Is It Safe To Go Out Yet? Statistical Inference in a Zombie Outbreak Model

Summary: 
The authors Calderhead, Girolami, and Higham (2010) wrote a paper dealing with the potential outcomes of a zombie outbreak using Bayesian theorem to support their conclusions.  Since there has never been a zombie outbreak it is logical to use Bayesian theory to account for unknown data that can be estimated.  Estimations can be made regarding a zombie outbreak, and in turn these estimations can be used in particular ways to end with likely outcomes.

The idea for using Bayesian theory applied to zombie outbreaks starts with a logical probability.  In the case of this paper, the authors state that in one day the far extremes of probability are that no human will turn into a zombie and all humans are converted into zombies.  This probability (labeled prior) is then updated as new data is used (such as different quantity of days) and thus this posterior distribution then becomes the prior and the process is repeated.

The authors then state that many questions can be answered by successfully finding a likely distribution for human to zombie conversion rates.  Such questions include how many soldiers should be mobilized, the scale of quarantine needed, and whether or not it is alright to leave a hiding spot given the number of zombie sightings during a particular time span.  The authors also emphasize that since the rate of change from human to zombie is likely to not be constant, the beta (conversion coefficient) should be a range and not a singular number.

One of the model comparisons the authors use is the comparison of two models: one that assumes zombies can attack alone while the other shows that after a rumor is circulated, zombies are believed to only travel in pairs.  The authors then seek to disprove the second model through Bayes factors (posterior odds = Bayes factor * prior odds), in which statistical evidence for the first model is weighed heavier than the second.  The authors find that the first model with the least amount of noise (introduced as Gaussian distributed noise) is more likely.  This means that the experimental data deviated the least from the expected curve.  Adding additional noise negatively impacted the Bayes factor (shows as how strong the evidence was against the second model.

Another example the authors used Bayesian theory on was for answering the question of whether or not it is safer to leave a hiding spot based on the number of zombies spotted in the past few days.  The authors use two types of analysis for this: one that does not have any observations of previous day's zombie sightings and one that has five day totals of zombie sightings.  The stated the zombie sightings for the second case were 123, 127, 104, 92, and 74.  The left column of the figure below shows Bayesian factors applied to the first model mentioned above, and the potential outcomes.  The right column shows this process with a second layer of data (the five days of observations) which greatly reduces the uncertainty regarding potential zombie totals for the next 45 days.  Thus, due to these observations and using Bayesian theory uncertainty can be greatly reduced and the chance of surviving longer during a zombie outbreak are much higher.

Critique:
I found this article to be fairly complex to read with no experience with Bayesian theory.  However, I really appreciate the application of Bayesian theory to a zombie outbreak.  Although this article topic is (most likely) fantastical, it is well constructed and thoughtful.  Honestly, the topic caught my eye and I doubt I would have tried as hard as I did to understand Bayesian theory had it been on a drier topic.

One issue I had with this article is that it was clearly meant for someone that had previous experience with Bayesian theory.  At times the authors referenced various aspects of Bayesian theory without defining them.  For example, the authors did not just list Bayes factors but instead referenced an article.  For the average reader this does not make reading and understanding this topic any easier.  Additionally, simple Bayesian theory is not that long or difficult to write out and would have saved me time having to look it up to double check I was thinking about the correct thing.

This article is not related to intelligence, save for if there were to ever be a zombie attack it would prove useful for intelligence analysts to extend their lifespans.  However, this model could be applied to medicine for spread of infectious diseases if the transmission rate is unknown.  Instead of zombies and humans there would be infected and healthy individuals.

Source:
Calderhead, B., Girolami, M., & Higham, D. (2010). Is It Safe To Go Out Yet? Statistical Inferencein a Zombie Outbreak Model.University of Strathclyde, United Kingdom.  Retrieved from http://www.strath.ac.uk/media/departments/mathematics/researchreports/2010/6zombierep.pdf

Bayesian Inference Analysis of the Uncertainty Linked to the Evaluation of Potential Flood Damage in Urban Areas

Summary:
Fontanazza, Freni and Notaro explain that flood impact on highly urbanized areas can be high and has the potential to increase with the effects of climate change. Thus, decision-makers prefer reduced uncertainty when planning flooding mitigation and prevention. This analysis is beneficial because there exists uncertainty in the physical processes that must be simulated in hydraulic models and in the limit of data for model calibration. Additionally, there are sometimes measurement errors in terms of depth-damage curves which can affect data.

In this article, the authors applied Bayesian probability analysis to a case study of Palermo, Italy to determine whether uncertainty decreases with the addition of data. Bayesian analysis has two benefits: "parameter estimation and uncertainty analysis" in both hydraulic model parameters and the depth-damage curve coefficients. They create a mathematical probability model using Bayesian analysis including values in the equation for "the uncertainty of a generic model parameter", "observed values" and a "likelihood function."

The authors split the historical data into three sections, that from January 1994 to April 1999, from May 1999 to January 2003 and from February 2003 to December 2008, to determine whether uncertainty would decrease with each subsequent addition of a data group. The land use in the Palermo case study was identified as mostly for residential dwellings with 88 percent of the area being impervious. The following three images show the reduction in uncertainty once more data became available, demonstrating that Bayesian probability analysis did in fact reduce uncertainty. By the addition of only the second set of data (in the second image), the reduction in uncertainty was about 40%, without a reduction in reliability.

Critique:
There were some limitations in Bayesian analysis, such as that it relies on an initial hypothesis which can often be subjective as well as that the approach may not be objective if the parameter distribution is not made on physical observations. Nevertheless, I noticed many advantages of the methodology. The authors were successful in demonstrating its effectiveness with a case study, thus showing with real but historical data, that a significant reduction in uncertainty was possible. They also accounted for the aforementioned limitations with additional probabilistic analyses on the parameter choices to ensure that they did not skew the results.

The interest in reducing uncertainty for a decision-maker seems to be the same for any profession. I would be curious to see how this could be applicable to a study of crime mapping in which it is determined whether a decrease in uncertainty actually does occur with an increase in data. This could perhaps be applied to the "Newton-Swoope Buffers" in ATAC Workshop that are intended to determine the location of an offender's home or business. These buffers change with each additional piece of information, seemingly because they are becoming more accurate with more data. A Bayesian probability analysis could be applied to this tool to determine its effectiveness and additionally, application to law enforcement intelligence.

Source:
Fontanazza, C.M., Freni, G., & Notaro, V. (2012). Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas. Water Science and Technology, 1669-1677. doi: 10.2166/wst.2012.359

Fusion of Intelligence Information: A Bayesian Approach

Elizabeth Paté-Cornell presents a classical probabilistic Bayesian model that she believes can be utilized by the intelligence community to aid in the fusion of intelligence information. The awareness of the need for such fusion is apparent in the wake of September 11, 2001, which the author suggests the probability of impending attacks can be found through a Bayesian analysis. The author's two major arguments for the use of the Bayesian model in the IC, particularly related to terrorist attacks, is that it allows for the computation of the posterior probability of an event given the probability of the event prior to observing signals, and the quality of the signals based on the probabilities of false positives and false negatives.

Summary:
The author begins by discussing the problems associated with a fusion of information within the US intelligence community, namely difficulties in ensuring internal communications and the difficulties in merging the content of multiple signals, some more sharp than others, some dependent or independent of others. This research claims that Bayesian analysis can be applied to help solve the difficulties associated with the latter and is explained in terms of identifying the probability of an impending terrorist attack. It should be noted the author does not claim the model will better detect impending terrorist attacks, rather that it can increase the probability that an attack plan is foiled through guiding "clear thinking at a time when the amount of information is large and confusing and intuitions can be seriously misleading" (Paté-Cornell, 2002, p. 454).

The elements of Paté-Cornell's Bayesian model can be explained through the following notations:

 

Namely, the event of interest throughout the article is an impending terrorist attack. Through the model, the author presents formula that addresses both the prior probability of the event occurring before reading signals, such as intercepted telephone conversations, as well as the quality of the signals. The formula, as appears in the following figure, considers what alternatives to the event of interest could occur in conjunction with the signal, a very important thing to consider in the intelligence field.

Additionally, the formulas the author presents address the chances the signals observed are false positives, or that some signals has been missed (false negatives), and how these affect the probability of a future terrorist attack. The probability of false positives can be calculated by considering the prior probability of the impending attack without considering the signals, in conjunction with the rate at which the signal occurs during normal sensor operation when the event does not occur. She explains that her definition of false positives and its application in Bayesian analysis is most useful to the intelligence community because of its consideration of the prior probability of the event, especially considering how drastically the prior probability has increased post-September 11.

Estimating the prior probability of an impending attack can be considered as a combination of the intention of the enemy to attack, the effective planning of that attack (ie. the ability of the perpetrators to coordinate a plan and avoid detection), and the successful implementation of the plan on a given day (ie. the ability of the perpetrators to carry out the plan and avoid target's safeguards). The author argues that the identification of these probabilities alone is of use to the intelligence community given the chance to reduce the probability of an attack attempt through various measures hitting these areas (ie. cutting flow of funds or increasing security).

Critique:
The research applies a Bayesian model by using hypothetical numerical illustrations for the interpretation and fusion of intelligence information and could be strengthened through the use of real-life numerical examples, though sensitive in nature. Additionally, the author switches between examples for multiple formulas, sometimes relating it back to the overarching theme of terrorist attacks, while other times relying on the unrelated example of testing chemicals for poison. This back-and-forth detracts from the overall readability of the research and does not add to the application of the model to the intelligence community. The author uses good examples of potential signals used intelligence but does not carry them throughout the research.

The author further admits some limitations of the research. First, the assumption that both the event and the signals are black and white, either they occur or they do not occur, which is not always the case, particularly in the intelligence community. Further, the research assumes that the likelihood of false signals, whether positive or negative, remains the same throughout time, also unlikely in the intelligence field. Finally, many of the sources of data for such a model are difficult to accurately quantify, including the frequency of past observations, reliability data for sensors or links, or expert opinions. For instance, how can we accurately, and quantitatively, determine the reliability of human intelligence?

Overall, the research is very interesting and provides insight into the intelligence community and process. Admittedly, the approach only helps solve the second half of the information fusion, not aiding in the means of internal communication among the intelligence community, however, any reduction in uncertainty, particularly through objective means, helps the success rate of thwarting plans of terrorists attacks, or other such problems addressed by the intelligence community.

Source: 
Paté-Cornell, E. (2002). Fusion of Intelligence Information: A Bayesian Approach. Risk Analysis: An International Journal, 22(3), 445-454.

The Deterrent Effect of Arrest in Incidents of Domestic Violence: A Bayesian Analysis of Four Field Experiments

Summary:
The authors of this study, Berk, Campbell, Klap and Western (1992), looked at a number of different studies conducted following a study of the Minneapolis Police Department.  The initial study looked at police responses to misdemeanors for domestic assault.  There were three response options available to the police officers, and this measures were supposed to be given out randomly.  These options were (1) to arrest the suspect, (2) remove the suspect from the premises for 24 hours, and (3) to attempt to restore order at that moment.  Through a series of initial and follow-up interviews, it was determined that arrest of the suspect was the most effective way to reduce further violence.  Based on these result, police departments were encouraged to arrest suspects as soon as possible in domestic assault cases.  In addition, that National Institute of Justice funded six replications of the Minneapolis experiment to take place across the United States.

The authors took results from the initial Minneapolis study as well as the following six studies, applied Bayesian Analysis, and attempted to determine if there was an applicable theory; labeling theory or social control theory.  The authors took a combination of a Bayesian Analysis and meta-analysis to attempt to replicate the original study as well as the results that came with that. The subsequent studies were used as different levels of the Bayesian Analysis.

The findings of the this analysis determined that there was no generalizable approach to effectively reducing further violence in domestic assault incidents.  Berk, et. al. determined that there were "good" and "bad" risks, and the different positions and relations individuals held in society determined the effectiveness of arrests.  Individuals who did not feel as constrained by their social standing, or not constrained by social controls are seen to be "bad' risks -- they are likely to repeatedly offend, since they are not as deterred.

This study concluded that social control elements, such as familial ties, relationships, and public perception, are only indicators, not actual measures of attachment.  Therefore, there is no generalizable finding that is applicable to offenders across the United States, or even to offenders in the same region, just over time.  Therefore, there is no statement overall that is applicable to offenders or one that applies specifically to site's past, present, and future offenders.

Critique:

The application of the Bayesian Analysis was interesting since it not only looked at a statisical element, but it also included a meta-analysis to attempt to understand a method that is most effective at curbing domestic violence.

The study did note that the detailed steps for the Bayesian Analysis were located in another document, which made it slightly difficult to understand the larger picture, including the specific elements that went into the analysis.  Overall findings from the analysis are presented, and analyzed in a manner that is coherent to individuals outside of the field.  That being said, it would have been beneficial to include a more detailed element in this study depicting the numerical application of Bayesian analysis rather than the written element.

Berk, R., Campbell, A., Klap, R., & Western, B. (1992). The deterrent effect of arrests in incidents of domestic violence: A Bayesian Analysis of four field experiments. American Sociological Review, 57(5), 698-708. Retrieved from http://www.jstor.org/stable/10.2307/2095923

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