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OECD Life Satisfaction Index – What makes people happy?

Friday, May 25th, 2012

Recently, OECD published its life satisfaction index across its member countries. How’s Life?: Measuring Well Being gives an overview of the OECD Better Life Index methodology, which is focused on households and individuals rather than aggregate economic conditions, and on well-being outcomes as opposed to well-being drivers. In particular, life satisfaction measures how people rate their general satisfaction with life on a scale from 0 to 10. The surveys show that Hungary, Portugal, Russia, Turkey and Greece have a relatively low level of overall life satisfaction while  Denmark, Norway, the Netherlands and Switzerland have a high level of overall life satisfaction.

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In addition to life satisfaction, OECD survey also captures various indicators under the topics of housing, income, jobs, community, education, environment, civic engagement, health, safety, and work-life balance. For instance, the housing topic includes the rooms per person (average number of rooms shared by person in a dwelling), dwellings with basic facilities (percentage of people with indoor flushing toilets), and housing expenditure (housing expenditure as a percentage of disposable household income) indicators. In contrast, the jobs topic includes the employment rate (percentage of people currently employed in a paid job), long term unemployment rate (percentage of unemployed people who have been actively looking for a job for over year), personal earnings (average annual earnings for a full-time employee), and job security (share of employment with a tenure less than 6 months) indicators.
201205242205.jpg The chart on the left shows the indicator values for Hungary. In general, Hungarians are less satisfied with their lives than the OECD average. 65% of Hungarians have more positive experiences (feelings of rest, pride in accomplishment, enjoyment, etc) than negative ones (pain, worry, sadness, boredom, etc) on a daily basis in contrast to the OECD average of 72%.

As the chart shows Hungarians feel safe, are happy with the water and air quality, find their work-life balance acceptable, have a strong sense of community, and are happy with the general quality of the education. For instance, 89% of Hungarians believe that they know someone they could rely on in time of need. Yet in life satisfaction, Hungary scores the lowest.

Referring back to the chart, the scores for housing, jobs, civic engagement, and health for Hungary are lower than the OECD average while income is considerably lower than the OECD average. In Hungary, the average person earns about $13K a year, less than the OECD average of $22K a year. It seems like the low life satisfaction score for Hungary is connected to low living standards stemming from sub-par income levels coupled with a lack of jobs. For instance,  around 55% of Hungarians aged 15 to 64 have a paid job, well below the OECD employment average of 66%.

In contrast, sense of community is the lowest score for Turkey. For instance, 69% of Turks believe that they know someone they could rely on in time of need, lower than the OECD average of 91%. Is there a correlation between life satisfaction and indicators for living conditions and quality of life? If yes, does the correlation hold across the OECD countries?

201205242219.jpgThe OECD Web site has a mixer tool that lets a user to select the relative rankings of the indicators and analyze the ranked list of countries based on these preferences. The customized index enables the comparison of well-being across countries based on personal preference of the importance of 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life.

While the OECD mixer is a nice tool for engaging readers, as a modeler, we see the life satisfaction sentiment indicator as an output while the rest of the indicators (housing, income, jobs, etc.) as inputs. In other words, we believe that the input indicators drive people how people feel about their life experiences. To test this hypothesis, we performed a correlation analysis between the life satisfaction index and the rest of the indicators in order to understand which factors contribute the most or the least to the life satisfaction sentiment. The correlation analysis is shown on the right.

In terms of topics, life satisfaction has high correlation with income, jobs, housing, health, low correlation with education and no correlation with safety. In terms of individual indicators, room per person has the highest correlation with life satisfaction while job security, housing expenditure, employees working long hours, educational attainment, years in education, student skills, consultation on rule making, air pollution, homicide and assault rate indicators have very low correlation with life satisfaction.

The indicators under each topic show some interesting results. For the jobs topic, while employment rate, personal earnings, long term unemployment rate indicators are correlated with life satisfaction whereas job security is not. Similarly, for the environment topic, while water quality has a high correlation with life satisfaction, air pollution does not.

It would be interesting of comparison if there was a similar survey for non-OECD countries. Perhaps the OECD country values are dominated by the population’s desire for the ability to collect as many material possessions as possible. Relatively poorer country values may not follow this correlation.

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Countries with Increased Political Violence Forecast: 2011 – 2015

Saturday, December 31st, 2011

In our companion Domestic Political Violence Model blog, we published yesterday the list of countries predicted to have increases in political violence for 2011 to 2015. The map below shows the countries with expected increase in political violence grouped by Very High Risk, High Risk, and Medium Risk. Our forecast is based on four different models. In the Very High Risk category, all four models predicted an increase. In the High Risk category, three models predicted an increase. In the Medium Risk category, half of the models predict an increase in violence. The countries in each category are sorted based on the size of the mean residual, so the states with the most pent-up demand for violence are listed at the top. The residuals imply that these are states that we expect to observe increases in violence although not necessarily high levels of violence. So United Kingdom and Israel are not expected to have the same level of violence but are expected to have the same magnitude increase in political violence.

201112311034.jpg

United Kingdom, Israel, Sri Lanka, Iran, Colombia, Zimbabwe, South Africa, Haiti, Egypt, Philippines, Guinea-Bissau, Venezuela, Chile, Syria, Chad, Belarus, Guinea, Kyrgyzstan, Greece make up the very high risk list. Israel, Sri Lanka, Iran, Colombia, South Africa, Egypt, Chile, Syria, Chad, Belarus, and Kyrgyzstan are returning countries from our 2010-2014 forecast. Of our 2010-2014 forecast, Syria, Egypt, and Libya saw the most violent protest in the Arab Spring of 2011. United Kingdom, Zimbabwe, Haiti, Philippines, Guinea-Bissau, Venezuela, Guinea, and Greece are the new additions to our very high risk list. United Kingdom tops the list as the pent-up demand for increased violence was certainly evident in the London Riots over the Summer of 2011. Greece saw substantial increase in political violence due to the measures introduced by the Greek government to address the debt crisis.

It is worth noting that our 2011-2015 forecast model is based on events dataset which captures both the frequency and the intensity of political violence from 1990 to 2010. Similarly, our 2010-2014 forecast model is based on events dataset which captures both the frequency and the intensity of political violence from 1990 to 2009. We publish our forecast based on our acquisition date of the event dataset. As the event dataset is available on a real-time basis – albeit at a higher cost, we can publish our forecast in real-time if needed.

Using a regression model applied to a large number of drivers of conflict variables spanning numerous open source social science datasets, our model uses a novel Negative Residuals technique. Negative Residuals result from the model predicting higher levels of violence than actually experienced, indicating nation states that are pre-disposed to increasing levels of violence based on the presence of environmental conditions and drivers of conflict with demonstrated correlation with measured political violence. In our model, the magnitude of future political violence directed towards the state is heightened by coercion, often thought of as violations of physical integrity rights, and by coordination, or the tools by which groups can associate and organize against the state. Conversely, the magnitude of political violence is lessened by capacity, defined as the ability of the state to project itself throughout its territory.

For the event dataset, we use the Integrated Data for Event Analysis (IDEA) framework. IDEA event dataset is based on the Reuters Global News Service, and organized in a “who” did “what” to “whom” manner for each particular event. This framework allows researchers to isolate events of interest for their particular project. Using this framework allows us to capture and isolate domestic anti-government violence. For the dependent variable, our model uses the Goldstein scores that captures the overall level and intensity of domestic antigovernment violence within a state in a given year.

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Wiki Surveys for Social Science Research

Friday, June 10th, 2011

Surveys and interviews form the central methodology for analyzing and discovering attitudes and opinions in social science research. With the advent of Web, online surveys have become an efficient way for researchers to collect and analyze large amounts of data. The popularity of the online survey tools like SurveyMonkey , Zoomerang, SurveyGizmo , etc. are testament to the productivity enabled by surveys. However, surveys represent a top-down rigid methodology forcing the survey designer to account for all possible answers up front, which is an impossible feat. In contrast, interviews allow the unanticipated information to bubble up bottoms up from the respondents. For instance, Integrity Watch Afghanistan (IWA), Afghan Perceptions and Experiences with Corruption: A National Survey 2010 primary data, involves interviewing randomly selected 6,500 respondents in 32 provinces on over 100 questions that deal with sectors where people experienced corruption; levels of bribes people paid to obtain services; what type of access people had to essential services; who people trusted to combat corruption; and experiences with corruption in the judiciary, police, and land management. However, the interview methodology is expensive and time-consuming as it requires implementation by research companies with expertise in effective research design, and precise management of data collection over several months.

Is there an alternative to surveys and interviews in social science research? Prof. Salganik’s team at Princeton came up with a hybrid approach, “wiki surveys”, that combines the structure of a survey with the open-endedness of an interview. To date, various organizations have created more than 1000 wiki surveys on the project Web site – All Our Ideas, generating in 45,000 ideas with 2 million votes. Wiki surveys range from the New York City Mayor’s Office’s engagement with citizens in shaping the city’s long term sustainability plan to the Catholic Relief Services surveying their 4000 employees to find out what makes an ideal relief worker. The figure below shows how the third question in Tactical Conflict Assessment Planning Framework (TCAPF) would be be implemented as a wiki survey:

tcapf wiki survey.jpg

Inspired by extending the kittenwar concept to ideas, the user interface guides the respondent to choose between two random alternatives, while encouraging the respondents to add their ideas into the mix of alternative responses. The additional ideas are added into the survey’s marketplace and voted up or down by the other survey-takers. Prof. Salganik says that “One of the patterns we see consistently is that ideas that are uploaded by users sometimes score better than the best ideas that started it off. Because no matter how hard you try, there are just ideas out there that you don’t know.”

All Our ideas have some basic visualization features to make sense of the wiki survey responses. Here is the visualization for the responses – “What do you think the Digital Public Library of America (DPLA) should be like?”:

DPLA Survey Reponse.jpg

It is worth noting that the top scoring 15 ideas starting with DPLA interoperability with Government Printing Office (GPO), Defense Technical Information Center (DTIC), an National Records Archive Administration (NARA) are all uploaded ideas not in the original set of alternatives. A powerful argument for crowd sourcing!

Admittedly, we still need boots on the ground to collect TCAPF data in Afghanistan given the demographics of the people we want to reach. On the other hand, wiki surveys hold great potential in reaching the younger generation fueling the Arab spring and the like.

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Bin Laden Hideout vs. Al Qaeda Training Manual

Sunday, May 8th, 2011

In our Building Intent project, we developed a geoprofiling algorithm that predicts the location of facilities that support adversary operations in the urban environment. Geoprofiling is a technique that is widely used in serial crime investigations. In our project, we researched and developed a building intent inference system based on terrorist preferences, building characteristics, and social network behavior. Our approach learns the utility function that the adversaries are using, and classifies and predicts the potential utility of a facility to the adversaries based on the derived metadata of each facility using influence networks.

For terrorist preferences, we have studied Military Studies in the Jihad Against the Tyrants: The Al-Qaeda Training Manual in order to find building use tactics that the adversary is training its recruits, and found a significant number of building use related tactics and procedures embodied in these manuals. In collaboration with the Terrorism Research Center in Fulbright College, University of Arkansas, we then studied the international terrorism cases in the American Terrorism Study, and found empirical evidence that shows the practice of terrorism manual tactics in the observed data. Based on these findings, we developed a baseline set of indicators for modeling building intent, and researched the likely causal connections among these variables. We then built extractors to derive a set of metadata for these indicators, and used machine learning algorithms to find the causal connections between the incidents or events and building attributes, and model parameters, and build classifiers based terrorist process preferences , building characteristics, and guilt by association data.

As shown in the figure below, our geoprofiling algorithm does a nice job in predicting the Japanese Red Army terrorist Yu Kikumura’s residence in New York based on the American Terrorism study. Here the blue markers signify police stations and white arrows signify the egress points. As shown in the figure, Yu Kikumura’s residence at 327 East 34th Street, NY is in the red hotspot area predicted by our algorithm. Avoiding police stations and ease of egress were two of the primary factors in Kikumura’s choice of housing. Not only is his apartment equidistant from the nearest police departments – all of which are over one kilometer away – it’s back-alley access road to the underground Queens Memorial Tunnel provides a quick get-away by car. In addition, the examination of the residence floor plan reveals that the apartment building had numerous staircases (one of which is private to the unit) to the basement level with a rear exit.

Japanese Red Army.png

The Al Qaeda Training Manual gives several instructions for renting a residence as shown in the table below. For instance, it is preferable to rent apartments on the first floor for ease of egress, avoid apartments near police stations and government buildings, and in isolated or deserted locations, rent in newly developed areas, and the like. In particular, the Al Qaeda Training Manual calls for the use if the following tactics in renting an apartment:

Al Qaeda Tactics.jpg

So how does the location of Bin Laden’s secret hideout in in Abbottabad follow the advice of the Al Qaeda Training Manual? Not that closely. Bin Laden clearly did not follow the tactics for selecting a ground floor location by living on the third floor, for avoiding police stations and government buildings by selecting a location near the Pakistan Military Academy, for finding an apartment in newly developed areas where people do not know each other by choosing a neighborhood with retired Army Generals, and for preparing ways of vacating the premises in case of a surprise attack by not building exit stairs. The only tactic that Bin Laden has used from the list above is avoiding an isolated location. One wonders if Bin Laden made a concerted effort to avoid his own tactical advice in order to thwart geoprofiling techniques. Perhaps another consideration that will need to be taken into account in future geoprofiling is the assistance from outside forces, given the possible connection to a support network that included elements of the Pakistani military or intelligence services in the Abottabad area.

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Increased political violence in store for Italy and Czech Republic?

Monday, November 29th, 2010

In collaboration with our academic partners Prof. Cingranelli at the Political Science Department, SUNY Binghamton University and Profs. Sam Bell and Amanda Murdie at the Department of Political Science, Kansas State University, we developed a Domestic Political Violence Model that forecasts political violence levels five years into the future. The model enables policymakers, particularly in the COCOMs, to proactively plan for instances of increased domestic political violence, with implications for resource allocation and intelligence asset assignment. Our model uses the IDEA dataset for political event coding, plus numerous indicators from the CIRI Human Rights Dataset, Polity IV Dataset, World Bank, OECD, Correlates of War project, and Fearon and Laitin datasets. Here is our model’s forecast for 2010 – 2014 as a ranked list:

  1. Iran
  2. Sri Lanka
  3. Russia
  4. Georgia
  5. Israel
  6. Turkey
  7. Burundi
  8. Chad
  9. Honduras
  10. Czech Republic
  11. China
  12. Italy
  13. Colombia
  14. Ukraine
  15. Indonesia
  16. Malaysia
  17. Jordan
  18. Mexico
  19. Kenya
  20. South Africa
  21. Ireland
  22. Peru
  23. Chile
  24. Armenia
  25. Tunisia
  26. Democratic Republic of the Congo
  27. Belarus
  28. Argentina
  29. Albania
  30. Ecuador
  31. Sudan
  32. Austria
  33. Nigeria
  34. Syria
  35. Kyrgyz Republic
  36. Egypt
  37. Belgium

Using a regression model applied to a large number of drivers of conflict variables spanning numerous open source social science datasets, our model uses a novel Negative Residuals technique. Negative Residuals result from the model predicting higher levels of violence than actually experienced, indicating nation states that are pre-disposed to increasing levels of violence based on the presence of environmental conditions and drivers of conflict with demonstrated correlation with measured political violence. The residuals imply that these are states that we expect to observe increases in violence although not necessarily high levels of violence. So Iran and Sri Lanka are not expected to have the same level of violence but are expected to have the same magnitude increase in violence.

There some unexpected countries on our list like Czech Republic and Italy. Time will tell the accuracy of our model’s predictions although recent political violence in Ecuador is an early indicator of the model’s effective performance. The model uses nuanced measures of repression and captures variables that can be manipulated by policy makers. Our project page has further details on the model.

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The Age of Assistants?

Wednesday, August 25th, 2010

Reading Norman Winarsky’s post on The Age of Assistants reminded me of the scene from the movie Dancing with the Wolves where the Sioux Chief asks Lieutenant Dunbar played by Kevin Costner:

You always ask about the white people. You always want to know how many more are coming. There will be a lot, my friend. More than can be counted.

How many?

Like the stars.

In a similar vein, Winarsky says: “And we likely won’t have just one assistant – we’ll have two or three or maybe even 10, a scalable, distributed cadre, an army, even – of Virtual Personal Assistants (VPAs) at our service.” I believed that in 1993 when we shipped the world’s first desktop assistant Open Sesame! for the Mac. Open Sesame! was a learning agent that observed a user’s interaction with the operating system GUI, found repetitive patterns and user preferences, and offered to automate repetitive tasks for the user:

Open Sesame Box.jpg Open Sesame Dialog Box.jpg

Open Sesame! was a relative success on the Mac. It was localized in Japan, shipped with every PowerPC in Taiwan, and got positive reviews in US, Italy, Germany, and beyond. In A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web, Fink and Kobsa state: “Open Sesame can be considered an early pioneer of personalization, both in research and commercial environments. Despite its early market entry and its sophisticated features, there is, to the best of our knowledge, no commercial system on the market that is comparable to Learn Sesame.” In spite of the positive reviews, Open Sesame! could not escape the criticism of generating a new category of software – nagware. Alas Open Sesame! cannot even get credit for generating this category in Winarsky’s article, which bestows this honor to Microsoft’s brain dead Clippy.

We presented our analysis of the rich database of user feedback collected with Open Sesame! in our Applied Artificial Intelligence paper Learn Sesame – a Learning Agent Engine. While the users found event based learning useful, they found the monitored events and offered actions limited in scope, and stated their desire for improved agent communication and social skills. In the intervening 15 years, a lot changed in personal computing to make the conditions ripe for software assistants:

  • Most personal computer users have embraced direct manipulation in user experience. The idea of delegating a task to a software assistant, and waiting a couple of seconds, minutes, hours, … etc. will only work for boring periodic maintenance type of tasks. Both personal and server computers have now become so powerful, enabling the opportunity for offering instant responses to delegated tasks. That will change everything in that delegated tasks to software agents will perform like direct manipulation to a user, thus increasing adoption.
  • Thanks to mobile computing, users have become used to notifications on their smartphones. Agent notifications that seem like intrusive spam on the desktop are now welcomed by users on their phones. In other words, we are more open to intrusion on our smartphones as they entertain us while waiting for a 3-hour flight delay. So the mobile computing platforms will be more welcoming to software agents that notify their users on the delegated tasks for status updates, additional task clarification requests, and the like.
  • Social computing helped users embrace the notifications of changes in user’s social networks. The new generation of users cannot go a couple of seconds without clicking on a Facebook notification on their mobile app, which is essentially a notification agent for Facebook. In other words, Facebook is teaching users the value of notification, which was considered nagware intrusion on the desktop.
  • There is a decent amount of content for personalization. In 1998, using Open Sesame learning engine, we built eGenie – a personalized Web site that learned user interests, built user profiles and presented personalized content for new books, movies, TV shows, concerts, etc. Frankly, how personalized can be a movie preference that you share with millions of others? Not much. In contrast, the social media is now generating truly personal content like your friend’s Facebook updates, Delicious annotations … Personalization that can be performed by software assistants has a lot more value for the Long Tail.
  • Semantic technology is coming on strong. As your friends and colleagues generate more semantically tagged content using tags, forms, etc., it will make the job of personal assistants easier in filtering knowledge of import to users. Similarly, web services APIs , linked data, etc. are becoming mainstream, thus making it easy for your personal assistant to interact with these data and services in the cloud programmatically on your behalf.
  • When I first showed Open Sesame! to Don Norman at Apple, he asked: “What enabled this product to be built? Why now?” I replied: Apple Events enabled us to monitor user actions reliably, and instruct the OS to perform tasks with ease. My answer had some element of truth as trying to build the same assistant for Windows 95 proved to be an insurmountable task due the lack of support for high level recordable and scriptable events on this platform. Now that the Web browser is becoming the GUI of the operating system as we move more towards cloud computing, it is relieving personal assistants from the necessity of learning the legacy of desktop operating systems, and putting up with their changes.

At milcord, we are keeping the learning agent flame burning in our Commander’s Learning Agent project. Stay tuned.

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Multi-Criteria Decision Modeling for Complex Operations

Thursday, July 8th, 2010

Next week we will be presenting a paper at the International Conference on Cross-Cultural Decision Making in Miami, Florida. I am looking forward to participating in a highly informative and interesting session, bridging modeling and simulation disciplines with socio-cultural data for military operations. In our paper entitled “Geospatial Campaign Management for Complex Operations”, we report initial findings from a research effort to understand the complexity of modern day insurgencies and the effects of counterinsurgency measures, integrating data-driven models, such as Bayesian belief networks, and goal-driven models, including multi-criteria decision analysis (MCDA), into a geospatial modeling environment in support of decision making for campaign management. Our Decision Modeler tool instantiates MCDA, a discipline for solving complex problems that involve a set of alternatives evaluated on the basis of various metrics. MCDA breaks a problem down into a goal or set of goals, objectives that need to be met to achieve that goal, factors that effect those objectives, and the metrics used to evaluate the factor. Since the selection of metrics for specified objectives and data for computing metrics are the biggest hurdles in using MCDA in practice, both the metrics and associated data are part of our tool’s library for user reuse. Below is an image of the MCDA structure. Click on any of the images in the post to see more detail.

Our decision modeling tool also incorporates a weighting system that enables analysts to apply their preferences to the metrics that are most critical for the mission. Linking these decision models in a shared space within the tool creates a repository of knowledge about progress along lines of effort in an operation, providing a source for knowledge transfer for units rotating into and out of the theater. The alternatives considered in the decision model are different courses of action that can be evaluated against metrics to determine the optimal action for accomplishing the commander’s goals. Of course, working in a complex human system such as the one found in counterinsurgency and stability operation environments, our tool is not meant to be a ‘black box’ model that simply reports to the user what to do, but rather the decision analysis provides insight through both qualitative and data-driven models about what courses of action will set the conditions for a more successful outcome based on the commander’s intent.

In evaluating our tool with users, we determined that one of the most important features involves the visualization of the tradeoffs for various courses of action in the decision model. To address this, we compute the uncertainty of data based on its distribution and propagate its effect analytically into the decision space, presenting it visually to the commander. A greater dispersion represents more uncertainty, while a clustered set of data points indicates more certainty regarding the cost and effectiveness metrics for a particular course of action. In this way, we are able to represent the high levels of uncertainty inherent in socio-cultural information without negatively impacting the ability of our tool to calculate a decision model. By incorporating a visual representation of uncertainty in the model, scenarios can then be played out to determine optimization for various courses of action based on data inputs and user preferences, translating model outputs into a form that can more readily be used by military users.

To demonstrate an example of how the visualization of uncertainty would work in the tool, in the image below we have analyzed two potential courses of action relating to the essential services line of effort with the objective of supporting healthcare initiatives in an area of operations. In this case, we are deciding where to focus our efforts, comparing two districts, Arghandab and Anar Dara in Southern Afghanistan. Here we are only examining a few potential metrics: the cost of building healthcare centers proposed by local development councils; the number of basic healthcare centers already in the district; and the number of people that identified a lack of healthcare as the major problem facing their village, a question that is collected in the Tactical Conflict and Assessment Planning Framework (TCAPF) data. Our MCDA tool would compute and display the effectiveness versus costs data points from metrics corresponding to the two proposed courses of action. We want to determine which district would optimize our goal of restoring essential services with the objective of supporting healthcare initiatives by leveraging the data inputs. In considering the uncertainty, we have represented the distribution in the ellipsoid around the data point. This allows a military planner to visually analyze and evaluate the potential courses of action based on cost versus effectiveness metrics, while accounting for the uncertainty of the data. In addition, the weighting system, sliders shown on the right hand of the image, allows a military planner to experiment to determine how a change in metrics will affect the proposed courses of action.

One of the key benefits of our approach is that it allows for real-time knowledge generation. By updating the model with new data the Decision Modeler will re-evaluate the outlined courses of action against the new information, allowing the user to view trends over time in the effectiveness and cost metrics for particular courses of action. In the example below, perhaps the cost estimates went up for the proposed course of action in Anar Dara given deterioration in the security situation that affected the ability of hiring contractors to execute the project. In Arghandab, the metric could have changed according to our collection of TCAPF data, emphasizing that more people responded that healthcare is the major problem facing their village, therefore, increasing the effectiveness against our objective if we built a healthcare center there. Given the increased need, the villagers have offered to provide labor at decreased cost and will contribute a certain percentage of funds to the project, therefore representing the decreased costs associated with Arghandab data points. In this way the tool will provide course of action forecasting based on an analysis of data for the purposes of proactively planning operations that optimize the commander’s objectives.

We will be presenting a more detailed analysis of our research results at the conference, so keep an eye out for links to our papers and presentation.

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Tribal Human Terrain of Afghanistan

Thursday, July 8th, 2010

Under the sponsorship of the OSD Human Social Culture Behavior (HSCB) program, we are developing a semantic wiki for Complex Operations. The envisioned operational impact of our effort is to foster collaboration and sharing of knowledge for whole-of-government approach, and to improve COIN/SSTR operations analysis and execution by focusing on population as center of gravity. The development of such a wiki presents several challenges that include the broad domain area of knowledge complex operations require, a large number of doctrine publications to wikify and semantify, several out of print key references, etc. With these challenges, we saw an opportunity to develop an open source culturepedia for Afghan and Pakistan human terrain as such knowledge is not aggregated and not readily available.

The Complex Operations wiki currently contains more than 1,000 articles on the various tribal dynamics and locational knowledge for the Afghanistan and Pakistan region, outlining tribal meta-knowledge such as the sub-groups, primary locations, traditional alliances, and traditional disputes of various groups to support situational awareness about the human terrain. Here is the wiki page for the covered Afghanistan Organizational Groups. We have created over 150 concept maps (an example shown below) to capture the knowledge about 1,000 ethnic groups, tribes, sub-tribes, clans within Afghanistan and Pakistan region to make this human terrain knowledge readily accessible to the complex operations practitioner.

tribal concept map.png

Tribal Tree in Afghanistan (click to view full-size)

Our use of a semantic wiki platform enables the representation of the human terrain knowledge as facts and relationships. For instance, the wiki page for the Achakzai tribal group lists the the known facts and relationships about this ethnic group both a human consumable form using semantic forms:

Achakzai Semantic Form.tiff

, and a machine consumable form as semantic RDF relationships:

Achakzai RDF.tiff

Factbox (click to view full-size)

By inspecting the semantic form, the reader can deduce that Achakzai is a sub-tribe of Zirak, which is a sub-tribe of the Durrani super-tribe, primarily located in the Chora and Khas Uruzgan districts, and traditionally have disputes with the Nurzai, Panjpai and Kakar tribes. The representation of this knowledge in a semantic wiki has the additional advantage for faceted browsing and answers engine queries. For instance, the semantic wiki can answer questions like “What are the tribes in Kandahar Province and their traditional disputes?” as a table which gets automatically updated every time a new tribe in this province is added to the wiki:
Tribes in Kandahar.tiff There are also several groups in Afghanistan that do not organize around tribal kinship ties, including Uzbeks, Tajiks, and Hazaras. In addition to tribal affiliation, social organizations such as solidarity groups – a group of people that acts as a single unit and organizes on the basis of some shared identity, and patronage networks – led by local warlord or khan – play an important role in understanding of the human terrain. Afghan and Pakistan human terrain and situational awareness knowledge base can be extended to include other populations of interest to the community, such as Yemen or Somalia.

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Shuffling methodology for sanitizing Afghanistan TCAPF microdata: a working paper

Wednesday, June 30th, 2010

Sometime back in February 2010 I started a working paper titled “Shuffling_Methodology_for_Sanitizing_TCAPF_Microdata” (click to download as PDF) which outlined the methodology I used for data sanitization of TCAPF data.  The sanitization approach I discuss is applicable to cases where its desired to share unclassified data while preserving the privacy (and operational security) inherent in the data.

Essentially the data which was shared with us by USAID, although it was unclassified it had distribution restrictions due to the sensitive nature of the data which was collected by 24th MEU and other units in Afghanistan.  We felt compelled to publish the results from a bayesian analysis we performed on the data and thought it best to sanitize the data first and then publish the results from the cleansed data.  In order to do so, we had to maintain the analytical value of the data by preserving the distributional properties of the dataset for the results obtained to remain valid.  We had to balance this need for preserving analytical value with the privacy needs to withhold or obfuscate data fields deemed too sensitive to disclose.

The discussion in the paper where I go through a thought process of what could go wrong should get you thinking, at least.  I welcome your feedback and ideas in the comments below.

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Socio-Cultural Modeling & Analysis at NPS

Tuesday, April 27th, 2010

Social Socio-Cultural Modeling & Analysis
(click to view video)

Naval Postgraduate School in Monterey is one of our government’s educational jewels. Nestled in the beautiful landscape of the Monterey Peninsula, this institution brings togethers a diverse group of educators, researchers and student practitioners to promote a vigorous debate of the issues facing our national defense, and the advancement of solutions addressing these issues. Last week I had the pleasure of giving a couple of talks and participating in a panel discussion at NPS. Here is a quick rundown.

The first day, I was the invited speaker for a panel discussion on Socio-Cultural Modeling & Analysis. This panel discussion explored the problem of modeling and analysis to provide insights to decision makers on complex socio-cultural issues from the perspective of both social scientists and computational modelers. The panel discussion addressed the questions:

  • How does the inherent variability within humans impact the ability to draw insights from modeling and analysis?
  • What strategies can be used to address the challenges of modeling and analysis in the human domain?

My presentation sparked some interesting questions like how can we convince the Commander to help with data collection when the Commander sees no immediate return on the invested overhead. I suggested that DoD can replicate what consulting companies do: Put a resource who has no execution task other than recording knowledge in project executions. Panel discussion generated a lively debate between social vs. computational scientists. One of the computational scientists on the panel said that everyone wants to solve “easy to model” instead of “hard to model”, which is what the decision maker is interested in. For instance, coloring the map of failing states using the Political Instability Task Force (PITF ) or our Predictive Societal Indicators of Radicalism (PSIR) models provides hardly any new insight to the General in charge.

Another criticism was the publishing delay in social science data sets (e.g. CIRI, MAR, Uppsala, etc.). For instance, human rights data set publishers wait for the State Department and Amnesty International to publish their annual reports for the previous year in spring this year. Then they take a couple of more months to code the reported incidents and publish. Such a delay does not exactly match DoD operations focusing on the current. I advocated the need for publishing real-time social science indicators that can be adjusted later like the government’s GDP revisions six months later.

Social scientists on the panel stressed the importance of representing qualitative in addition to quantitative knowledge in these models. For instance, socio-cultural responses to color can be significant as the color red represents celebration in Chinese, purity in Indian and danger in Western cultures. This kind of knowledge is certainly relevant in SSTR operations. Dr. Guttieri cautioned against the public perception of manipulation using socio-cultural models citing Project Camelot.

It was nice to see the articulation of the healthy tension between the social and computational scientists in the audience. In closing, I advocated packaging of social science for tactical operations where warfighters are serving as or advising governors, town managers, mayors – jobs that they were not trained for.

HSCB Brown Bag: Hybrid Knowledge Management
(click to view video)

The second day, I gave a brown bag seminar at the NPS Cebrowski Institute on our Semantic Wiki for Complex Operations project. This project aims to address the gaps in current solutions supporting COIN/SSTR operations:

  • Document-centric repositories makes seeking answers time and effort intensive
  • Disparate knowledge “silos” makes situational awareness hard for complex contingency operations requiring interagency cooperation

Semantic wikis enable community-powered structured knowledge production using semantic forms, faceted browsing of structured content, powering answer engines and linking different data sets. There was significant interest in using our semantic wiki for teaching as such an approach can significantly increase the amount of learned knowledge NPS students take to the field of practice, and provide an effective reach back capability from the field.

I visited TRAC-Monterey, which has a number of interesting projects. In particular, I found the Cultural Geography project interesting as an agent application. This project started as Urban Cultural Geography for Stability Operations. The Cultural Geography model employs issue based segmentation of the social network of leaders, followers using communication theory and weapons of influence concepts to predict the future based on population identity groups. The mind of the agent is a belief network that develops actions based on the beliefs, values, interests of the associated identity group. COIN IPB and Center of Gravity (COG) is the target result.

I also paid a visit to Defense Resources Management Institute (DRMI) at NPS. Here I found the Multi Criteria Decision Making (MCDM) course of particular interest as it relates to the SSTR Campaign Planner tool we are developing in our PSIR project. DRMI teaches the MCDM course as a 2-day, 2-week, 4-week and quarter formats to a wide audience from DoD, DHS, Emergency Response Teams. MCDM is widely used as a decision-aid tool for ranking decision alternatives. DRMI course emphasizes visualization of the decision space instead of ranking alternatives by scores. Such an approach enables the user to detect conflicting criteria, cluster alternatives, eliminate undesirable alternatives, and select the optimal alternative.

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