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Predicting the future and then acting on our predictions leaves us vulnerable to surprises. So we need decisions that will work in a variety of potential situations.
Robust decision making (RDM) is a framework for making decisions with a large number of highly imperfect forecasts of the future. Rather than relying on improved point forecasts or probabilistic predictions, RDM embraces many plausible futures, then helps analysts and decision makers identify near-term actions that are robust across a very wide range of futures - that is, actions that promise to do a reasonable job of achieving the decision makers' goals compared to the alternative options, no matter what future comes to pass. Rather than asking what the future will bring, this methodology focuses on what we can do today to better shape the future to our liking.
RDM emerged from work at RAND beginning in the early 1990s, when we, analysts Robert Lempert and Steven Popper, were separately grappling with policy problems characterized by deep uncertainty and potentially non-equilibrium dynamics - in particular, climate change and the transition of east European communist societies to market economies. Meanwhile, RAND computer scientist Steve Bankes was grappling with the question of how one can use imperfect computer models to inform policy decisions, particularly to deal with the next wars rather than previous ones.
In brief, RDM uses the computer to support an iterative process in which humans propose strategies as potentially robust across a wide range of futures. The computer then challenges these strategies (stress tests) using simulations and data extrapolations to suggest futures where these strategies may perform poorly. The alternatives can then be revised to hedge against these stressing futures, and the process is repeated for the new strategies.
Rather than first predicting the future in order to act upon it, decision makers may now gain a systematic understanding of their best nearterm options for shaping a long-term future while fully considering many plausible futures. The result is nearterm policy options that are robust - i.e., that, compared to the alternatives choices, perform reasonably well across a wide range of those futures.
The strength of robust decision making is its flexibility. In this iterative process, the computer retains the full range of uncertainties, multiple interpretations, and...