The amount of information and the diversity of sources of information in tactical environments continue to grow, increasing the requirement of intelligence analysts to filter the valuable information from the noise. Information is often gathered by individuals of differing expertise in a given topic area. Furthermore, it might be inherently more difficult to determine signal from noise in some topics. Hence, it would be crucial to understand how both the level of expertise and the level of difficulty of a problem impact the ability of a person to correctly classify it. By understanding the distribution of errors, it is possible to create methods to overcome them. To accomplish this, we analyze the results of an experimental study and develop a mathematical model for expertise. To the best of our knowledge, no such model exists. Based on this model, we show how we can develop an agent simulation that mimics the expected performance of a human agent under different conditions.
展开▼
