Synthetic Worlds for On-Demand Experience
In the rapidly shifting, demanding, under staffed world of information analysis, it is often difficult to remove an analyst from the production environment to engage in education and training without causing the rest of the workforce to be chronically shorthanded. It has been documented that current programs for intelligence analysts vary considerably in their objectives and content and have questionable immediate value to the analyst’s job (Lahneman 2006). Analysts 1 often know when they need to learn something to complete a task, and in such cases they should develop the necessary skills “on-demand.” By providing accelerated on-demand learning experiences to analysts, we might be able to shorten the time required to become an expert.
The primary role of an analyst is to iteratively apply analysis 2 and synthesis 3 to move forward from data and backward from hypothesis and create a final product that explains the available data (Waltz 2003). Given the rapidly changing nature of the problems and threats that analysts must face, it is not surprising that they are often lacking the requisite expertise to effectively and efficiently complete the task.
Experience, about 10 years worth, is what distinguishes experts from novices (Ericsson 2006). Through experience, an expert accumulates more knowledge than the novice. In addition, the expert differs from the novice in how the knowledge is internally structured and recalled when necessary (Caneel). This accumulated knowledge leads to the development of new capacities, skills, values, understanding, and preferences.
There are many ways of accumulating the experience necessary to become an expert; the most effective of which is usually through the direct involvement in or exposure to that thing or event (Schank 2002). Sometimes, experiencing an actual event is not possible or is cost prohibitive. In that case simulations are used as an artificial (or simulated) form of experience by modeling some natural or human system. There have been many forms of simulations through the ages with the latest and most powerful being computer based. Computer based virtual reality (VR) represents the latest attempt at simulating the real world and there have been several mid-fidelity VR training systems built (Vargas 2006). When VR becomes indistinguishable from “true” reality, we will finally have a generalized simulated reality (“qVerse”) that can be used not only for play but for serious work.
The qVerse might hold the key to providing on-demand simulated first-hand experience that could be directly applied in the real-world. In this context, qVerse is defined as a universe of nested multi-dimensional immersive worlds (i.e., simulations within simulations) representing real or imaginative environments, augmented with abstract conceptual information. The qVerse would allow the analysts of the future to quickly gain the experience they need from interactive involvement in historical cases 4 to solve a new problem instead of being limited to solving problems they have the experience to solve. Let’s take a peek into the future and see how an analyst might benefit from such a paradigm shift.