Surprise takes many forms, all tending to disrupt plans and planning systems. Reliance by decision makers on formal analytic methodologies can increase susceptibility to surprise as such methods commonly use available information to develop single-point forecasts or probability distributions of future events. In doing so, traditional analyses divert attention from information potentially important to understanding and planning for effects of surprise. The authors propose employing computer-assisted reasoning methods in conjunction with simulation models to create large ensembles of plausible future scenarios. This framework supports a robust adaptive planning (RAP) approach to reasoning under the conditions of complexity and deep uncertainty that normally defeat analytic approaches. The authors demonstrate, using the example of planning for long-term global sustainability, how RAP methods may offer greater insight into the vulnerabilities inherent in several types of surprises and enhance decision makers’ ability to construct strategies that will mitigate or minimize the effects of surprise.
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