Just What are Wargames Good For?

Recently I attended a roundtable discussion on wargaming at one of our national war colleges. During the discussion, a distinguished practitioner of our art mentioned his conviction that wargames were, in fact, good predictive tools. This comment was quite controversial, and it ought to be. Throughout not just wargaming circles, but in the OR world in general there is much ado made about the ability to predict the future. The notion is cast in various terms and syntaxes, most frequently masquerading as anticipatory analysis or behavior.

What’s more, the ability to predict the future is a stated goal of many federal business opportunities (see almost any recent SBIR or STTR solicitation), not to mention various programs already in place in the armed forces (for instance, see Air Force Research Lab’s Focused Long-Term Challenges). As a result, much effort and expense is being put into the notion that somehow there must exist some way to predict what our enemies are going to do, and thus be able to circumvent their actions. Oh what a tangled web we weave.

When we look at both qualitative and quantitative points of view and techniques to gain some insight into how to anticipate the behaviors of adversaries, the level of complexity rapidly outstrips our capacity to account for it. Simplifications usually rely on the description of trends, or the subjectiveness of the subject matter expert. The critical assumption that we’ve taken for granted is that in order to understand what our adversary is going to do, we must understand his culture, his motivations, his environmental influences, and so forth. What we find with this approach is that the problem rapidly becomes intractable.

There are two governing issues. The first I call faith in the one-to-one map, the second is the fallacy of classical determinism. Faith in the one-to-one map is simply the belief that the closer a model gets to reality, ostensibly through the inclusion of as many governing variables and interactions as possible, the more accurate the predictions will be. In truth, this is likely to be an inaccurate correlation. In practice, this approach is simply ridiculous. The problem, of course, is that the amount and accuracy of data required in order to make such an approach feasible doesn’t, and is unlikely to ever, exist. But even if we were able to gather accurately all the necessary data and correctly put together all of the interactions in the system and we could then run experiments with our one-to-one mapping of the world, we still would not be able accurately predict adversarial behaviors. Why? Because the underlying assumption with the approach is that the universe behaves according to the tenets of classical determinism. And the problem with classical determinism is a very simple one: it assumes away random evolutionary variation and the existence of creativity. It also ignores such metaphorical but very real notions as Heisenberg’s Uncertainty Principle or the Lucas Critique.

The nut of the argument: the moment free will enters the equation, deterministic approaches become untenable. We are governed by ANOVA in our techniques, while the world of social interaction, or society, is governed by discrete events that do not fall within the assumptive confines of our scientific notion of trend.

This problem is well Illustrated by Nassim Nicholas Taleb in his book The Black Swan. Taleb refers to this problem as the ludic fallacy. It is summarized as "the misuse of games to model real-life situations." Taleb characterizes the fallacy as mistaking the map for the reality.

This is Taleb’s central argument and is a rebuttal of predictive mathematical models, as well as an attack on the idea of applying statistical models in complex domains. According to Taleb, statistics only work in casinos or places in which the odds are visible and defined. This conclusion rests upon the following three points.

• It is impossible to be in possession of all the information.
• Very small unknown variations in the data could have a huge impact (the Butterfly effect).
• Theories/models based on empirical data are flawed, as events that have not taken place before cannot be accounted for.

Taleb is highly critical of the notion that the unexpected may be predicted by extrapolating from variations in statistics based on past observations, especially when these statistics are presumed to represent samples from a bell-shaped curve. This point of view is easily demonstrable by showing that unlikely events occur significantly more frequently than the tails of the bell curve would indicate. This falsification proof holds particularly well in the realm of social science. He goes on to claim that better descriptive tools include power laws and fractal geometry.

Taleb’s idea that power laws and fractal geometry provide better descriptive tools may hold some promise for discovering new approaches to the problem, but only if we start to better understand what is actually possible in the realm of the predictive. One place to start might be to recognize that understanding our own vulnerabilities may be the best predictor of enemy behavior we’ll ever have. Wargames can certainly help us with that, but we have a lot of poorly preconceived notions to overcome.