Computer-assisted reasoning

Over the past few years, a novel approach to understanding complex and uncertain problems has emerged. The central insight is to conceive of any model run on a computer as a computational experiment. Instead of constructing and running only the single model that we believe best represents the system in question (after making various assumptions and a priori decisions), we can examine large numbers of models that depict alternative plausible future states of the system. This ensemble of plausible models can provide information not captured by any single best-estimate model. Furthermore, working with such an ensemble enables methodological approaches leading to more powerful and appropriate means than have heretofore been available for reasoning about these problems. By looking in many mirrors, each necessarily flawed (albeit in different ways), we can see truths that no single mirror can reveal. The authors show how they have implemented this approach, giving examples of problems where it has been fruitful