## Thursday, March 6, 2014

### An Intel-Dance(!) Joint Operation? You Betcha... (Entrepreneurial Intelligence)

No kidding.

This is a real thing.

 http://kck.st/1gexKzM

When Mercyhurst Dance professor, Noelle Partusch, came to me in the fall to ask how the intel studies program could help her with a trip she and her dancers wanted to take to Jerusalem, little did I imagine that I would find myself smack in the middle of the Journey to Jerusalem Kickstarter Campaign.

Kickstarter is a highly popular (over a billion dollars collected so far...) crowdfunding platform.  People fund projects from art to books to games to, well, dance.  In fact, dance is one of the most statistically successful of the various Kickstarter categories with over a 70% success rate.

I have run two successful campaigns (For my games, Widget and CVTV) and so it made sense for Noelle to ask me for advice.  As I began to think about it, however, I saw a real opportunity to explore - in a more tangible way - some of my ideas about entrepreneurial intelligence and to give students a unique learning experience in the process.

Pretty soon I had a team of students working the details of the campaign.  If intelligence is, in some way, about the things that are critical to your success or failure but are fundamentally outside your control, then Kickstarter success or failure is all about intelligence.

I started with Emily Francis, one of our top undergrad intel students.  Emily is known for her intelligence, drive, and ability to work with others.  Kickstarter, with its focus on short (30 days is recommended) win-or-lose (you either meet your goal or you get nothing) campaigns, is a perfect place to start to hone leadership skills.

I let Emily pick her own team and she quickly hired a copywriter, Alison Hosko, to help craft the marketing message and a social media manager, Amanda Marley, to help build an audience and get the word out.  We went to the Graphics Design Club here to get the logo.  Noelle, the other professors in the department, and the dancers themselves provided us with all the content and approved all the messages.  After building up an audience on Facebook, we launched on 18 Feb.

We are now a day past the midway point and, with the help of nearly 50 backers, over 100% funded.   Kickstarter allows projects to become overfunded, so we are going to try to push the dollar amount as high as we can knowing that every dollar the project makes goes to help the dancers.

Assuming we stay above our goal (dirty little secret:  In crowdfunding, people can cancel their pledges, too...), this is a big win for:
• ...the dancers.  Every dollar will go to defray their costs.
• ...the backers - the people who pledged the dollars.  They not only get the excitement of helping these dancers achieve their dream, they also get tangible rewards, such as an exclusive video of the dancer's performance while in Jerusalem.
• ...for the University (a double win to be exact).
• We have an excellent dance department here at Mercyhurst.  Efforts like this spread the word about that department and about the University in general in ways that traditional marketing can't match (for example, take a look at this article or this one...).
• In addition, our current University marketing campaign is built around the slogan, "Experienced Guaranteed." We do a lot of hands on, project based work in our classes here at Mercyhurst (particularly in the intel department).  This project makes that work interdisciplinary.
• ...for the students, who not only get the experience of working on a project like this but also get a (modest) stipend for doing so.
• ...for the community of Erie and rust-belt towns generally.  The rust belt has been the hub of heavy manufacturing in the US for so long that many places lack a body of what I call "pre-entrepreneurs" - people who could be entrepreneurs but haven't thought about it yet.  The environment for entrepreneurs is pretty good but there simply aren't enough people taking advantage of it because they don't think they can.  Project based crowdfunding sites, like Kickstarter, help overcome what is, in reality, a psychological problem.
• ...for entrepreneurial intelligence as a field of study.  Crowdfunding activities like this one provide a perfect laboratory for determining what entrepreneurs need from intelligence.  I have already learned a bunch of lessons and expect to learn more.
We have received good press so far (here and here) and almost 350 "likes" on Facebook.  It has all helped but if you like dance or are just interested in seeing how this project proceeds, drop the project a couple of bucks or share it with someone who will!

## Tuesday, March 4, 2014

### The Mind's Lie AKA "Biases With Friends" (Free Beta App)

BLUF:  The Mind's Lie is a free Android gaming app on the Google Play Store available now.  It is similar to games such as Words With Friends in that you are playing against real people and not against the machine.

The game is designed to implicitly teach you and the other players (up to six players per game) to recognize confirmation bias, anchoring bias, stereotyping/representativeness bias, projection/mirror imaging bias, bias blind spot, or fundamental attribution error in more or less realistic situations. It is based off a successful tabletop game I designed.

Background:  A few years ago, I was inspired by IARPA's SIRIUS program (which seeks to develop a video game which will teach analysts to recognize and mitigate the effect of the six specific cognitive biases listed above) to try to come up with my own game that would do at least some of the same things.

I don't know how to design video games, though, so I did what I could do - design a tabletop game.  Called The Mind's Lie, it uses an argumentation mechanic to implicitly teach players how to recognize variants of the same six biases that IARPA is testing in the SIRIUS Project.

Eventually, through some good fortune, I did get to be involved in SIRIUS as a part of a team that Boeing put together.  Mel Richey, who worked with me on that Boeing team, eventually tested The Mind's Lie using people from all over the US and showed that it seems to work - the more you play it, the better you get at identifying the presence of the six biases in more-or-less realistic scenarios.

Since then, we have been using The Mind's Lie in a series of workshops and in class.  I have been encouraged by the fact that it seems to work best with people who understand that bias is a persistent risk in their day-to-day work - people like lawyers, soldiers and, yes, intelligence analysts.

At about the same time, I was asked to submit an idea for a senior project to the software engineers at Penn State (the Behrend Campus).  I have done this in the past and we had explored the possibilities of a balloon based surveillance system and a Bayesian calculator for analysts (among other ideas) together.

What I wanted this time, though, was to turn The Mind's Lie into a Words With Friends-type game.  I wanted people to be able to re-create the experience of playing The Mind's Lie around a table while on the go.  The engineers, Steve Chalker, Joe Grise and Kit Torelli, along with their professor, Dr. Matt White, decided to turn my game into an android app.

Nearly a year later, the app is here.  It is not perfect - it's a beta version (at best), but it is out there and free to download and play.  Hope you like it!

## Monday, March 3, 2014

### What The Syrians Had To Say: Views from Istanbul (Part 5 Of A Multi-Part Series)

First of all, thank you to those who have stuck with us through this lengthy series on network analysis and Turkey. This will be the final installment, though the projects are still ongoing and the techniques still being refined and applied.

Below are the stories from Syrians that have fled the country, collected by the author in January 2014.

For those who missed the first four pieces, they can be found here: My Conversation With The Free Syrian Army, Ethnolinguistic Mapping, Gravity Model Analysis, and Population Simulations.

All the methodology involved in this lengthy project was recently presented at the International Network For Social Network Analysis Sunbelt Conference, and very well received (a big thanks to a very special CENTCOM Sunbelter who made the presentation and the trip to Turkey all possible).

The methods led to very intriguing conclusions, some of which were confirmed and some of which were challenged by the Syrian refugee population in and around Istanbul, Turkey. In the suburbs of Istanbul, I spoke with many refugees to gain perspective on everything from their geographic movement to their political opinions, and what they had to say was equally as heartbreaking as it was informative.

A woman held a one year, seven-month old child in her arms as she spoke to me in Arabic. "She is a living marker of the revolution," she told me. "She was just one month old when we left Syria; her life has only seen the war."

 Children playing outside for recess at a Syrian day school in Bayramtepe

A single mother of three, this woman had been living in an apartment in Fatih district of Istanbul since early 2013. The last time she had heard that her husband was alive after being incarcerated in Aleppo was March 2013.

"We agreed before we left Syria," she said, "that if either one of us was killed or put in jail, the other one would continue to Istanbul."

"Do you intend to stay in Istanbul?," I asked.

She replied that she could not leave. She hopes every day that her husband is alive, and if he is and escapes, he will only know to find her here. "I send money back to Aleppo for people to pay to the prison guards to ask about my husband. They take the money, but I never hear anything back."

She was a woman of higher socioeconomic status, obvious from her black silk hijab. Many people living in Istanbul, namely the district of Fatih, are of a higher socioeconomic standing than those out in the suburbs, like Bayramtepe.

Of all the people I interviewed, a total of 22 families and 152 individuals (about half of them children), there was a clear difference between groups of both lower and higher socioeconomic statuses. Those of the former group responded that they hoped to return to Syria as soon as possible. When asked what their ideal outcome would be, they replied that who would be in power in Syria did not matter, only that it was safe enough to return.

Those of a higher socioeconomic status indicated that they did not intend to return to Syria. They were bitter and angry.

"Why would I go back?," one man shouted at me. "I have lost everything! There is nothing there for me and my family!"

"What would you like to see happen in Syria, politically," I pressed, despite his challenging tone.

"Assad has treated the Syrian people like animals," he replied. He spoke of the need for international intervention. He expressed his disbelief at the U.S. government's lack of initiative in the region. "And this Geneva conference..." he continued, "they are just playing their game." He spit these last words at me before taking a sip of the Turkish tea his son's wife had made for us on arrival.

This interaction was representative of the class divide among those with whom I spoke.

The following day I walked through Bayramtepe, sun setting quickly around 1600 behind rolling hills littered with cinder blocks and the occasional haggard donkey. Crawling out of the muddy streets and up the cement stairs to a shack on stilts, I was overwhelmed with the smell of garlic and cooked greens as the door opened. A chorus of Salaam Alaikum filled the entryway as I pried my muddy boots off my feet for about the 20th time that day.

It was a single room with a drafty roof, but it was cozy, with a pot in the center that contained simmering green leaves. Many children, too many to count, ran in and out of the house as a woman, upwards of 95, tottered around yelling at them to keep quiet for the guests. She brought out an apple on a tray for the seven of us to share.

"We walked," she told me, "first from Damascus, then to Raqqa where we crossed the Turkish border at Tel-Abyad."

She mentioned walking all that way with all the children (more than 10) and her mother, who was ill (the 95-year old woman who was surprisingly lively and cheerful, excited to have company).

"Also," she pressed on after she had contained her tears enough to continue, "when we crossed the border, the Turkish police caught us on the other side. None of us had passports, so they returned us back to Syria. We had to wait for them to leave before sneaking across the border the following night."

This was a common story I heard from the refugees. Almost none had passports or citizenship papers. Many were captured and returned to Syria, but then sneaked over the border later.

"My life was saved by an American," a man later told me.

This was intriguing.

"I was captured near Aleppo by the Syrian government, and put in jail with my family. We stayed there for a few days until another man was put in jail with us. An American, I think. He spoke only in English. Later that day, I heard the military talking and the captured man was someone of great importance, so the Syrians took us all, my family and the man, and left us out in the middle of the country near Aleppo. They just drove away. I was lucky."

Thoughts of who this man might have been flashed through my mind. A journalist, perhaps? An international peacekeeper? Someone the Syrians did not want to be responsible for incarcerating.
 Bayramtepe, Turkey

The refugees were from Aleppo, Raqqa, Homs, Idlib and Damascus. They had come to Istanbul looking for work, some by way of Cairo. It was encouraging to hear that those that had crossed the border between Syria and Turkey had all crossed at the precise locations used in my simulations.

I also learned that most traveled on foot through Syria and then by bus from Turkish border towns to Istanbul. The factors in my simulation reflected railways and roadways. In retrospect, railways were unnecessary, where roadways were important, but so pervasive throughout such a developed country that the factor hardly would have made a difference. It is through these valuable insights that I can now alter the factors in the simulation to better represent the region hosting the population and the characteristics of the population itself.

A final surprise from Istanbul was the groups of people that I encountered. I expected to find Shia Kurds, Christians and Alawites, all minorities in the Sunni Muslim refugee camps near the border. What I found were both Sunni and Alevi (Shia) Kurds, some Sunni Muslims and no Christians. Where were the Christians?

Granted, I had assumed they would be in Istanbul by process of elimination. They did not go to Jordan, Lebanon or Iraq, but preferred Turkey's more secular environment. They were not in the refugee camps near the border, though, so I assumed they must be among those migrating to Istanbul. After speaking with those who knew the entire Syrian population in Istanbul and its suburbs I determined there were no Christians in the area. Where were the Christians? News articles answered the question for me once I got home.

In speaking with a man named Azzad, the leader of a private Syrian refugee organization in Bayramtepe that survives on donations from Turkish citizens, he indicated that he had registered 2,500 families, upwards of 12,000 people, with 5 - 10 new families arriving daily. He said that no one leaves and everyone needs help. The majority of the refugees are Kurds.

A man named Muffa who runs the same kind of organization in Fatih district of Istanbul indicated he had 5,500 families in the city with 100 new families registering every month.

"If I had to guess," he told me, "about 20 percent of the people in Fatih are Kurdish."

These are just some of the many stories from the refugees that I spoke with representing the ways in which they challenged and confirmed my analytic predictions.

While I continue to follow the crisis in Syria, my current research involves the application of the methods presented in this multi-part series to central Africa. The utility of the methodology developed is its flexibility, but different regions and refugee groups require a comprehensive re-evaluation of the factors influencing the simulation.

With luck, look for the information on Central Africa to surface sometime early summer!

## Monday, February 24, 2014

### Syrian Refugee Population Simulation: From *ORA to Istanbul (Part 4 Of A Multi-Part Series)

Note: For those who haven't been keeping up with the most recent posts, this is part 4 of a fairly extensive series on network analysis techniques and the human geography of Turkey. In case you missed them, the first three posts can be found here: Part 1 (The Free Syrian Army), Part 2 (Ethnolinguistics of Turkey), Part 3 (Borrowing Analytic Techniques: Populations, Predictions And What Physics Tells Us About The Movement Of Alawites).

 Figure 1. Simulating Population Movement Source: CASOS *ORA Software
No competent analyst today can brief the situation in Turkey without at least mentioning the now 1 million refugees flowing across the porous border with Syria.

My ethnolinguistic project was no different.  I quickly realized that I would have to do far more than "at least mention" the current crisis in Syria.  In fact, it quickly became the focal point of the investigation.

Conceptually, the problem was simple.

How to turn Turkey, a 1000 mile-long country with 81 provinces and 957 districts, into a network. Why did I want to make it a network? So I could use it as an architecture through which I could simulate refugee population flow.

And the answer is going to surprise you (I know it surprised me).

If you are a tabletop wargamer, you will likely already have an answer to this. Ever played Showdown? Then you know what I'm talking about.

For those unfamiliar with classic tabletop wargames, they tend to overlay hexagonal grids onto geographic landscapes in order to create a game board. Showdown turns the India-Pakistan border, for example, into a hexagonal grid system on which players explore the possibilities surrounding a hypothetical, near-future, nuclear Indo-Pakistani war.

Intelligence analysts also have a way of assessing terrain of strategic importance within the context of war: The well-known analytic method Intelligence Preparation of the Battlespace. With this method, intelligence analysts look at everything from climate to elevation to road/rail infrastructure to land cover/use in order to determine the most strategic plan of attack or invasion.

Finally, brilliant scientists are using a methodology similar to mine in South Africa to predict elephant migratory patterns and calling it "resistance mapping." [1]

I, however, wanted to take a more organic approach to building the network architecture. And what better way to do so than to use the pre-existing administrative boundaries of Turkey? (A particularly ironic decision given that the original goal of the project was to replace all administrative boundaries with ethnolinguistic ones).

This is when the 957 districts of Turkey because 957 nodes in a network, and the two to seven district border crossings for each of these 957 districts became a link in the network. It looked something like Figure 2.

 Figure 2. Derived Turkey Network Source: CASOS *ORA Software and ArcGIS

Now that the nodes and links were established, there was the issue of link weights (if all links were weighted equally, the simulation would spread through the network equally in all directions, and that would be rather useless and not very predictive).

The link weights were derived for each district border crossing from six factors on a scale of one to three. The factors were a combination of the ethnolinguistic mapping, the gravity model analysis and IPB factors:

1. Terrain

3. Railways

4. Expanding, neutral or decreasing ethnolinguistic group

5. Pre-existing Syrian refugee presence

6. Gravity model output (influence on area from abroad in the next 12 - 24 months)

A higher link weight was indicative of increased difficulty a refugee would experience in moving from one district to the next. A lower link weight indicated that the route would be easier to navigate for reasons reflected in the factors above. [2]

The simulation began in the five nodes representative of the seven most frequently-trafficked border crossings as determined by the UN. [3] It iterated 19 times and moved through Turkey revealing some encouraging findings along the way (See Figure 3).

 Figure 3. Simulated Syrian Refugee Population Movement Prediction Over 19 Weeks Source: CASOS *ORA SOftware

 Figure 4. Known Syrian Refugee Presence in Turkey Source: UNHCR (November 2013)

 Figure 5. Known locations of Syrian refugees. Size of dot indicates number of camp inhabitants. Source: UNHCR (November 2013)
An important element in validating the simulation is analyzing the degree to which it tracks with reality. The simulation ran directly from the border districts between Turkey and Syria and tracked with the known locations of Syrian refugees (See Figures 4 & 5).  When the image of the known locations is overlaid with the image of the simulation it looks like this:

 Figure 6. Simulation Overlay on Known Syrian Refugee Presence
The two provinces outlined in white (Nigde and Aksaray) are where the Turkish government subsequently indicated that it would construct new refugee camps to relieve camps operating at capacity within the region. More confirmatory evidence.

Now fast forward to two months later. The date is 18 February and, according to the UNHCR, the refugee population has moved further into the provinces indicated in Figure 7. Reality is unfolding within the lines of the simulation from which analytic predictions were drawn back in December 2013.

 Figure 7. Starred Provinces (Left to Right): Konya, Karaman, Mersin, Batman (Yes, that is correct, there is a Turkish province called Batman)
All of this, from methods and process to final product analysis, led me to Istanbul (or just outside, as it were) where stories from Syrian refugees both challenged and confirmed my predictions.

Be sure to check out the next installment, What The Syrians Had To Say: Views from Istanbul.

***

[1] The article, Computational Tools in Predicting and Assessing Forced Migration, employs much the same methodology as my approach, but utilizes an artificial hexagonal grid as opposed to the more organic network architecture that I used. It is an excellent read!

Edwards, S. (2008). Computational tools in predicting and assessing forced migration. Journal of Refugee Studies21(3), 347-359.

[2] These numbers were later inverted for the purposes of the simulation as, in a network, a higher link weight value indicates a stronger relationship. The numbers were inverted so that, the lower the link weight, the more difficult it would be for a refugee to move there and the less likely the simulation would be to move into that region as a result.

[3] Five nodes represented seven border crossings because each node represented one district. Two of the seven most frequently-trafficked border crossings were located in the same district as another border crossing, therefore only five nodes were necessary.

## Tuesday, February 11, 2014

### Borrowing Analytic Techniques: Populations, Predictions And What Physics Tells Us About The Movement Of Alawites (Part 3 Of A Multi-Part Series)

Note: This is the third part in a multi-part series covering an extensive project on the human geography of Turkey and network analysis applications within the field of Intelligence Studies. Be sure to read the first two posts, My Conversation With The Free Syrian Army and Turkey Redrawn.

 World Migration into Turkey Source: International Organization for Migration Map
In the past, SAM has covered many different Structured Analytic Techniques (SATs), the zooming technique and Structured Role Playing among them. This post covers an analytic technique somewhat unfamiliar to the intelligence discipline, but extremely useful, especially within the realm of human geography and population movement.

Using this technique, I was able to make predictions that read something like this:

"The ethnolinguistic populations within Turkey that will likely expand within the next 12 to 24 months are Bulgarians, Serbs, Iraqi Kurds, Iranian Arabs and Azerbaijanis."
and...
"The groups that are highly likely to impact Turkey within the next 12 to 24 months are Syrians and Kurds (from Syria, Iraq and Iran)."
Where did these predictions come from?

Gravity models.

I know what you're thinking, and yes, gravity models do have to do with gravity and yes, gravity models do happily reside within the domain of physics.

Now, before you close the page, I am not going to spend the next four hundred or so words discussing Newton's Law of Universal Gravitation. Instead, on a one-stop flight from physics to intel, we will have a brief layover in the discipline of economics.

I said, don't close the page!

Back in the 1950s, economists began to import gravity models into economics to predict trade flows and it looked something like this:

$F_{ij} = G \frac{M_i M_j}{D_{ij}}$

Where F is the flow of bilateral trade between country Mi and country Mj (measured in economic size). D is the distance between these two countries in kilometers squared and G is a constant. If it seems familiar, it should.  It is Newton's Law with the names of the variables changed.

In the 1970s, a man by the name of Peter Trudgill, a well-respected British sociolinguist, brought the gravity models to dialect geography. Trudgill, whose most recent book was published in 2011, holds many firsts within the field of linguistics, of which gravity models are one. He enacted a series of alterations to the gravity model in order to account for linguistic factors, the end goal being to create gradient zones of gradual shift from one phonetic marker to the next within geographic space (like geospatially representing the line in and around Boston that marks where people stop saying cah and start saying car). Trudgill's gravity model looked like this:

Trudgill's modifications to the original gravity model accomplish two goals:
1. By adding the s variable measured on a scale of one to four, he factors in the well-known assumption in sociolinguistics that contact occurs more readily between groups that have "prior-existing linguistic similarity." This means that if the languages spoken by Population i and Population j are mutually intelligible (such as American English and British English), the s variable would be higher than for, say, American English and Italian).
2. By adding the second half of the equation, he gives the model directionality. The original gravity model was intended to predict bilateral trade, meaning it would predict the economic contact between one country and another. Trudgill's model predicts the influence country Pi will have on country Pj as opposed to the bilateral contact country Pand country Pj  will have.
Now, what does all this have to do with intel?

Within the context of my most recent project, mapping the Syrian refugee population in Turkey, gravity models provided a way to analyze the ethnolinguistic landscape in Turkey through a predictive lens. Instead of saying, "Here are where Armenians live in Turkey," I can say, "Here are where Armenians live and this is how likely there are to be more Armenians traveling to this region from Armenia in the next 24 months."

In other words, it took the ethnolinguistic mapping project from descriptive to predictive

In order to achieve this, I added one more variable to Trudgill's altered gravity model: the c variable.

The c variable is a variable assessed on a scale of one to four that takes into account the trend line of migratory patterns from target ethnolinguistic groups over the past 10 years (data taken from the International Organization for Migration). In other words, to the degree that future migratory patterns follow historical trends for each ethnolinguistic group, the c variable takes these trends into consideration.

What results from these models is an index score (usually a decimal) that is indicative of how likely or unlikely contact is to occur (or, in the case of some of the most advanced equations, how likely one region is to influence another region). This has the potential to  make a compelling predictive argument when translated onto a map (See Figure 1).
 Figure 1. Results of gravity model analysis represented geospatially. Green areas: Ethnolinguistic areas that are highly likely to expand in the next 12 to 24 months Black areas: Ethnolinguistic areas that are likely to expand in the next 12 to 24 months Click here for a map of all the ethnolinguistic areas of Turkey
These models have widespread application for predicting contact between any kinds of populations, but arguably the circumstance in which gravity models work best are when a generally homogeneous geographic region hosts pockets of ethnic diversity. Such is the case with Turkey, therefore gravity models provided a solid predictive approach.

Gravity models were the backbone of my ethnolinguistic predictions regarding Turkey. It is the output of these models that got me to thinking about the incoming Syrian refugee population which, at the time, numbered close to 700,000 and today likely exceeds 1 million.

With that in mind, don't miss the next post (by far the most interesting): Syrian Refugee Population Simulation: From *ORA to Istanbul.