On Automating Candle Lighting Analysis: Insight from Search with Genetic Algorithms and Approximate Models

As decision makers, we often need to find solutions to problems that we understand only approximately. For these difficult situations, we consider a model-based approach for a type of analysis, which we term candle lighting analysis, which suggests actions we might take to create new and better options for us. This paper shows how a space of actions can be searched by a genetic algorithm, automatically providing tradeoff and sensitivity information to the decision maker. We use this approach in a DSS and show how a decision maker can obtain insight into the problem itself and knowledge about effective actions to take.