if any of us had come across the name Steven Rieber in our research of articles relating to argument mapping. He then explained to the class that Steven Rieber currently works as an analytic methodologist at the Office of Analytic Integrity and Standards within the Office of the Director of National Intelligence (DNI). Rieber has instituted the teaching of argument mapping into the DNI analysts training curriculum. Because the above article was published in 2005 and that argument mapping was only method mentioned more than once, out the seven suggested, it is highly likley that argument mapping passed most of the criteon mentioned above for being a viable method for better intelligence analysis.

Argument Mapping - The Basics

Argument Mapping - The Basics
based on the heuristics and Rationale software developed by Austhink

*Author's Note: Although this guideline does not delve into the pros and cons of argument mapping, it does give a good idea of how to construct an argument map - whether you are using this particular software, or if you are making an argument map with pencil and paper.

Summary:

The "Argument Mapping - The Basics" sheets provide the reader with a outline of understanding for what argument mapping is, the terms used in argument mapping and logic, as well as some important rules of logic that you must keep in mind when structuring an argument map. Argument maps start with a conclusion, which is at the tip of the pyramidal hierarchy, with reasons and objections listed below the conclusion. Reasons can have co-premises, and co-premises can have other reasons to support the claim listed above. Co-premises can also work together to support particular reasoning. Objections are listed to oppose the conclusion or reason and can have rebuttals listed underneath the objections.

Similar to games of strategy (chess, risk, etc.), there appears to be a learning curve with argument mapping. It takes some time to get the 'feel' of the game and to fully understand the rules, but with time, the process become quick and effortless.

Important information within the document:

Definition of Argument Mapping: "Argument mapping is a way to visually show the logical structure of arguments. You break up an argument into its constituent claims, and use lines, boxes, colors and location to indicate the relationships between the various parts. The resulting map allows us to see exactly how each part of an argument is related to every other part."

Other important definitions to know when creating argument maps:

• Argument: a claim and reason(s) to believe that that claim is true.
• Simple argument: the building block of all arguments, consisting of one claim and one reason (with two or more co-premises).
  
• Complex argument: has several simple arguments linked together (the diagram below illustrates a complex argument)
  
• Conclusion: the main point an argument is trying to prove, usually a belief. Also called the position, the main claim, the issue at hand.
• Reason: evidence given to support the conclusion.
• Co-premise: the subset of a reason. Every reason has at least two co-premises, and each of these co-premises must be true for the reason to support the claim.
• Objection: a ‘reason’ that a claim is false; evidence against a claim
• Rebuttal: an objection to an objection.

Syntax of an Argument Map:


Of note:

• Arguments can have many claims, many reasons, many objections and rebuttals, but only one conclusion.
• Distinguish a claim with a single reason (made up of two co-premises) from a claim with two independent reasons.
• The exact structure of an argument is very important. For example, if side A has two good reasons to conclude something, and their opponent (side B) thinks one of those reasons is bad, then A’s conclusion may still be true/warranted if the remaining, unobjected-to reason is convincing.
• An argument map can represent a debate by showing exactly where two sides disagree on the issue.
  
• Argument maps show the structure of the argument/debate – every box is not necessarily true, but the first step is to understand the structure of the argument.

Rules within each box:

• Declarative Sentence: Each box should have a full sentence (not a phrase) and should be declaring something, taking a position (whether it is true or false).
• No Reasoning: No box should have reasoning going on inside it, only single claims. The reasoning is represented by the arrows and locations in the map. Look for words that indicate reasoning (e.g. because) and translate the reasoning into the map.
• Two Terms: Each box can only have two main terms, so that each box is either true or false, not both. If you have more than two terms in a single box, separate them into multiple boxes.

Rules within each simple argument:

• Assertibility Question: All reasons for claims must answer the question: “How do we know that [insert specific claim here] is true/warranted?” You are asking what evidence allows one to assert that the claim is true. Every claim box should have a reason box below it that answers this question.
• Holding Hands: Applied horizontally within each simple argument. Within each reason, a term stated in one co-premise must be mentioned in one of the other co-premises in that same reason (if it is not in the claim above it – see the Rabbit Rule below). The terms must ‘hold hands’ within a single reason if they are not already accounted for by the Rabbit Rule.
• Rabbit Rule: Applied vertically, between a claim and each of its reasons, and is combined with the Holding Hands rule. “You can’t pull a rabbit out of a hat.” Using these two rules for each simple argument, you make sure that every term mentioned in each box is found in one of the others.
  

Argument Mapping (Efebia)

Summary
Argument mapping is a logical sequencing method that employs box-and-line diagrams to "map" a course of possible decisions for a given "argument" and the ramifications of each option. The purpose of the map is to provide a visual depiction of the relationships between the overall contention and the logical (evidential) claims supporting or opposing it. Upon examination of the supporting and opposing claims, an argument map should serve as a useful tool for use in the decision making process, by reducing the complexity of a dilemma, conflict, or argument. Since argument mapping distinguishes itself from other box-and-line methods by utilizing different shapes, colors, and positioning, it appeals to the strong visual comprehension abilities of humans, allowing for improved processing and understanding, ultimately simplifying the complexity of an argument.

Basic Construction of an Argument Map
The components of an argument map are listed below with a brief explanation. Those pieces of the map are identified in the example below (click on the map to link to the source and to access a higher resolution map).

• The main claim: referred to in this article as the "position" or "contention" -is the hypothesis to be logically examined. The argument map's purpose is to help the decision maker accept or reject this hypothesis.
  
• Reason: a positive claim, or one that directly supports the main claim. Reasons not only support the main position, they may also support another reason.
• Objection: a negative claim, or one that opposes the overall main claim.
• Rebuttal: opposes an objection directly above it; or, "objection against an objection".

Strengths of Argument Mapping
According to the article, by using colors, shapes, etc. in argument mapping, the mind can better process the complexity and abstract nature of a difficult argument. Additionally, the logical structure of argument mapping is easier to present than traditional prose, which requires the reader to deconstruct the argument for himself, demanding a large investment in time and cognition.

The article also cites "extensive research" conducted by the University of Melbourne to gauge the effectiveness of argument mapping in the context of critical thinking. The study compared the achieved critical thinking abilities of students who utilized the Reason!able (Rationale) argument mapping tool versus those who used traditional prose. The study determined that, in a 12-week course, students who used argument mapping gained 12 IQ points.

Author's Comment:
Since this article may have commercial implications (as it names a particular brand of software), it is obviously a strong advocate for argument mapping, and does not approach the method with much objectivity. While there may be some obvious disadvantages to using argument mapping in certain situations, none are provided here. Moreover, the only other method this article discussed was traditional prose, which is not an analytic method by itself, but a medium of production or dissemination. Despite a mere mention of conceptual modeling and flow charts, there was no substantive, formal comparative discussion on them.

Argument Mapping and Analysis of Competing Hypotheses (AM vs ACH)

Summary

This article presents a comparative assessment of argument mapping (AM) and Analysis of Competing Hypotheses (ACH) as analytic diagramming techniques. While the paper focuses primarily on the differing techniques in diagramming a decision making pathway, the author considers both as "complementary analytical frameworks".

Comparing the Techniques

Both techniques involve analyzing a given proposition (hypotheses in ACH; conclusions in AM), evaluating the evidence (evidence in ACH; reasons and objections in AM), and diagramming a visual representation of the relationships (matrices in ACH; box-and-line diagrams in AM). In ACH, the letters C, I, and N represent positive (consistent), negative (inconsistent), or nuetral relationships, respectively. In AM, arrows indicate a relationship between the reasons supporting or opposing the conclusion; color indicates whether the evidence is a reason (green) or an objection (red) for the argument. When a piece of evidence is neither a reason nor an objection (neutral or irrelevant), the creator of the AM may choose either to eliminate it from the map or to make it a different color (typically gray).

Multiple Propositions
Due to the manner in which each of these methods handles multiple propositions, ACH gets the advantage over AM. ACH incorporates multiple propositions simply by adding another column in the matrix. However, adding any additional propositions to an AM may result in a cluttered diagram with crossed lines and complicated pathways. To resolve this issue, it may be more appropriate to duplicate pieces of evidence, or even create an additional map, making it less efficient.

Multi-tiered Evidence
The paper uses an example from Psychology of Intelligence Analysis to demonstrate a situation in which AM may be a more efficient method. The chosen proposition is "Iraq will not retaliate forUS bombing of its intelligence headquarters," and the piece of evidence in question is "Saddam [has made a] public statement of intent not to retaliate". Often, as cited in this example, an additional piece of evidence may be necessary to establish a piece of evidence. When using an ACH, the analyst may need to construct another matrix to validate evidence. The AM technique allows for the addition another level of evidence within the same map. The paper refers to the multi-tiered evidence as "granularity". Granularity allows evidence of differing levels of abstraction to be present on the same map; "the higher the level on map, the more general or abstract the reason or objection".

Assumptions
AM also allows the user to add assumptions or warrants alongside the pieces of evidence. Incorporating an assumption into an ACH would force the user either to add a justification with the conclusion in its cell in the matrix, or combine multiple pieces of evidence together.

Enhancing Deliberation Through Computer Supported Argument Mapping

Tim van Gelder
Department of Philosophy, University of Melbourne, Australia; and Austhink


Summary
Tim van Gelder defines deliberation as "a form of thinking in which we decide where we stand on some claim in light of the relevant arguments." Although this is a common and important process, it is complicated and often conducted poorly. Gelder contends that deliberation can be improved by mapping out arguments, especially when the methodology utilizes the new computer tools available. An argument map is a presentation of reasoning in which the evidential relationships among claims are made wholly explicit using graphical or other non-verbal techniques. Argument mapping is producing such maps.

This fairly minimal or broad definition recommended by Gelder allows for enormous variety in argument maps. The point of an argument map is to present complex reasoning in a clear and unambiguous way, and mappers should use whatever resources work best. Currently, argument maps are mostly comprised of "box and arrow" diagrams. With technology expanding, other presentations are likely to count as argument mapping. For example, somebody may develop a way to present arguments in virtual 3D or through a virtual reality environment.

According to Gelder, at least four main factors explain the superiority of argument maps. These points concern the limitations of prose which are partly or wholly overcome by argument maps. 1) In prose, the reader has to figure out what the relationships among the claims are. In an argument diagram, in contrast, all relationships are made completely explicit using simple visual conventions. In practice, this relieves a huge burden. Readers can devote their mental energy to thinking about the argument itself rather than trying to figure out what the argument is. 2) Prose is a monochrome stream of words, sentences, and paragraphs. Prose does not use any color, shape, line, or position in space to convey information about the structure of the argument. We know, however, that our brains can process huge amounts of color, shape, and space information very quickly. In an argument map, color can be used to indicate in a matter of milliseconds whether a claim is being presented as reason or an objection. 3) Prose is sequential in nature. However, arguments are fundamentally not sequential. Arguments are more than just one thing after another; they are more complicated. 4) Using diagrams, we can to some extent take advantage of the way humans learn and understand. "We can place all the reasons over here and all the objections over there, or we can make stronger reasons bigger, or place them underneath (supporting) the conclusion."

Until now, argument maps have not really taken off as a practical tool for argument deliberation. Creating these diagrams by hand can be quite difficult. However, new computer software (both free and commercial) is making this method easier. New argument mapping pieces of software include Araucaria, Athena, and Reason!Able.

Argument Maps Improve Critical Thinking

Charles R. Twardy
Revised draft for publication in Teaching Philosophy
School of Computer Science and Software Engineering
Monash University, Australia

Summary
When Charles R. Twardy, a professor at Monash University, first heard about Tim van Gelder's Reason!Able argument mapping software, Twardy was quite skeptical about the effectiveness of the methodology. Rumors circulated that the new software had the ability to drastically increase the quality of critical thinking by van Gelder's students. Twardy contacted van Gelder and the two professors agreed that Twardy should visit van Gelder's university and teach one of his classes to see if students' critical thinking skills really do improve with the Reason!Able software (argument mapping) or whether the students benefit from the "founder effect."

The Reason!Able software for argument mapping amazed Twardy. He saw a significant improvement in the abilities of his students to think critically about arguments after taking a course on the Reason!Able software and the argument mapping methodology. Twardy concluded that "Computer-based argument mapping greatly enhances student critical thinking, more than tripling absolute gains made by other methods." (The gains, or scores, Twardy is referring to are those from the California Critical Thinking Skills Test)

The most significant advantage that argument mapping provides students is the ability to show precisely how students make errors in their reasoning, making it much easier for them to fix their errors. Specifically, argument maps help us to understand how arguments are structured. Typically, we do not make the distinction between two claims forming part of a single reason or whether they are parts of separate reasons. Prose does not force students to know the structure of arguments. Even if you understand an argument, you may not understand the argument's structure.

The second benefit to argument mapping is the methodology's versatility. Argument mapping is a general skill that can be applied to all kinds of arguments.

The major negative to Reason!Able and argument mapping is that users really need a class and significant practice to master the skill. Almost anyone who is asked to map a two-paragraph argument fails to do so correctly. Twardy argues that "practice is clearly important; argument mapping without practice would not much improve critical thinking."

Enhancing our Grasp of Complex Arguments

Enhancing our Grasp of Complex Arguments
By Paul Monk and Tim van Gelder
This paper was presented by Paul Monk as a plenary address to the 2004 Fenner Conference on the Environment, Australian Academy of Science, Canberra, 24 May 2004

Summary:

What argument mapping is used for:

• To structure, communicate, and correct arguments of any degree of complexity
• To govern deliberation, keeping it on task, target use of evidence, specify disagreements, and make the process more efficient
  

Verbiage tends to make people miss what is being said and asked and encourages people to grasp tightly to their own thoughts. Monk and van Gelder posit that the use of only language, writing processes, and mental cues are too primitive to completely understand the complex arguments that people are now faced with. They continue by stating, “We conduct complex arguments as if a combination of holistic apprehension, intuitive judgment and natural language were sufficient for handling them [arguments]. None of us, I think, would consciously make that claim. We do what we do by tradition and by default, not because we have thought through why we do it, how it works and whether it serves us well.”

Playing the game of tic-tac-toe (on a 4x4 grid or larger) without using actual gridlines is used to illustrate the point that our working memory struggles without the presence of a visual aid (the grid). Cognitive blind spots and biases, the methods used to record and communicate arguments, and separation of disciplines due to different idiolects all accentuate the problem of our limited working memory.

Just as maps and charts allow us to navigate land and sea with more ease than an oral explanation, a map can help us visualize and navigate through problems and arguments. To map an argument, you must start with a proposition, or chief contention – this contention is entered into a white box and placed at the top of an argument map. Supporting claims are color-coded green, while objections are coded red. Claims are organized in a pyramidal hierarchy to maximize the appearance of evidential and logical relationships. The first set of claims (top level) begs the question “what are the distinct arguments provided for the main point (the chief contention)?” Subsequent levels are asked, “Do they support all of these primary arguments with further evidence? [and] Do they countenance any objections to their argument and rebut them?

The authors use the article Coalition of the Willing? Make That War Criminals, which discusses whether or not a preemptive strike on Iraq would constitute a crime against humanity, to demonstrate how argument mapping is useful. (See Image Below)


Advantages of argument mapping over prose:

1. It makes explicit logical relationships that the linearity and abstractness of prose cannot help but obscure.
2. The map offers an instant and effortless scan-ability of the overall structure of the argument, which you simply cannot derive from prose.
3. There is an ease of movement from the detail to the overview that is far more difficult in the case of prose.
   
4. There are unambiguous visual clues as to the significance that particular details have, due to the hierarchical ordering of the structure, the color-coding of the individual boxes and the inferential relations between boxes.
5. A map offers a visual clarity as to the limits of a debate, whereas prose obscures these limits or labors to spell them out.
6. The cognitive burden imposed on us by the task of analyzing a piece of prose is drastically reduced in the case of a map, for the same reasons that it is reduced in moving from a prose description of London to a map.
7. For any given proposition, all claims are integrated into a single structure, instead of consisting of various component parts, which then have to be assembled by whoever happens to be trying to comprehend the argument in question.
   

*Author’s Note: Tim van Gelder has done extensive research in the field of argument mapping and is the leading mind behind Reason!Able, a computer program designed for argument mapping. Reason!Able has now evolved into Rationale. See Video Below.

Summary Findings: Dialectic And The Socratic Method (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 25 MAR 2009 regarding Dialectics generally and the Socratic Method specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Definition:

The Dialectic Method is an analytic technique designed to force the participants to re-examine their internal beliefs, biases, and conclusions through an open and directed dialogue.

The Dialectic Method uses questioning techniques with the intention of creating a better understanding of a problem or concept. In the realm of intelligence analysis, it should be used as an analytic modifier; i.e. a technique to reassess the validity of the analytic process, not as a forecasting method.

Strengths:

--The primary strength is the ability to identify and challenge initial assumptions about a target, and in effect, it reduces prejudice and bias.

--The dialectic method is also useful throughout the intelligence cycle from requirements, estimative conclusions, and feedback.

--Using dialectic demands the analyst think critically about the certainty of the analysis generated.

Weaknesses:

-- It does not provide an analytical forecast by itself.

-- The questioner needs to be highly skilled in managing the process.

-- As a cautionary note, thinkers caught in their own illogical concepts may become irritated or even angered by such an approach.

-- The approach can be time consuming, and should not be used under time constraints.

How-To:

-- The first step is to provide an initial, well-formulated question with group-wide understanding of the hypothesis at-hand.

-- After the initial hypothesis is presented, the group undetakes an opposing line of questioning to disect the hypothesis and its sub-components.

-- Use the discussion to synthesize arguements for and against the initial hypothesis to determine its truth and validity.

Experience:

We applied the Socratic Method specifically and the principles of dialectics generally to a variety of realistic intelligence situations. We explored how a formal questioning approach that assumes an antithesis, for example, could perhaps have impacted the estimate regarding the presence of WMD's in pre-war Iraq. Even if a Socratic approach to questioning the conclusions of that estimate would not have changed the overall finding, the group generally agreed that it would probably have altered the final level of confidence.

We also applied the method to the requirement phase where the decisionmaker who wants "everything" could be seen as establishing a thesis while the intelligence professional who knows that the decisionmaker doesn't need everything essentially establishes an antithesis. The ultimate intelligence requirement could then be seen as the synthesis of the two positions.

Socratic Method

Learn UNC, From The UNC School Of Education

Summary
Developed from Plato’s Socratic Dialogues, the Socratic approach challenges learners to develop their own critical thinking skills and engage in analytic discussion. “Socratic questioning is a systematic process for examining the ideas, questions, and answers that form the basis of human belief. It involves recognizing that all new understanding is linked to prior understanding.”

A group leader (or questioner) engages participants by asking open-ended questions that require generative answers. Ideally, the answers to the questions serve as a beginning for further analysis and research. The questioning process requires participants to consider how they rationalize about a particular topic.

The goal and benefit of the Socratic Method is to aid participants in processing information and engage in a deeper understanding of a particular topic. Most importantly, rather than engaging in a competitive debate, the Socratic Method allows participants to dialogue and discuss the topic in a collaborative and open-minded manner.

Unfortunately, the success of the Socratic methodology often depends on the quality of the initial question that initiates the investigative discussion. As a result, the first question posed by the questioner to the participants must:
*arise from the curiosity of the leader
*not have a single "right" answer
*be structured to generate dialogue that leads to a clearer understanding of the topic
*require participants to refer to concrete data or textual resources

The Socratic Method

Communities Resolving Our Problems, Western Carolina University

Summary
The Socratic Method is a chain of questions that seek the truth of some topic. Although the methodology may include summarizing ideas, in its purest form, the Socratic Method only includes questions. The questions allow users to utilize their critical thinking skills to find false paths and dead ends in the reasoning process. As a result, the Socratic methodology is a problem solving methodology.

To help develop the proper questions for a Socratic analysis, the discussion group should consider playing the game 20 Questions. This game allows players to see the value of some underlying analytical strategy.

The Socratic Method does not have a concrete methodology for generating the chain of questions. One person in the discussion group should serve as the lead questioner, engaged in analysis and in breaking things down into logical parts. Typically, the initial question must get at what the group already knows about the topic at hand. After this phase, there is the option of pausing to summarize the conclusions found once the group reaches a certain level of complexity. The lead questioner should formulate questions that will move the group into the next area of the topic that the group needs to know. Once the group becomes familiar with the process, all members can be free to pose questions and direct the process’s path.

Law schools often utilize this process to reveal contradictions to invalidate initial assumptions (a handy skill in legal cases). As a cautionary note, thinkers caught in their own illogical concepts may become irritated or even angered by such an approach. As a result, it is very important to develop an egalitarian attitude among all members of the group so that everyone feels comfortable with this process.

Dialectic and Method in Aristotle

Aristotle. Image via Wikipedia

http://aristotle.tamu.edu/~rasmith/dialectic-and-method.pdf

Summary:

Aristotle's overall method is described as a philosophical inquiry beginning with appearances and undertaking to resolve apparent puzzles. Dialectic arguments originate from endoxa, or commonly held opinions. The enoxa serve as the first series of premises of scientific demonstrations. Arguements are classified according to their premises: some are in accordance with a particular art (field of study), i.e. rest on premises peculiar to that art, whereas others are general. It is the general arguements which are available for dialectic inquiry; the focus is on deriving the general truth as opposed to circumstancial truth.

Aristotle came to see the inadequacy of of the appeal to intuition for the justification of arguements and sought its replacement through dialectical proofs. To establish truth, a method allowing participants to syllogize from common beliefs needed establishment. This is known as the dialectic.

Since Aristotle believed everyone has a built-in grasp of the truth, the opinions of the many, as well as the wise are acceptable, although each needed clarification and correction. The dialectician is to collect the views from each type of person and use them to gauge the acceptability of premises to a particular opponent. However, not everyone's opinion is treated with equal weight.

When a general agreement is initially reached when attempting dialectic dialogue, it is acceptable in some instances to perpetuate false premises for examination. The false premise than can be argued for and against to establish truth in dialectic dialogue. The key property of dialectic is to examine completely the opinions presented on the topic from the truths contained therein.