Our nation has seen an increased need to train its civil authorities and emergency personnel under life-threatening scenarios where human life and critical infrastructure are assumed to be at risk. This training is typically obtained or re-enforced via (human) performance-based tests. At issue is the ability to accurately simulate the scenarios without exposing personnel or human test subjects to injury. In addition, these performance-based tests carry a large monetary cost, and certain scenarios are so complicated, catastrophic or rare that any performance-based test is unrealistic. Our paper outlines the research that must be conducted to develop a framework for modeling and analyzing risk-assessment and decision making when evacuating large populations. The research is aimed at extending an existing construct for simulating passenger and crew behavior during aircraft evacuations, to larger populations, and relies upon rare-event simulation methods, paralleland-distributed simulation and agent-based simulation.
We develop a 3-D knowledge pyramid/prism model to structure the relationships of (i) lower-level learning, (ii) 'optional' knowledge bases, (iii) concurrent knowledge, and (ii) new knowledge; so one may view the learning needs of a higher-level learning objective. Our paradigm stems from Bloom's taxonomy of learning, but has the advantage of supporting 'just-in-time' and 'learn-by-doing' delivery, teaching and learning styles. We illustrate the paradigm through the BMMKP (the 3-D knowledge pyramid/prism model of the highest-level, batch-means-method learning objective for our language-focused, undergraduate course). The BMMKP reveals how highly dependent and fully integrated this learning is to calculus, probability, statistics, and queuing theory-regardless of the simulation modeling language chosen to teach in the course. The BMMKP is then used to develop a set of lower-level learning objectives for the undergraduate course. The 3-D pyramid/prism approach should lend itself well as a communication tool for visualizing other simulation learning objectives.
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