Enhancing Strategic Planning with Massive Scenario Generation

This report extends research on using scenarios for strategic planning, with experiments in what can be called massive scenario generation (MSG), a computationally intensive technique that seeks to combine virtues of human- and model-based exploration of “the possibility space.” We measure particular approaches to MSG against four metrics: not needing a good initial model; the dimensionality of the possibility space considered; the degree of exploration of that space; and the quality of resulting knowledge. We then describe two MSG experiments for contrasting cases, one that began with a reasonable but untested analytical model, and one that began without an analytical model, but with a thoughtful list of the conditions that might characterize and distinguish among circumstances in the situation considered, a list derived from a combination of single-analyst thinking and group brainstorming. We experimented with a variety of methods and tools for interpreting and making sense of the “data” arising from MSG, using ordinary linear sensitivity analysis, a generalization using analyst-inspired aggregation fragments, some advanced filtering methods drawing on data-mining and machine-learning methods, and motivated metamodeling.

On the basis of this preliminary work, we conclude that MSG has the potential to expand the scope of what are recognized as possible developments, provide an understanding of how those developments might come about, and help identify aspects of the world that should be studied more carefully, tested, or monitored. It should assist planners by enriching their mental library of the patterns used to guide reasoning and action at the time of crisis or decision and should help them identify anomalous situations requiring unusual actions. Finally, it should identify crucial issues worthy of testing or experimentation in games or other venues and, in some cases, suggest better ways to design mission rehearsals. If MSG can be built into training, education, research, and socialization exercises, it should leave participants with a wider and better sense of the possible, while developing skill at problem-solving in situations other than those of the “best estimate.” Much development is needed, but prospects are encouraging.