Bayesian Analysis of Intelligence or Improved Advice to Decision-Makers

Summary:

Decisions associated with protecting critical infrastructures are facilitated through collecting intelligence leading to the formulate courses of action to protect them from adversaries. Sometimes there are time restraints that prevent analysts from evaluating the full scope of a situation and evaluating every piece of data available. During a crisis, analysts must make assessments based on new pieces of information. Bayesian analysis allows decision makers to assess the credibility of potential threats. Researchers have often used Bayesian analysis along side other techniques such as probabilistic risk analysis and game theory to model threat scenarios in hopes of formulating effective responses by identifying vulnerabilities and risks.

According to the authors,’ Bayesian analysis is not very popular in the intelligence community to identify indicators and warnings. The following are the reasons why Bayesian analysis has not been be adopted by the intelligence community.

1.      Bayesian inferences in intelligence have not been defined because analysts are uncomfortable applying probabilistic distributions to events.

2.      It is assumed that analysts often pre-process raw intelligence to produce intelligence reports.

3.      A large number of Bayesian tools evaluate only one hypothesis and cannot be applied to situations where adversaries have more than one strategic interest.

4.      Current Bayesian models cannot handle the short time horizon during a crisis.

5.      There is a lack of need for a clear confidence threshold for decision makers.

6.      The way of updating the prior beliefs about a specific scenario utilizing new pieces of evidence is considered insignificant.   

With consideration of these reasons, the authors proposed a ways of improving the effectiveness of decision made during a crisis. First, they plan on incorporating the moving time horizon to the new model. Secondly, they plan on creating a model that is not hindered by above ideologies common to the intelligence community. The new model would include the following characteristics:

1.      Generalize the Bayesian approach of analyzing intelligence.

2.      Include signals intercepted knowingly and ones gathered through clandestine means.

3.      Recognize denial and deception.

4.      Evaluate the scenario using temporal elements.

5.      Play games that would allow misinterpretation of the data leading to signals directed at a third party.

6.      Identity and apply a threshold for decision making.

7.      Define prior beliefs based on available military and intelligence resources.

8.      Develop conditional probabilities of the existence or the absence of a threat based on new evidence.   

Critique:

Elisabeth Pat-Cornell and David Blum’s article provides a good introduction to the Bayesian analysis with regards to intelligence analysis. Although the authors proposed a new model to make Bayesian analysis more relevant to intelligence analysis, they did not sufficiently test their model by conduct an experiment. Therefore, no evidence is present to assess the effectiveness of their model. In addition, they failed to provide a good definition of the theory as well as the advantages and disadvantages of adopting Bayesian analysis for analyzing intelligence. Bayesian analysis allows for the use of prior knowledge to alongside or to update current knowledge of a scenario. However, Bayesian analysis is sometimes restricted to small sample sizes. Also, there is not valid method of choosing the priors. Each member of a team working to resolve a specific problem may come to the different conclusions depending on the prior they chose.      

 
Source:
Cornell, M., & Blum, D. (n.d.). Bayesian analysis of intelligence or improved advice to decision-makers. Retrieved from http://create.usc.edu/2010/06/bayesian_analysis_of_intellige.html

Bayesian Analysis for Intelligence: Some Focus on the Middle East

In a report written for the CIA and declassified in 1994, Nicholas Schweitzer discusses the use of Bayesian analysis and how it can benefit traditional analysis.

Schweitzer begins his report by discussing how much information is generated on a yearly basis and how it is the analysts job to be the funnel for this information. Along with the ever increasing amount of information, the analyst also must deal with very complex situations that they must analyze, usually with models that fail to account for a large amount of complexities. The author believes that by using Bayesian analysis, some of the complexities of situation can be reduced.

Schweitzer discusses the use of Bayesian inference in the analysis of IMINT. Schweitzer gives the example of an analyst attempting to determine whether a a military unit is a motorized rifle battalion or an infantry regiment. In his example, the analyst finds that there are 10 tanks stationed with this unknown military unit. Using either expert opinion or historical observation, the analysts assigns a probability that the there is a 90% chance that the unit is a motorized rifle battalion and a 10% chance that it is an infantry regiment.

In discussing more complex applications, Schweitzer warns the reader that there is rarely objective probabilities of events and that historical observation is not very useful. However, if the analyst can overcome the difficulties of assigning subjective probabilities to events, than Bayesian analysis will allow him to "squeeze a little more information from the data we do receive". With this in mind though, he warns that analysts tend to attribute more precision to a number than they should.

The complex example that Schweitzer creates is four hypothetical scenarios, each involving the potential for war between Israel and Egypt and Syria. The scenarios are: No hostilities are planned by either country for 30 days, Syria, either alone or with other Arab nations, plans to attack Israel within 30 days, Israel is planning an attack with an Arab nation within 30 days, and the last one is that Egypt will disavow the disengagement treaty in the next 30 days. Among the analysts used, the initial probability that they assigned to continued peace was between 70% and 95%. After this, the analysts started to change their estimates based on new evidence that was presented.

The author concludes by discussing the applicability of Bayesian analysis and the types of questions it can be applied to. The questions must have mutually exclusive categories (war, no war), the question has to be expressed as specific hypothetical outcomes, there needs to be a rich flow of data that is related to the situation, and the question must resolve around an activity that produces signs and is not largely a chance or random event.

Critique

The biggest criticism that I see in this report is the lack of an assessment in the Middle East example. The author sets up the four scenarios and gives a preliminary probability of continued peace. He then begins introducing evidence into the question and assigns probabilities to the pieces of evidence and how they apply to a certain scenario. However, he stops there. He does not take the next step to show what the new probability is is each scenario playing out while taking into account the various pieces of evidence. he has an appendix of the formulas used, but he does not show the answer.

The author does a very good job of showing how difficult it can be to set up a question where Bayesian analysis can be applied. This includes is warning about subjectively assigned numbers and how analysts tend to put more belief in them than they should.

Overall, the paper was a good set up to Bayesian analysis and intelligence, it just felt like an important part was missing.

Source:

Schweitzer, N. Bayesian Analysis for Intelligence: Some Focus on the Middle East. Retrieved from: https://www.cia.gov/library/center-for-the-study-of-intelligence/kent-csi/vol20no2/html/v20i2a03p_0001.htm

Bayesian Analysis of Longitudinal Data Using Growth Curve Models

Summary:

Bayesian Analysis of Longitudinal Data Using Growth Curve Models bases Bayesian analysis on the principle that the application of probability depends on the degree to which a person believes a hypothesis or a proposition.  The article provides a summary of the overall basics of Bayesian terms and methods beginning by reviewing terms such as priors, posteriors, and the Markov chain Monte Carlo (MCMC) method.  Following the introduction, the authors explain the basic concepts of the latent basis growth model.  Lastly, they incorporate an empirical example fitting a latent basis growth curve model to achievement data from the National Longitudinal Survey of Youth.  The example demonstrates how to analyze data using noninformative and informative priors.  The findings show that Bayesian methods are an alternative to the maximum likelihood estimation (MLE) method.  Further findings suggest Bayesian methods have other strengths including systematic incorporation of prior information from previous studies.  The authors found that the Bayesian method was a more plausible way to analyze small sample data as compared to the MLE method. 

Bayesian methods are applicable to intemresopnse models, factor analytic models, structural equation modes, genetic models, and multilevel models.  Both applied and theoretical measurement can benefit from the opportunities that Bayesian methods can bring forth.  The authors point out that oftentimes the strenuous programming and computational demands of Bayesian methods as well as the complexities of the models that usually need the application of Bayesian methods make the methods seem fairly remote and frustrating for empirical researchers.  Thorough this study and the examples provided the authors attempt to provide an easy way to implement Bayesian analysis.   

  

Critique:

This article provides a summary of the basis for Bayesian methods.  While it is not related to intelligence analysis it does provide a breakdown of the technique, especially useful to those with no experience using Bayesian analysis.  Additionally, the systematic example provided using data from the National Longitudinal Survey of Youth provides a fairly easy to follow, step-by-step example of the method.

An issue that not only arose with this article but all articles on Bayesian analysis is that although they talk about its importance in terms of analysis there are very few that apply the method directly to intelligence analysis.  Statements describing Bayesian analysis such as, Bayes’ theorem is useful because it provides a way to calculate the probability of a hypothesis based on the evidence or data, is very relevant to intelligence analysis.  Additionally, by discussing the probability of the data also calling it the likelihood, would allow analysists to use the correct WEP when making statements of estimated probability.  Lastly, the ability of Bayesian to estimate complex models in data analysis is extremely effective.  All of these were findings or statements found within the article, and all are very applicable and beneficial to the intelligence community.  The application of these findings and statements to a problem faced by an intelligence analyst would demonstrate the usefulness of Bayesian analysis in these scenarios. 

Finally, the article mentions that an alternative to meta-analysis are Bayesian methods that use informative priors.  The authors provides a short explanation of Bayesian’s ability to do this but, considering the reliability and weight placed on meta-analysis studies, more information should be given to back up this claim.  A claim like this is deserving of an entire study rather than a short paragraph and leaves the reader wondering exactly how the Bayesian method can act as an alternative to a meta-analysis.  A more comprehensive explanation is necessary.

Source: 

Grimm, K.J., Hamagami, F., Nesselroade, J.R., Wang, L., & Zhang, Z. (2007). Bayesian analysis of longitudinal data using growth curve models.  International Journal of Behavioral Development. 31 (4), 374-383.

A bayesian analysis of human decision-making on bandit problems

A bayesian analysis of human decision-making on bandit problems


Summary: 

Steyvers, Lee, and Wagenmakers (2009) conducted their study on the differences in individuals balancing between exploration and exploitation in solving bandit problems by using Bayesian analysis.  In a bandit problem situation, the individual will have to choose between a set of alternatives that have inherently different reward levels.  Moreover, the individual will have to try and maximize the total reward that they receive over a set number of trials ( Steyvers et al., 2009).  Bandit problems require that the individual analyzes their environment in two distinctive manners, both explorative and exploitative.  It is crucial that the individual exploits situations in their environment that they are familiar with and explore areas of their environment that they are less familiarized with (Steyvers et al, 2009).  Thus, conducting a happy medium between both exploitation and exploration in bandit problems is critical to effective decision-making thought processes.

Steyvers et al. (2009) utilized a Bayesian extension of optimal decision-making processes to display differences in human decision-making when the reward rates are different within the individuals situated environment.  The ultimate goal is to determine which situations an individual would be more willing to make optimistic assumptions about reward rates, as opposed to pessimistic assumptions about the potential reward rate.   The sample size for the study included 451 participants who completed a series of bandit problems, as well as a series of psychological tests.  The psychological tests measured some aspects of psychometric assessments of cognitive, intelligence, and personality traits of the 451 participants (Steyvers et al., 2009).

Over the course of the study it was determined that by completing a larger amount of problems the participants were able to learn more effective decision-making processes.  Hence, becoming more familiar with a certain environment improved decision-making abilities.  Completing more tests allowed the individuals to have more efficient decision-making processes about what their assumptions should be to maximize rewards and minimize losses in different situations/environments.  Thus, environments with high reward rates displayed participants as being more likely to conduct exploration as opposed to limited reward environments in which participants chose to be more exploitative in decision-making endeavors (Steyvers et al, 2009).  Moreover, Steyvers et al. (2009) found that standard psychometric measurements of intelligence had a direct correlation with choosing to be explorative or exploitative in ones decision-making thought processes.

Critique: 

Overall, with not being strongly familiar with the concept of Bayesian analysis the article was a little hard to follow at times, however, the authors did describe the various explanations of the decision-making variables within the study that were part of the various Bayesian analysis equations.  A more thorough explanation would certainly benefit the reader to follow the way in which the experiment and calculations were conducted more easily.  Most significantly, I found the study interesting in the way it utilized Bayesian analysis to examine bandit problems.  Bayesian analysis' ability to update the probability of an event to occur when more evidence is added allows for a good analysis of bandit problems.  Bandit problems challenge the decision-maker to explore unfamiliar ground or exploit situations they have more direct experience with.

I agree with the authors that they would need to expand their study in order to determine if more individuals conduct decision-making process in bandit situations in the same manner that was displayed in this study.  Thus, it would be necessary to choose more factors that would affect the cognitive thinking capabilities of the respondents.  One such factor that would be needed to consider as a variable in this study would be the factor of learning.  The authors found that continued testing allowed the participants to choose the correct decision-making possibility, either exploitative or exploratory.  I think that it would be interesting to find out at what point over a certain amount of tests would the respondents have a sense that they were making the right decision, or is this type of decision-making inherently present in our cognitive abilities without testing.

Source:

Steyvers, M., Lee, M.D., & Wagenmakers, E.J. (2009). A bayesian analysis of human decision-making on bandit problems. Journal of Mathematical Psychology, 53 (3), 168-179. Retrieved from http://www.sciencedirect.com/science/article/pii/S0022249608001090.

Summary of Findings (White Team): Game Theory ( 3.5 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the 8 articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in March 2013 regarding Game Theory specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.


Description:
Mesquita (2011) defines game theory as a "a body of reasoning, grounded in mathematics but readily understood intuitively as a reflection of how people may behave, particularly in situations that involve high stakes for them. It is part of a family of theories that assume people are rational, meaning that they do what they believe (perhaps mistakenly) is in their best interest." Game theory is a methodology that allows a player to make a decision based on predictions of what the other player will do and weigh their potential options in relation to the options of the other players. Each payoff will be different depending on the combination of strategies.


Strengths:
1. Looks at multiple scenarios between the actions of two parties
2. Applicable to many fields
3. Denial and deception actions by the adversary can be taken into account within game theory models
4. Not entirely accurate or helpful if only performed once
5. Is effective in emphasizing the importance of critical thinking in making a decision and understanding the potential consequences of particular decisions


Weaknesses:
1. Have to be able to play a role
2. Level of accuracy can be low
3. Difficult to simulate unless it is a real-life situation
4. Assumes that the players are rational actors
5. Some scenarios need to be played multiple times to witness a discernible pattern of behavior between the parties.
6. Rational behavior within the game model is different than rational behavior with human interactions. Human interactions should be taken into account with the game model.
7. An iterated game will change the result of the outcome


Step by Step Action:
1. Construct a matrix
2. Determine possible decisions the players are likely to make
3. Determine how the players maximize the benefits based on those decisions
4. Conduct the exercise to see how the possible outcomes play out
5. Repeat exercise to get better results


Exercise:
Begin with 20 gold coins considered your “loot”. Each person playing the game is assigned a number. The person with the highest number holds the most power. The person holding the most power begins the game by making a proposal as to how to assign the coins, keeping in mind the person with the most power wants maximize the amount of coins they have while appeasing a majority of the players. A vote is taken, a majority is necessary for the person with the highest number to keep power. If a majority is not achieved power moves down to the next person and the process starts again.

Some interesting dynamics resulted when we calculated the exercise as a class. Rationality for the first round demonstrated that the rationality to distribute the coins evenly among the participants was not the chosen course of action by the first offering which was voided. Playing the game multiple times demonstrated the need for the first two individuals to give offerings that were fair to everyone, and most significantly allow the players with the most power to gain coins ( positive sum games) instead of zero sum games in which the other players benefitted from the higher ranked individuals incorrect rationality.


Source:

Mesquita, B (2011) Applications of Game Theory in Support of Intelligence Analysis. Intelligence Analysis: Behavioral and Social Scientific Foundations, 57-82. Retrieved from http://www.nap.edu/openbook.php?record_id=13062&page=57

Summary of Findings (Green Team): Game Theory (2.875 out of 5 Stars)

Game Theory

Green Team

Rating (2.875 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the 8 articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in April 2013 regarding Game Theory specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Description:

Bruce Bueno de Mesquita defines game theory as “ a body of reasoning, grounded in mathematics but readily understood intuitively as a reflection of how people may behave, particularly in situations that involve high stakes for them. It is part of a family of theories that assume people are rational, meaning that they do what they believe (perhaps mistakenly) is in their best interest.” The method attempts to identify potential actions and reactions from players using a variety of factors, the most important of which is the belief that players will act in their best interest. Additionally, this theory maintains the understanding that individuals will make rational decisions and that rationality will not necessarily end with the most optimal solution for both (or more) parties.

Strengths:

• Can use and evaluate a range of unstructured data
• Can be used in many fields including economics, international relations, and intelligence
• Some models of game theory are simple, though models increase in sophistication and difficulty
  
  

Weaknesses:

• Relies heavily on individuals acting in their best interests - does not account for altruistic behavior
• Single play games are not very useful and in reality, games/scenarios cannot or will not always be repeated
• Complex applications of game theory require substantial knowledge of math and statistics
• Context heavy, relying on isolated, hypothetical scenarios
• Operates under a number of assumptions, including actors’ motivations and psyches
  
  

How-To:

1. Find a problem that can be modeled through game theory (nothing overly complex with too many factors).  Make sure the simplified essence of the problem is used.  
2. List all actors and options.  A table is the most common representation technique -- since it visually portrays a payout matrix
3. Assign values to each of the potential outcomes.
4. Weigh potential outcomes according to these values.
5. Describe situations in which these outcomes are likely to occur and then choose the most likely, regardless if it is the ‘best’ outcome or not.
   
   

Personal Application of Technique:

The class was tasked with finding the optimal outcome for themselves in a pirate puzzle application of game theory. In this application of the pirate puzzle, five pirates have an understanding of rank that is respected on the ship. The pirates must decide how to divvy up a bounty of twenty gold (chocolate) coins. Players operated under the assumption that they wanted the twenty gold coins.  The highest ranking pirate makes the initial proposal to divide the coins, knowing he must gain majority’s approval in order for the proposal to pass. In our example, the pirate who fails to gain the majority vote is out of the running for the gold coins and the next highest ranking pirate makes his own proposal until one proposal is agreed upon. In the event of a tie, the highest ranking pirate involved in the proposal casts the deciding vote. The game was run twice in order to increase the presentation of different options.

In both instances, the highest ranking pirate in the class failed to gain the majority vote and was eliminated from the running. Instead, in the first instance, the game was played out to the third ranking pirate. The next iteration ended with the second highest ranked pirate, who was able to appeal to the lowest ranked pirate to earn a fifty-percent vote. Despite what game theory suggests, the lowest ranked pirate in our scenario was not satisfied with an offer of a single coin, or a few coins, even knowing she could be left with none if she let game continue. During the second round of the game, the individuals acted more to their interests rather than attempting to appease everyone.

Rating:  2.875 out of 5 stars

For Further Information:

Stewart, I. (1999). Mathematical recreations: A puzzle for pirates. Scientific American, 98-99. Retrieved from http://www.cse.iitb.ac.in/~saifhhasan/files/pirate_puzzle.pdf

Source: Mesquita, B (2011) Applications of Game Theory in Support of Intelligence Analysis. Intelligence Analysis: Behavioral and Social Scientific Foundations, 57-82. Retrieved from http://www.nap.edu/openbook.php?record_id=13062&page=57

New Economics of Sociological Criminology

Summary:
The article New Economics of Sociological Criminology, written by Bill McCarthy, looks at the intersection of rational choice approach theory and game theory as applied to predictions of criminal behavior. The study not only looked at rational choice approach theory in regards to the acts committed by criminals, but the author looked at how the combination of rational choice and game theory can be used in conjunction with one another to not only gain an understanding but also a prediction of criminal behavior.

Rational choice theory argues that individuals commit crimes to achieve certain outcomes, particularly economic gain. Therefore, the application of the theory in combination with game theory creates a persuasive argument for the combination of rational choice and game theory to predict criminal behavior.

McCarthy looked at some elements that influence decisions and the choices people make -- particularly the fact that people do not make choices in a vacuum,  Therefore, there are a number of factors that are incorporated into the decision to commit a crime. The application of game theory is relevant because it relates to the process of creating and generating a decision based on either simultaneous or subsequent actions that impact the way in which a decision is made.  Game theory incorporates assumptions that individuals will make assumptions regarding the actions of others and how they will react to the situation. Additionally, it incorporates the assumption that individuals will act rationally and strategically.  The theory also looks at the majority, where people typically act on the decisions of other individuals.

This theory was applied to offenders and their interactions with law enforcement.  The relationship between offenders and law enforcement typically is that they obey the law when law enforcement is present and prefer to commit crimes when law enforcement is absent. This relates the interactions between the two parties to the idea that individuals often make decisions based on the actions of others.

Critique:
Game theory gives insight into the decisions that individuals make, while simultaneously operating under the assumption that everyone will act rationally and in their best interest. While there is a deductive manner to the application of the games and the outcomes that will likely result from it, the underlying assumption incorporated is that criminals will be rational and act in their best interest and with the best outcome.

It was interesting to look at the application of game theory and the intersection of rational choice and crime as an entity.  Specifically, the element that addressed the likelihood of offenders acting when law enforcement personnel are present is decreased coincides with the application of game theory and the influence that other actors have on the main individual.  This theory is not without flaws, but it does appear to be applicable to the general society as well as the reasoning behind certain individuals and their motivation to commit crimes.

McCarthy, C. (2002). New economics of sociological criminology. Annual Review of Sociology, 28, 41-22. Retrieved from http://www.jstor.org/stable/3069248?seq=6&Search=yes&searchText=%22Game+Theory%22&list=hide&searchUri=%2Faction%2FdoAdvancedSearch%3Fq0%3D%2522Game%2BTheory%2522%26f0%3Dall%26c1%3DAND%26q1%3D%26f1%3Dall%26acc%3Don%26wc%3Don%26fc%3Doff%26Search%3DSearch%26sd%3D%26ed%3D%26la%3D%26pt%3D%26isbn%3D&prevSearch=&item=20&ttl=17489&returnArticleService=showFullText&resultsServiceName=null

Relations between Free Trade and Economic Protection: A Game Theory Analysis

Relations between Free Trade and Economic Protection: A Game Theory Analysis

Summary:

Lin and Lee (2012) state that there is a conflict between mutual policies between countries over discussions that result in a stalemate between free trade and environmental protection issues.  The authors' goal of their research endeavor was to find actions in which both parties can pursue both policies of free trade and environmental protection that do not harm both sides and can result in positive-sum games for the parties involved.  Thus, Lin and Lee (2012) had the purpose of using game theory to: "examine the possibility of including environmental issues into trade negotiations and examining the impasse confronting the coordination between trade and environmental protection (592)."

The results of the study demonstrated that once trade and environmental liberalization were brought in together within negotiations, the international community was more likely to combine both trade and environmental issues into the decision-making process.  Overall, this finding benefited developed nations interests' and forced developing nations to join in the discussions or be left out all together.  Moreover, the developing nations seem to stick to their own agenda and have different priorities as compared to the developed nations in terms of trade and economic polices.  The authors state that the extreme differences between the North and the South countries disturb the balance between trade and environmental policy negotiations (Lin and Lee, 2012).

Through various Prisoner Dilemma's (PD),  Lin and Lee (2012) examined various issues that pertained to trade agreements and environmental protection, which can be witnessed by the graphic above.  Through the utilization of the above PD, the authors came to the following conclusions for developed and developing nations.  Developed nations believe that trade restrictions were the most powerful decision-making option to solve environmental issues against the second party.  Environmental protection is a top priority in terms in conducting international trade.  Lastly, the developed nations were more likely to strive for unification of international environment standards as compared to developing nations (Lin and Lee, 2012).

The PD emphasized that developing nations view economic development and the elimination of poverty as their top challenges.  In addition, developing nations affirm that tariffs known as green barriers which are created for environmental protection, negatively affect the developing nations export economy.  The developing nations can not meet the environmental standards initiated by the developed nations due to limited financial resources.  This aspect is likely to increase production costs in developing nations, but also will reduce the amount of exports.  Lastly, the developing nations see it as unethical that the developed nations ban their products from being exported into their country, but consistently move their environmentally harmful production facilities into the developing countries around the globe (Lin and Lee, 2012).

Critique:

It was interesting how the authors utilized game theory to examine the possibility of including environmental issues in trade negotiations between developed and developing nations.  The use of PD displayed some interesting characteristics of the differences in decision-making and viewpoints as it curtailed to trade and environmental negotiations between the viewpoints of developed and developing nations.  The findings presented in this study would be able to display to decision-makers the differing viewpoints between developed and developing nations and applying this knowledge to better serve the needs of the two parties.  Hence, creating positive sum games.  This knowledge may be able to provide decision-makers the ability to likely increase the effectiveness of negotiations that bring in environmental factors into trade agreement conversations.

However, the study needs to elaborate further what changes to the PD would create the scenario for developing nations to become more willing to participate in trade negotiations that bring in environmental issues.  More in-depth utilization of PD would allow the authors to determine what scenarios made developing nations stick to their own criteria/decisions and how could those scenarios be reversed to allow for mutual cooperation between the parties. Overall, this study brings up intelligence gaps that could be further researched to provide possibly more actionable intelligence for a decision-maker who deals in negotiations of trade and environmental issues between nations of differing economic standing.

Source:

Lin, C.M. & Lee, C.K. (2012). Relations between free trade and economic protection: a game theory analysis. International Journal of Management, 29 (2), 591-605. Retrieved from http://ehis.ebscohost.com/ehost/detail?sid=6e2f16a5-67b2-478f-b717-fad8b32698c0%40sessionmgr10&vid=1&hid=6&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=bsh&AN=76442241. 

Role of Game Theory in Eliminating Terrorism

Summary:

This article focuses on evaluating the two primary categories of counter-terrorism policies, the offensive and defensive counter-terrorism policies. The authors explain why most countries rely on defensive efforts rather than offensive activities. According to the authors’ game theory is an extremely useful methodology to examine terrorism because it demonstrates strategic interactions of opposing interests of terrorists and the governments being targeted. Game theory characterizes interfaces among terrorists and their targets based on offensive and defensive activities between both parties. Game theory can be applied to negotiations between terrorists and governments.   

The authors focused on further analyzing whether a government’s policy of not negotiating with terrorist hostage takers will prevent other terrorists from taking hostages. The idea behind this is that if the terrorist know they have nothing to gain, then they will not resort to this type of activity. This belief has become one of the four pillars of US policy regarding transitional terrorism. Based on game theory, this type of ideology characterizes failure because past concessions indicate that the government has special consideration depending on the situation and some terrorist organizations may still benefit from failures through heavenly rewards or through media exposure. Through global networking, terrorist organizations have managed to limit the effectiveness of countries defensive efforts. The authors emphasize that international corporation among the government is necessary to combat terrorism.     

Critique:

This article provided five advantages of the game theory for intelligence purposes. However, the authors’ did not have enough examples to properly show the effect of game theory in reducing terrorism.  Only two examples were presented that managed to portray the role of game theory in combating terrorism through negotiations. Additionally, I disagree with the idea that terrorists will naturally seek the weakest link or most unstable countries to target for attacks. By targeting the weakest link, the terrorists will only gain pleasure in thinking that they will gain a heavenly reward for committing such an act. Also, the weakest link may not have the capability to provide ransom money for hostages. Therefore, the terrorists will not gain any monetary benefits from taking hostages. Lastly, the authors’ failed to mention the limitations of the study.

Source:
Mangladevi, M., & Dhaigude, R. (n.d.). Role of game theory in eliminating terrorism. (2013). International Journal of Advanced Computer and Mathematical Sciences, 4(1), 685-689. Retrieved from http://bipublication.com/files/IJCMS-V4I1-2013-15.pdf

Applying Analytical Methods to Study Terrorism

Summary:

In Applying Analytical Methods to Study Terrorism, Sandler and Enders discuss choice-theoretic and game-theoretic methods and the study of trends and forecasting in terrorism. They apply game-theory as a method to the study of terrorism in which "strategic behavior among rational players is assumed that allows a player to anticipate the response of others to its own actions". Operating under the assumption that terrorists function as rational agents, game theory has the potential to allow us to make predictions and decisions in response to their threats and attacks. It also allows for strategic advantage and bargaining behavior for decision-makers, a behavior relevant to terrorist organizations. Additionally, choice-theoretic models allow for a subject to maximize their profit (i.e. greatest reduction in terrorism) and forecast terrorism patterns.

Data on trends of terrorist attacks is provided in context, concluding that as rational actors, terrorists will reduce risk by choosing the mode of attack with least risk: bombings. With additional statistical analyses, studies have shown that a rise in casualties associated with transnational terrorism began with the U.S. embassy takeover in Tehran and the Soviet invasion of Afghanistan in 1979. Trends in the cycles of threats versus incidents with deaths from terrorist attacks can also be evident. "Threats had a primary cycle of just under year, while incidents with deaths had a primary cycle of almost 10 years." Understanding these cycles aids in the decision-making for when to augment defenses. Intervention analysis is another statistical technique used in counterterrorism that measures unintended consequences of policies on terrorist attacks internationally.

The authors state that "game-theory is an appropriate tool for understanding the strategic interactions associated with terrorists and those charged with counterterrorism." They proceed to offer a game-theory matrix involving two countries who both have the following decisions: preempt a terrorist attack, maintain the status quo and do nothing, or defend against a terrorist attack. The matrix combines two 2 x 2 matrices together. This figure is shown below.

According to this matrix, the Country A would gain the most by doing nothing if Country B preempts and therefore allowing Country A to "free-ride" on their decision and in essence gaining from the offensive attack (top, left 2 x 2). This is reliant on the other country actually preempting an attack. However, if the roles of costs and benefits are switched, then each country's dominant strategy is to defend rather than maintain the status quo, but this has the lowest payoff in the game (bottom, right 2 x 2). Thus, this leads to an overly defended but unsafe world.

Critique:

The provided data and statistical analyses on transnational terrorism was helpful in providing a context and additional background information on the decision-making skills of terrorists. This is important when applying the method of game-theory to this context because there first must be an understanding of the subjects in the method. The authors are also effective in explaining the benefit of game-theory's application to counterterrorism and showing how terrorists can exploit asymmetric warfare, evident by the matrix.

A serious limitation in this study, however, which was noted by the authors is that the matrix does not include terrorists as players. Although this issue was highlighted in the article, the authors did not attempt to include terrorists in the game-theory model they presented. This would require a new three-agent game including two countries/ governments and terrorists; it would have been nice to see an attempt at somehow creating such a matrix in the article. Because a country's decision on how to proceed in counterterrorism relies heavily on the behavior of the terrorist organizations, including them as a player in the game would add new beneficial insight into the methodology for this field. Nevertheless, the authors did apply a double matrix in which country A and B had two likely scenarios and weighed the cost/ benefit of the two decisions in relation to each other which was interesting and valuable.

Source:

Sandler, T., & Enders, W. (2007). Applying analytical methods to study terrorism. International Studies Perspectives, 8, 287-302. Retrieved from http://ehis.ebscohost.com/eds/pdfviewer/pdfviewer?sid=312e4b6a-1c25-4322-8422-638d3ebe16fa@sessionmgr10&vid=2&hid=17

Computer Gaming and Interactive Simulations for Learning: A Meta-Analysis

Summary:

The article Computer Gaming and Interactive Simulation for Learning: A Meta-Analysis attempts to uncover whether games and interactive simulation or traditional teaching methods are more effective for learning.  The study goes on to further evaluate in what situations one method may be more effective over another.  The results concluded that games and interactive simulations were more dominant for cognitive gain outcomes.

The study’s premise is contingent on the idea that as software decreases in price becoming more available and incorporated more heavily into education the use of game theory should increase.  This is in response to a theory stating that playing games allow the brain to work more efficiently and takes in more cognitive material than it would in a traditional setting.  Additionally, motivation, a major focus for teachers based on the premise that motivated, interested students will learn more and learn faster will also increase with the use of games.  Game theory, specifically video game theory supports the argument that computer games are highly engaging, motivating, and interactive.

One finding showed males exhibited no preference to either method while females showed a preference for the game and interactive simulation programs.  Additionally, students showed preference for the game and interactive simulation programs.  When teachers were controlling the programs no advantage was found for either method.  When a computer dictated the sequence of the program the study found that the traditional teaching method was favored over games and interactive simulations.  The study also evaluated the subjects’ attitudes toward learning using both methods, this area of the study found that individuals’ attitudes were significantly better than those using traditional learning. 

Critique:

Overall, this was a effective study.  The authors made note of the limitations within the study as well as the issues they faced when collecting literature.  They found a plethora of articles were not sufficient in that they lacked a control group.  This was the largest methodological flaw found in the collected literature.  Other limitations included a lack of statistical data, a lack of demographic details as well as a lack of a description of the programs and activities used as interventions in sufficient depth to categorize properly.

Gender was an important aspect of the study.  The findings concluded that there were not enough males to effectively measure their preference of games and interactive simulation or traditional methods.  Considering the weight put into this area of the study an adequate number of male participants would strengthen the study.  Therefore, although the authors mentioned this limitation properly, accounting for it in the beginning would prove highly beneficial to the study and its findings.  The authors further suggest the part of the study analyzing learner control stated there is little data to draw meaningful conclusions about learner control options besides interactive simulations or games that required the subject to navigate using their own preferences.  The type of activity portion also yielded subpar findings.   The study incorporated two types of activities using the computers.  The results of the interactive simulation programs had a large fail-safe number while the gaming programs yielded a low fail-safe number giving conflicting results.  The low fail-safe numbers suggest low reliability.  The authors state the research base is insufficient to draw reliable conclusions. 

Lastly, although this article does not specifically mention intelligence, its findings are easily applicable to education in the intelligence field as well as intelligence work.  By applying the findings of this article to intelligence through game theory, higher cognitive gains and better attitudes toward learning will likely take place.  

Source:

Bowers, C.A., Cannon-Bowers, J., Muse, K., Vogel, D.S., Vogel, J. J., Wright, M. (2006). Computer gaming and interactive simulations for learning: A meta-analysis. J. Educational Computing Research 34(3) 229-243. Retrieved from researchgate.net 

Applications of Game Theory in Support of Intelligence Analysis

Bruce Bueno de Mesquita wrote an article in Intelligence Analysis: Behavioral and Social Scientific Foundations which examined how game theory can be applied to intelligence analysis.

Mesquita begins by defining game theory as "a body of reasoning, grounded in mathematics but readily understood intuitively as a reflection of how people may behave, particularly in situations that involve high stakes for them. It is part of a family of theories that assume people are rational, meaning that they do what they believe (perhaps mistakenly) is in their best interest." Mesquita discusses the basics of game theory, such as anticipating how others will act, constraints, considering counter-factual actions, and using cost-benefit analysis to help make decisions. Mesquita argues that game theory helps integrate knowledge based on different theories, such as structural, organizational, behavioral, and psychological.  

In the realm of national security, Mesquita discusses five constraints that effect analysis. These are uncertainty, risks, distribution of costs and benefits, coordination, and patience. According to Mesquita, game theory examines uncertainty in two different ways: random shocks to a situation and not knowing a critical piece of information about a player. Random events can change the expectations of a player, leading to a change in action. Examples can be a key figure dying or an earthquake, both events of which can alter the focus of a decision maker. Not knowing about the player also increases uncertainty, as it is difficult to determine what game players are playing. 

Risks deal with the probability of alternative results which arise from different choices of action. Part of what makes risks so important is how players respond to risk. Some are more prone to taking risks while others are less prone. Mesquita gives an example of risk while discussing the Shah of Iran. Nondemocratic leaders who stay in office past two years see a significant year-to-year decline in the risk of being forcibly removed from office, as long as all else is equal. This is why it was a surprise when the Shah was ousted after over 20 years after his coronation. What was not taken into account was the Shah's terminal illness, which tend to increase the risk of a nondemocratic being deposed.

Distribution of costs and benefits deals with the outcomes of games. This leads to players bluffing in order to gain their desired outcome. There are two types of bluffing: cheap and costly. A cheap bluff is a bluff that costs the player nothing, such as rhetoric or hinted threats in official communiques. A costly bluff, which is more likely to be noticed by the other player or players, could be the mobilization of military forces or missile tests. The more costly the bluff, the more likely the other players will believe it.

Coordination is a problem that arises when players work towards a common goal or resolution of an issue. There are two types of coordination issues: pure coordination problems and more complex coordination problems. A pure coordination problem is deciding which side of the road allied tanks should drive on. A more complex coordination problem is creating incentives to get allies to coordinate in a time of war. En example of this could be promising an ally territory from the enemy if they were to invade at a certain time.

Lastly, patience examines the value a given cost or benefit has today when compared with tomorrow (or longer). The more patient an decision maker is, the closer the future cost or benefit is to the current one. The more impatient the person, the greater the difference in current and future costs and benefits.

Mesquita concludes by discussing some of the limitations of game theory. Game theory makes very heavy assumptions about information and people. For a game to work, it requires that some critical information is common knowledge (Player A knows that Player B has nerve gas, and Player B knows that Player A knows). Same applies with people: Player A assumes that Player B will continue playing the game, or assumes that there will be no more players. Lastly, game theory models can lead to very (sometimes overly) precise outcomes. This can cause issues as it can be difficult to be adaptive with outcomes

Critique

Mesquita's article was very readable, which is an issue that I have come across when attempting to learn more about game theory. In particular, his caveat about rationality in game theory was right on the spot: what may be irrational to some is rational to others. He explained concepts very well and gave great examples of the different types of constraint. That being said, there were two issues that I can see some having with his discussion of game theory and intelligence.

First, at no point did Mesquita use mathematical formulas. While this was not an issue with me, as I would not have understood them anyways, it could be problematic to those that want to know how to do game theory mathematically. The purpose of the article was not to teach the reader how to crate game theory models, but to explain how it can be applied to intelligence. Even so, it could have been useful to those who are interested in creating a game theory model.

Second, while his discussion of game theory was interesting and the examples he used helped explain the concepts, most of it seemed to be more focused on international relations than intelligence. Granted, game theory is heavily used in international relations, so it would make sense for the majority of examples to be international relations-focused. The constraints he discussed definitely have applications in the intelligence field, but this application seemed to be a secondary objective.

Source:  Mesquita, B (2011) Applications of Game Theory in Support of Intelligence Analysis. Intelligence Analysis: Behavioral and Social Scientific Foundations,  57-82. Retrieved from http://www.nap.edu/openbook.php?record_id=13062&page=57



  

Terrorism & Game Theory

Summary:
Sandler and Arce M. (2003) use many different game theory models to model various terrorist related games.  The authors argue that game theory is an appropriate method for terrorism analysis for several reasons.  Although the authors offer a list of reasons for the appropriateness of terrorism related to game theory, three reasons in particular stood out to me.  One of these reasons is it is a strategic move game between terrorists and the targeted government.  Each moves in accordance to the moves the other makes.  Additionally, each actor believes themselves to be rational.  The example used was how increased use of metal detectors diverted terrorist efforts from airplane hijackings to kidnappings.  Lastly, neither side has complete information.  This unknown leads to some guesswork, ultimately leading to non-optimal results.

The authors use many examples to show that game theory is applicable to terrorism studies.  Three of these examples show basic but very important points for arguing how game theory is applicable to terrorism studies.  The first compares two entities (countries) who wish to prevent terrorist attacks on their land and spend x amount of money on terrorism prevention.  The second looks at the prisoner's dilemma and how it relates to harboring terrorists.  The last uses the United States and the European Union as an example for comparing how each could attack a terrorist group.

The authors use an example of how both the United States and the European Union wish to prevent terrorist attacks on their land.  Because both frequently do not share their spending values with one another, each is forced to guess how much the other has spent on terrorism prevention.  If one entity spends more than the other on terrorism prevention, and the terrorists can see this, the terrorists will pick the more vulnerable entity.  Even if both entities knew what the other was spending on terrorism prevention, a suboptimal result would emerge.  The entity who has less spent on terrorism prevention would increase their spending to eclipse the other, and so on and so forth until you have a large buildup of terrorism prevention.

Another example used is that of terrorism's application to the prisoner's dilemma.  Terrorist groups have to have a location by which they can set up camp.  Each country is faced with three potential options: A- do nothing against terrorism and face having terrorist attacks occur in your country, B- retaliate against the terrorists by force, or C- accommodate the terrorists in exchange for safety.  All countries do not wish to be attacked by terrorist attacks and many also do not have the power, authority, or planning to retaliate against terrorist groups.  Thus, the least optimal option (option C) for those who wish to rid the world of terrorism is most likely taken in order to protect themselves.

Lastly, another model used to represent compares the United States and the European Union.  Each entity has the option to preemptively attack against a particular terrorist group in order to eliminate them.  If both entities work together and simultaneously attack the terrorist group, the costs are low.  However, if one entity decides to attack while the other free rides of the attacking entity, the cost to the attacking entity is higher than had the entities worked together.  The same goes for the other side.  Thus, because neither entity wishes to end up attacking alone and incurring the full cost of the attack, both entities take the initiative to attack the terrorist group, and the terrorist group continues to exist. 

Critique:

I feel the authors did a good job proving how game theory is applicable to terrorism studies.  Although each terrorist group can target as many countries or entities they wish, simplifying the model down to country x and terrorist group y and how they interact can prove very valuable to country x.  It could also prove valuable to other countries as well, particularly if their security declines due to increased security for country x.   This article definitely applies to security analysis as terrorism studies are well within our realm of work.

One issue I have is the applicability of these models to actual instances.  There is great merit to designing model diagrams from things that could not otherwise be tested (personally, I would not want to run or see the results of a real life test regarding terrorism and the potential outcomes).  Much like many models found in economics and beyond, the models work well in isolation.  However, the minute you begin to add even a dozen countries the game theory model breaks down due to exponential complications of 'if's and then's'.  As stated previously, showing one, two, or even three countries could be immensely valuable to many countries, however, real life examples incorporating all participants would be just too cumbersome, if not impossible to model with so many moving parts.

Source:
Sandler, T. and Arce M., D. (2003). Terrorism & Game Theory. Simulation Gaming, 34(3), 319-337. Retrieved from http://sag.sagepub.com/content/34/3/319

Game Theory-Based Identification of Facility Use Restriction for the Movement of Hazardous Materials Under Terrorist Threat

Summary:
Reilly, Nozick, Xu, and Jones (2012) developed a model of interactions among government, terrorists, and carriers of hazardous materials using game theory. Their intention was to understand how governments might prohibit certain travel routes for carriers shipping hazardous materials, how the carrier might decide which routes to take in response to the prohibitions and the threat of terrorism, and how terrorists might target available links and in what frequency. An extension of a two-person, non-zero sum game, Reilly et al. constructed a  non-cooperative, non-zero sum three-person game in which the government is the leader and both the carriers and terrorists are followers.

The idea of the research is that governments will respond to threat levels of terrorist activities by restricting the transportation of hazardous materials that could place the greater population at risk. These restrictions would likely come in the form of prohibited travel routes for carriers to reduce risk. In reaction to these restrictions, carriers must decide which routes to travel while considering travel time and consequence measure, a combination of population exposure and accident probability. Terrorists meanwhile react to government restrictions by choosing targets whose access will not be impeded by such route restrictions. The research operates under the assumption that the terrorists will be equally aware of route restrictions as carriers will be.

A case study applying this to the rail systems used by carriers of hazardous materials found 259 links which could be considered of interest to the government in terms of risk restriction. The research considers the change in expected payoff for terrorists upon restrictions, compared to the change for carriers. In some surprising cases, the expected payoff for terrorists increases with government restrictions, while other times it returns to the same point as no restrictions, though at a substantial expected loss for carriers who cannot transport a percentage of their total carloads due to the restrictions. This shows that despite government's best intentions, route restrictions may further exacerbate threats.

Critique:
The research by Reilly et al. only represents the interactions between the three parties for the movement of hazardous materials by a single carrier. While this creates reasonable rules to predict carrier and terrorist actions with regards to a maximum allowable expected payoff for the terrorist, it significantly limits the scope and utility of the research in the intelligence and policy fields. As the authors note, to improve the research the formulation and solution procedure should be expanded to handle multiple carriers with several origins and destinations. Additionally, the current research lacks depth in that it only considers single attacks by terrorist organizations, rather than coordinated attacks. Given the maximum profit-seeking nature of terrorists, this inclusion would also substantially improve the utility of the research.

The research, or perhaps the limitation of game theory, also fails to include the albeit unlikely scenario that government restrictions on particular routes would cause either carriers to cancel shipments altogether and/or for terrorists to change targets away from hazardous materials if the expected value is not significant enough. In some of the cases presented in the findings, the carriers project substantial loss, though this application of game theory does not account for the potential political backlash carriers may inflict on government, complicating the goals of government. It is narrow-minded to assume that any government will strictly look to minimize security threats without considering the economic and political backlash such closures would have. I do not consider this a failure of this particular research, rather a weakness of game theory itself.

Source: Reilly, A., Nozick, L., Xu, N., and Jones, D. (2012). Game theory-based identification of facility use restriction for the movement of hazardous materials under terrorist threat. Transportation Research Part E, 48(1), 115-131. Retrieved from http://www.sciencedirect.com/science/article/pii/S1366554511000810

Summary of Findings (White Team): News Analysis (1 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the 8 articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in March 2013 regarding News Analysis specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.


Description:
A vaguely defined technique that includes the analysis of news in some way but generally relies on an established analytic technique such as sentiment or content analysis.

Strengths:
1. Fast way to find information on a topic of interest.

Weaknesses:
1. Technique is not well defined.
2. New sources can be skewed by whether one is left or right leaning politically.
3. The analysis of the news sources can be biased by the analysts inherent biases on the proposed topic/personal experiences.
4. Articles affected by high emotion.
5. Extremely affected by circular reporting.
6. Just examining news sources is not an effective approach to creating accurate. intelligence estimates.
7. Difficult to approach or carry out due to the lack of structure.
8. Produces estimates of low analytical confidence.

Step by Step Action:
1. Analyze a specific topic utilizing news articles from various publishers.
2. Determine whether information related to the topic can be corroborated between the sources.
3. Produce an estimate based on information found.
4. Provide the analytic confidence for the findings.

Exercise:
Provided the class with a topic to research using only online news sources. The question was, “ Has the U.S. government been training Syrian rebels?” The class was given ten minutes to research the topic. Then as a class we discussed our findings to the proposed topic. We discussed whether news analysis is an effective tool to provide reliable intelligence estimates and what does the term “news analysis” really mean.

Summary of Findings (Green Team): News Analysis (1 out of 5 Stars)

News Analysis

Green Team

Rating (1 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the 8 articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in March 2013 regarding News Analysis specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Description:

News analysis is a poorly defined analytic technique that is often confused for a separate technique, such as sentiment analysis, content analysis, and computational linguistics all applied to news sources. News analysis is meant to analyze the qualitative and quantitative attributes of news sources, with particular focus on sentiment, context, and novelty. News analysis, or other techniques referred to as news analysis, is frequently utilized for the financial industry to predict stock movements and consumer confidence.

Strengths:

• Can be applied in a relatively short amount of time
• Can be done individually or within a group
• Can examine multiple facets of news, including sentiment and novelty

Weaknesses:

• Not easily defined -- ambiguous in regards to what is measured, how to conduct it, and the information it provides
• Unable to separate human bias from news analysis
• Frequently used as a guise for a separate technique -- sentiment analysis, content analysis, and computational linguistics
• Often limited in use to textual news sources
  

How-To:

1. Determine a topic that is likely to be covered in the news.
2. Search for relevant news articles from news sources (online, print, or other)
3. Take into account the different biases that certain sources may contain along with personal biases from previous knowledge or personal interpretation.
4. Note which information is important, relevant trends, and anything else noteworthy.
5. Decide likelihood of topic and confidence interval.

Personal Application of Technique:

The class was tasked with using only online news sources to create an estimate to the question: Has the US government been training Syrian rebels?  The class had ten minutes to look at different news sources and create an answer to the question.  In addition to answering the question with an estimate, the class had to assign an analytic confidence in the assessment.  This exercise reiterated the difficulty in using this method, since it is not easily defined nor is it possible to eliminate biases or the framing through information already known.  Additionally, the issue of Circular Reporting was raised through this application and was an additional entity that should be taken into account when conducting news analysis.

Rating: 1 out of 5 stars