Information tradeoffs in model building: A network routing application

An integral part of the model-building process is the modeler's choice of how much information to gather and encode in the decision model. Obtaining more detailed and accurate information enables a more precise problem representation which, in turn, leads to more effective decision making. However, acquiring extensive and accurate information entails higher costs and delays. This paper uses a network routing decision context to illustrate the tradeoff between model precision and decision effectiveness, and explores a formal decision-theoretic approach to determine an appropriate model specification that balances information gathering costs and decision quality. We propose optimal and heuristic methods for generating good information search strategies, and report computational results based on random test problems. Our results highlight the importance of simultaneously considering information costs and decision payoffs for constructing a decision model to support routing decisions. The issues raised in this paper are especially significant for modeling dynamic, real-time decision contexts where delays induced by information gathering activities could have significant economic impact.