Scott George C, Shachter Ross D
Department of Medicine, University of California, San Diego, CA 92103, USA.
J Biomed Inform. 2005 Aug;38(4):281-97. doi: 10.1016/j.jbi.2004.12.003. Epub 2005 Jan 19.
Complex decision models in expert systems often depend upon a number of utilities and subjective probabilities for an individual. Although these values can be estimated for entire populations or demographic subgroups, a model should be customized to the individual's specific parameter values. This process can be onerous and inefficient for practical decisions. We propose an interactive approach for incrementally improving our knowledge about a specific individual's parameter values, including utilities and probabilities, given a decision model and a prior joint probability distribution over the parameter values. We define the concept of value of elicitation and use it to determine dynamically the next most informative elicitation for a given individual. We evaluated the approach using an example model and demonstrate that we can improve the decision quality by focusing on those parameter values most material to the decision.
专家系统中的复杂决策模型通常依赖于个体的一些效用和主观概率。虽然这些值可以针对整个人口或人口统计学亚组进行估计，但模型应根据个体的特定参数值进行定制。对于实际决策而言，此过程可能既繁琐又低效。我们提出一种交互式方法，在给定决策模型和参数值的先验联合概率分布的情况下，逐步增进我们对特定个体参数值（包括效用和概率）的了解。我们定义了诱导价值的概念，并使用它来动态确定给定个体的下一个最具信息性的诱导。我们使用一个示例模型对该方法进行了评估，并证明通过关注对决策最为关键的那些参数值，我们可以提高决策质量。
