The Information Acquisition Model (IAM) illustrates the decision-to-perception chain, which begins and ends within the decision-maker. The chain is initiated when a decision-maker realizes that additional information is needed to make a decision, and then performs or requests an action that should reduce uncertainty by providing the desired information. Examples range from a healthcare setting in which a physician uses a series of laboratory tests to decide on the best therapy for a patient; to a culinary setting where a cook uses a series of taste tests to decide on the need for additional seasoning. In either setting, decisions are made, actions are taken, and information is generated.

The decision-to-perception chain has numerous opportunities for failure. First, it is subject to personal bias because the chain begins and ends within the decision-maker. Second, an action cannot be assumed to have been performed as expected, especially if the action was performed by another individual. Third, the appropriate data from the action cannot be assumed to have been generated, captured, and recorded. Fourth, the resulting information cannot be assumed to have been accurately portrayed to and perceived by the decision maker.

IAM is a useful framework for preventing failures in the decision-to-perception chain when used to design and develop information management systems. It can also be used to identify existing failures when each component and activity in the model is used to focus attention on its corresponding real-world counterpart, which can then be examined for characteristic patterns of missing or inaccurate information.

Decisions are always made with some degree of uncertainty. The goal is to reduce uncertainty as much as possible by creating efficient and effective information management systems, and by improving the reliability of all personnel and activities associated with the decision-to-perception chain. IAM provides a systematic framework for identifying sources of that uncertainty.