Multi-Criteria Decision Modeling for Complex Operations

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.

NAACSOS Annual Conference

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 CenterCultural 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.

The Inheritance

Perhaps now more than at any other time in our nation's history, the United States faces a multitude of strategic threats and challenges. Rogue regimes, militant Islamist networks, and changing power balances from rising nations such as China, to failing states such as Pakistan, threaten to upend the security and stability of the United States. 


As a research assistant for The Inheritance: The World Obama Confronts and the Challenges to American Power, a book by David E. Sanger, Chief Washington Correspondent for The New York Times, I had the opportunity to dive deep into issues ranging from Chinese military modernization to cyber-security to the Iranian nuclear program. My research took me into the Pakistani nuclear establishment and the militant threat emanating from the tribal areas to the post-invasion environment in Afghanistan and the personalities shaping the debate on counterinsurgency in the post-9/11 world. 


The democratization of technology involving nuclear materials, cyber-attacks, and biological agents, has provided non-state actors access to weapons that were previously the purview of states. The multifaceted nature of these complex issues will require greater interagency cooperation and knowledge transfer, in particular in the civil-military field. Securing the homeland from the threat of radiological weapons will require a robust intelligence effort abroad to root out shadowy networks dealing in such materials, such as those of A.Q. Khan, increased focus on securing at-risk facilities in Russia and the former Soviet states through initiatives like Cooperative Threat Reduction, and increasing collaboration between the scientific community and government entities such as the Domestic Nuclear Detection Office to bring cutting edge research and technology to the detection of radioactive materials crossing our borders. 


In the cyber-security realm, bolstering public-private partnerships between government entities such as the military and intelligence community, and corporations, financial institutions, and public utilities, often the targets of cyber-attacks, will be important in developing detection and response capabilities and formulating comprehensive rules of engagement. In addition to the military component of COIN operations, civilian teams specializing in security-sector reform, judicial and political affairs, economic development, and infrastructure, will be operating in the battlespace to bolster host government legitimacy, the center of gravity in the campaign. Given the shared responsibilities in the civil-military field on these issues, fostering knowledge integration and cooperation between the various branches of government, military, and civilian stakeholders is of paramount importance to ensuring unity of effort. 


The Inheritance is a researched-backed analysis of the challenges we currently face, a legacy of the opportunities missed after 9/11.  While I may be biased because of my involvement with the book, I strongly recommend it to anyone interested in understanding the challenges confronting Obama and the complexities of the geopolitical environment. 

Milcord extends Political Instability Task Force model to insurgency forecasting

Using the COIN and Stability Operations Field Manuals as a process model, Milcord's [[Predictive Societal Indicators of Radicalism]] (PSIR) analytical model predicts future radicalization based on current and historical societal indicators by finding the causal relationships between governance, economic, grievance, essential service indicators, and radicalization metrics. Find out more about our [[PSIR]] project.