The Age of Assistants?

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

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

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

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

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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Kneber Botnet – less fluxy but more stealthy

February 19th, 2010

The recent news story about the Kneber botnet based on the excellent work done by the NetWitness team and informative posts by Dancho Danchev and others brought the ZeuS Trojan botnet into limelight. In contrast to some misleading reports, the security community has been following this botnet, which infected more than 75,000 computer systems at nearly 2,500 companies, for quite a long time. We have been tracking ZeuS with our Fast Flux Monitor for some time as well. Given the recent interest in this botnet, we decided to analyze the reported ZeuS data using our Fast Flux Monitor database to provide some additional insight.

Most of the domain, nameserver and IP entities associated with the attacking infrastructure reported in the NetWitness Kneber report have been in our FastFluxMonitor database. What is interesting is that most of the reported Kneber domains and nameservers are not exhibiting fast flux behavior. For instance, all of the reported Kneber domains for the Trojan installers resolve to 1 to 4 IPs, which is not enough for using a fast flux evasion scheme. The number of domains the Kneber Trojan installers resolve to are shown in the table below.

ZeuS Installer.jpg

Comparing the ZeuS network graph with the various botnets in our database reveals that ZeuS botnet has a different network graph than others like Avalanche, Conficker, Gumblar and Pushdo. The figure below shows the domain, nameserver and IP connectivity for the Avalanche botnet:

ffm_avalanche_network.jpg

In this graph, the blue, red, green nodes denote the IPs, domains, and nameservers addresses, respectively. Each cluster represents a set of entities where any two nodes can be linked through the domain, nameserver and IP connectivity . The Avalanche graph has one large cluster and six small clusters, making it easy to discover the various entities of this botnet. In contrast, the same graph for the ZeuS botnet shown below has one large cluster and over 200 small clusters, thus making it hard to discover the various entities of this botnet.

ffm_zeus_network jpeg.jpg

Referring to the data shown in the table above, the reported Kneber domains and nameservers belong to one of the small clusters on the right. These clusters consist of domains and nameservers that do not exhibit fast flux behavior. Whether the small clusters represent the discreet probe of networks by large criminal organizations, or small operator hosting set-ups that downloaded free phishing kits, the ZeuS botnet is stealthier than the others by relying on a large number of smaller clusters used for attack campaigns.

We will present our comparative analysis of the Avalanche, Conficker, Gumblar, Pusdhdo, and ZeuS at the NATO IST-091 Symposium on “Information Assurance and Cyber Defence”, which will provide an explanation for the difference.

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Operation Aurora – Searching for Stars, Finding Comets

February 1st, 2010

When the ‘Operation Aurora, AKA trojan.hydraq’ controversy surfaced, we investigated the role, if any, of fast-flux botnets in the reputed exfiltration attacks from Chinese-supported actors against 33 US technology companies. Our preliminary results using FastFluxMonitor found no direct indication of fast-flux activity associated with the reported domain names. But just as astronomers may detect comets when observing stars, we did find associations between nameservers with fast-flux history and some of the domains and IPs involved in the attacks. In the FastFluxMonitor table below, we see that three of the reported domains used in these attacks share the same nameserver, ns1.3322.net, which is registered to Chinese network operator, CHINANET, ASN 4134, the leading ASN worldwide in terms of Conficker activity.

domains-table-feb-11

Building on this finding, we then used FastFluxMonitor to discover more than 600 bots associated with fast-flux behavior registered to this ASN. In the FastFluxMonitor table below, we see that a few of the nameservers associated with a known-spamming IP from this ASN, 60.191.221.123, are classified as fast-flux. While the IP in question is not classified as fast-flux, its association with nameservers that are fast-flux is reason for suspicion.

nameserver-table-feb-1

With guilt-by-association, domain names or IPs associated with these nameservers are suspicious, irrespective of whether the individual IPs or domains are classified as fast-flux. Cyber-defenders can apply this intelligence as a proactive measure to filter access to or from these domains, IPs, and nameservers. As exfiltration attacks are often complex attacks preceded by social engineering probes such as spear-phishing, proactive measures such as real-time filtering are essential. Perimeter and vulnerability-based defenses are necessary, but insufficient, measures against social engineering attacks.

News Scan – Cyber Security

  • “… unless Google had told us about the attack on it and other companies, we probably never would have seen it. When you think about that, it’s really scary.” – “In Digital Combat, U.S. Finds No Easy Deterrent”, NY Times, Jan 26, 2010
  • ‘Had this attack employed more sophisticated hosting or resolution techniques like fast flux, even the IP addresses would have been useless..” – “Finding Aurora (googlehack)”, NetWitness Blog, Jan 15, 2010

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Concept Map vs. PowerPoint for Briefings

November 25th, 2009

What has PowerPoint given the knowledge worker besides universality? PowerPoint features like automatic generation of slides from outlines, structured knowledge constructs like tables, graphs, and charts support knowledge organization and communication. Although most PowerPoint features have been available since the early days of Mac software such as MORE and Cricket Graph, it is the ubiquity of PowerPoint that created a backlash against the uniformity it imposes on thinking, organizing , sharing of knowledge concepts. In his essay The Cognitive Style of Powerpoint: Pitching Out Corrupts Within, Edward Tufte argues that PowerPoint templates weaken verbal and spatial reasoning, and corrupt statistical analysis by analyzing the NASA briefings preceding the Columbia disaster.

In the government space we serve, the situation is not different. As reported in the Wall Street Journal, PowerPoint has become an ingrained part of the defense culture. For instance, PowerPoint Ranger is now a derogatory term used for a military professional who excels in slidemaking than warfighting. In fact, Margaret Hayes at the National Defense University posits that “You can’t speak with the U.S. military without knowing PowerPoint.” In the Armed Forces Journal essay, Dumb-dumb Bullets, T. X. Hammes goes further to argue that PowerPoint is “actively hostile to thoughtful decision-making”, and has “decreased the quality of the information provided to the decision-maker”.

From a cognition perspective, would you ask a first grader to build a PowerPoint presentation to see their grasp of a concept? No. Luckily for us, thanks to the pioneering work of Joseph D. Novak at Cornell and others, there is something that educators are using for such assignments in K-12 and higher education: Concept Maps. A concept map is a graphical network diagram where each node represents a concept, and the labelled links depicts the relationships between concepts. Here is a concept map that describes what a concept map is.

CMap of Concept Maps.tiff

What is special about this representation? The teacher sees the limitation of the student’s understanding, and multiple students can collaboratively build a concept map for shared understanding. Is that possible in PowerPoint? No. It is simply not possible to assess an author’s level of understanding of the subject domain from a PowerPoint deck as lack of communication skills often masks the knowledge gaps in the underlying domain.

As articulated by Joseph D. Novak, meaningful learning involves the assimilation of new concepts and propositions into existing cognitive structures. What this means that the viewer needs to first identify her/his known cognitive map of the presented concept and then detect the additions to this concept map for true learning. In other words, the viewer of a presentation always tries to find the answers to the following questions:

  • What do I already know in the presented topic?
  • What are the additional knowledge chunks that complement what I already know?
  • Can I trust the presented addition to my knowledge base?

On answering these questions, concept maps trump PowerPoint presentations, which explains their popularity in learning environments. Concept mapping is not a religion espoused by some education crusaders as the effectiveness of concept maps has been studied empirically. In an experiment conducted at the Naval Postgraduate School, Concept Maps were empirically demonstrated to be more effective than PowerPoint on key measures of knowledge transfer and rapidity in creation. In an anonymous survey at the American University in Cairo, a majority of students stated that doing concept maps required them to look at the assigned reading in more depth. A study conducted at a nursing school in Bangkok, Thailand showed that concept mapping is effective in assisting nursing students to summarize their own concepts and improve their nursing core competency in primary medical care.

There are other advantages of using Concept Maps in presentations. Jim Benson in his blog post makes the interesting point that concept maps create a continuous conversational flow with no breaks while noting that PowerPoint creates an unhealthy distraction of “What’s coming next?”. Steven Kaminski writes that most business PowerPoint presentations, with a little extra work, would be better—even much better—without it because the speaker becomes an audio aid for the PowerPoint slides instead of the presentation being a visual aid for the speaker, which is the case for concept maps.

In addition to several commercial software packages, there are several open source concept mapping tools. The Visual Understanding Environment (VUE) project at Tufts, and CMap Tools at the Institute of Human and Machine Cognition (IHMC) have large active community of users. We don’t have to wait until Concept Maps become a part of the Microsoft Office suite to start using them. Do we?

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Cyber-Terrorism/Warfare – The Emergent Threat: Strategies for Survival

November 23rd, 2009

Last night I attended a panel discussion entitled, “Cyber-Terrorism/Warfare – The Emergent Threat: Strategies for Survival” at Boston University. While the cyber threat is not a new one, it is something that the intelligence community and the Department of Defense have more recently become invested in examining in some depth. One of the first questions raised to the panel involved defining the problem. What is the difference between cyber-crime, cyber-terrorism, and cyber-warfare? To give my own humble two cents, it would seem that the distinction is the same as in conventional operations. What distinguishes between criminal acts and terrorist attacks is the end goal. In crime, the action, for example robbing somebody, is the end goal. The point is to get the money. In terrorism, the end goal is beyond the intended target. There is a political message inherent in the act that is targeted at an audience beyond the victims. Additionally, cyber-warfare would also have a political motive, and to quote Clausewitz, the action would simply be a continuation of politics by other means. To make the distinction between cyber-warfare and cyber-terrorism, it would matter what the intended target was.  Terrorism is usually distinctive from war because it targets noncombatants, or individuals not in a “declared state of war”.  Therefore, the attacks against the Marine barracks in Lebanon in 1983 that killed more than 200 servicemembers was considered terrorism, because the barracks, while being a military target, was housing Marines that were part of a peacekeeping force in the country, and therefore, not in a declared state of war.

computer-cyber-image

The tricky part comes in when one tries to attribute a cyber-attack to a particular actor. Dr. Leonid Reyzin, a cryptology expert stated that our best defense against an attack is to harden our systems. Many government systems do not employ state-of-the art cryptology mechanisms (e.g., many sensitive systems currently use one password for numerous people). Additionally, he pointed out that life-critical systems, systems that if comprised could result in loss-of-life, should be completely disconnected from business networks altogether. He gave an example of a computer virus that spread through email systems, and eventually infected the business system of a nuclear power plant. Due to the fact that the power plant’s business system and critical systems were on the same network, the virus comprised and actually shut off the safety mechanisms of the plant.

Arthur Hulnick, a veteran of 30+ years in the intelligence community, stated that resources to address the cyber threat would best be spent on hiring the best and brightest people. He added that there were too many hurdles to hiring the right people in the intelligence community due to security concerns. Reliance on the polygraph and issues with traveling abroad or having foreign connections (despite the fact that you want bi-cultural or foreign language speakers that often have spent time in these places) prevent people from contributing to the effort.

Another question that was brought up to the panel involved the development of cyber-warfare doctrine. How can one reliably develop a strategy for engagement when there is the issue of attributing an attack to a particular state or actor? Is there a proportional response? Does one respond with offensive cyber capabilities against a country that may not have known their systems were breached? Is there a way to declare this policy for deterrence purposes? Joseph Wippl, another career CIA officer, stated that a robust international effort to share information and best practices would be the best preventive defense against cyber attacks. Dr. Robert Popp, a former DoD official in OSD and DARPA, stated that resources would best be allocated to develop offensive capabilities that could overwhelm our adversaries, hopefully providing some level of deterrence.

Overall, it was interesting and informative evening however, it seems that while there has been much discussion on the subject, there are many more questions than answers.

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NAACSOS Annual Conference

October 26th, 2009

Last week we presented work entitled, “A Systems Dynamics Model of Counterinsurgency in Southern Afghanistan” at the North American Association for Computational Social and Organization Sciences at the Center for Social Dynamics and Complexity at ASU. NAACSOS (which will be changing its name soon to the much more digestible acronym CSSS – Computational Social Science Society) is scholarly society seeking to advance social science through the application of computer simulation and other computer-based methods to the analysis of complex social systems and processes. In a break from our normal conference circuit, there were a small number of presentations focusing on global security issues. The largest percentage of papers addressed developments in agent-based modeling. In particular, the most interesting advance from this perspective involved the integration of GIS technologies and 3-D agents for visualization in agent-based models. Capturing more realistic movement of humans as agents in a model will allow for greater complexity, with particular implications for evacuation and disaster management and planning.

Our paper focusing on Southern Afghanistan was well received and fostered a lively debate. Our presentation related to our work to build a campaign design tool for counterinsurgency and stability, security, reconstruction, and transition (SSTR) operations. In this project we are researching the root causes of insurgency and instability and fusing this knowledge to doctrinal components to find vulnerability points in the insurgent system, modeling the insurgent environment for use by operational commanders in answering what-if type strategic planning and resource allocation questions in the design of campaigns. Our approach supports analysts, planners, and practitioners involved in asymmetric operations by providing operationally relevant information on the relationships between factors driving the insurgency and leverage points identified through counterinsurgency measures, helping to build a more effective campaign design for complex operations.

Integrated Feedback Loops of Instability in Southern Afghanistan:

Integrated Feedback Loops of Insurgency in Southern Afghanistan

The main questions that were raised during the presentation revolved around the utility of relying on the Counterinsurgency Field Manual, given its conceptual approach to operations. This is a familiar criticism we have heard regarding the Field Manual, which was released in 2006. Additionally, a major focus of the conference was on validation of models. Given that our model is more of a conceptual framework for critical thinking as opposed to a black box model, that our project is based on qualitative rules from peer-reviewed and authoritative sources, we offered a different approach to traditional model validation requirements.

The most relevant presentation for our work in complex operations was from the U.S. Army TRADOC Analysis Center “Cultural Geography Model Use in Support of Human in the Loop Experimentation”. This project involved developing an agent-based model of a civilian population to determine responses to government and stability force actions in a counterinsurgency environment. The population was based on data from the city of Amara in Iraq. This model was interesting in that the population was the center-of-gravity, to use Clausewitzian terms, rather than more traditional insurgency-focused representations.

An additional paper of interest involved work out of George Mason University focusing on an agent-based model of kinship relationships in Pakistan. This presentation focused on developing a model based on qualitative rules from anthropological research that informs a template for the actual computer code. While this work is still in its early stages, the goal is to enable prediction of alliance formation.

A personal highlight of the conference revolved around the presentation by Zachary Schaffer on “The Foundress’ Dilemma: An Agent-Based Model of Colony-Founding Strategy in Ants”. This research was looking at the phenomenon whereby unrelated ant foundresses (queen ants essentially that found new colonies) can form seemingly altruistic cooperatives with other foundresses in establishing new colonies. In learning about cooperative colony foundation, I was able to tour the various species of ant colonies kept at the Center for research. Satisfying my itch for an ant farm growing up, it was a fascinating experience.

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