Constraints, Limitations, and Assumptions
The TRADOC Analysis Center provides a useful handbook on constraints, limitations, and assumptions. For clarification, the following definitions are used:
- Constraint: A restriction imposed by the study sponsor that limits the study team's options in conducting the study.
- Limitation: An inability of the study team to fully meet the study objectives or fully investigate the study issues.
- Assumption: A statement related to the study that is taken as true in the absence of facts, often to accommodate a limitation.
There will generally be two sets of CLA: full and key. The full set should be maintained by the Study Team and many of them, typically those that have a minor impact on the study, may not be shared outside the team.
"The study team must gain concurrence from the sponsor on those CLA that could significantly impact the study results. The study team may have to engage the sponsor more than once in reaching agreement on this initial 'key set' of CLA, but it is absolutely critical for facilitating the conduct of the study." [from the TRAC code of best practice]
CLA should be recorded as they become apparent. And they should be reviewed and updated continuously. They can be recorded in the Project Workbook at time at which they are recognized by the Study Team. When offered to others they may be tailored to the audience: there may be some that are technical, others that are non-technical. Rather than wasting time, the technical concerns can be raised with a audience that would find them pertinent, and which may be able to assist the team in that regard. For non-technical CLA, the team may wish to seek advice from various sources.
Two obvious constraints are caps on resources and available time to conduct the study. These are typically set by the sponsor. Indeed it is more typical that the sponsor will allocate a budget for the study and set a due date for a final report (which become contraits for the Study Team). Within these broad limits, the Study Team may have considerable latitude.
In the defense and security arena, another constraint can be the security classification of the project. This may rule out certain venues, or players who may lack clearances. For example, it may rule out using certain venues that are not configured for classified work. For wargaming, a more significant implication of constraints from security classification is that it may reduce the diversity of opinion amongst the players. Contrariness in wargaming is a critical feature: it should mean that concepts or tentative plans are well scrutinized during the gaming process. But if some of the most effective mavericks and contrarians are left out of the activity, the scrutiny may be inadequate.
Some potential limitations are:
• skill levels within the team (which may be ameliorated by a training plan, or by augmenting the team with appropriate experts)
• data on weapon, sensor, or communications systems (which may require assumptions that a system which has unknown performance operates with the same characteristics as a well-known and similar system)
• computer-based simuations are running too slowly to provide timely results (which may necessitate running a number of situations in advance and using results from this to interpolate something for a faster response)
Assumptions can be used to narrow scope, keep wargaming players focused on the objectives and issues YOU want them to examine. (A well-prepared wargaming moderator/facilitator will anticipate the need for assumptions as the play of a game unfolds, and be able to create plausible ones on the fly.)
Constraint: "The war game must be completed by 11 June."
Constraint: "The wargame report is due at the end of the month."
Constraint: "You must use Strategic Scenario DELTA."
Constraint: "The wargame study is classified no higher than CONFIDENTIAL."
Constraint: "Do not plan to draw players from VF 310 as they are preparing for an OPEVAL."
Constraint: "I want RADM Johnston to be the RED commander."
Limitation: Our study team lacks extensive knowledge of the culture, politics, and economics of South Central Asia.
Limitation: We lack statistical software to do more than an elementary quantitative analysis.
Assumption: Since we lack performance data for the PERIWINKLE UAV, we will use data for PREDITOR. (We know they are of similar size, powerplant, and payload, and used for similar tasks.)
Assumption: Since we do not know the level of training and morale of the OPFOR, we will assume they are the same for all units.