The effect of human pattern-recognition abilities in improving DSS performance

In this paper, the function of a data-centred decision-support system (DSS) is simulated to investigate whether the incorporation of human pattern-recognition abilities significantly improves the performance of a system. Two decision making scenarios are considered. In one scenario, there is no human interaction, whereas the other scenario uses the pattern-recognition capabilities of humans. The simulation is performed by mining 10,000 records in 980 replications. The DSS has the ability to take corrective actions with the purpose of keeping the incoming data records within a given set of upper and lower boundaries. The results indicate that incorporating pattern-recognition ability in a DSS significantly improves the system’s performance. However, the impact of human input is not linear with respect to system performance. Our study shows that a moderate degree of human intervention will usually provide the greatest positive impact on the system’s performance.