This project aims to develop and evaluate an innovative approach to address errors in judgement in the decision-making process.
Intelligence analysts are typically required to process large volumes of data in a timely manner in order to extract useful information and detect potential security threats. This task relies on consistent judgements by the analyst in order to efficiently process the data and effectively identify useful information.
Research has shown that analysts’ judgements can be inconsistent due to the mass of data, the variation in types and nature of intelligence information and the time pressures the analyst is operating with. Consequently, intelligence analysts may take decisions that deviate significantly from those of their peers, from their own prior decisions, and from training rules that they themselves aim to follow. Such inconsistency is mainly due to two types of errors; noise and bias, which complicate the intelligence analysis process and can result in key pieces of data being misclassified or overlooked with potential security threat implications.
The project will develop, train and evaluate an innovative analytic approach to address these errors and enable analysts to achieve better judgements about the value of elicited information from intelligence reports.
Specifically, the project aims to address the following research questions:
- How much does individual analyst bias affect the quality of the decisions?
- Will incorporation of group decision support, as opposed to individual support, improve the quality of decisions?
- Do additional facilities of feedback for consistency and sensitivity analysis provide support for better decision-making?
Professor Ashraf Labib
Operations Systems and Management Group, University of Portsmouth