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.