Toward Measuring Information Value in a Multi-Intelligence Context

In decision-making under uncertainty, the objective of measuring information value is to assist a decision-maker toward making good decisions by generating necessary metadata about the information that is being considered for the decision-making task. Epistemic decisions about which sources to trust or query are critical for a decision-maker when the end-goal (or final) decisions are to be made using limited number of information sources. This is even more critical in a multi-intelligence context, where information sources are highly heterogeneous, prone to errors, partially informed, deceptive, or even malicious. In these contexts, the ability to distinguish between aleatory and epistemic uncertainty (ignorance) becomes a fundamental requirement. In this paper, we propose some extensions to classical measures of value of information for imprecise belief states represented by belief functions relying on a general observation model. The proposed measures allow a decision-maker to highlight critical parameters, such as the probability of source reliability and the degree of confidence expressed by the source. We compare several decision models and illustrate the use of proposed measures in a maritime surveillance scenario, where the decision-maker has to make a rational selection of information sources that consists of both physical sensors and human sources. We conclude by providing some insights on future research directions to expand this preliminary exploration.