Tremblay Sébastien, Gagnon Jean-François, Lafond Daniel, Hodgetts Helen M, Doiron Maxime, Jeuniaux Patrick P J M H
École de psychologie, Université Laval, Québec, Canada.
Thales Research & Technology Canada, Québec City, Québec, Canada.
Appl Ergon. 2017 Jan;58:349-360. doi: 10.1016/j.apergo.2016.07.009. Epub 2016 Aug 5.
While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies - using simplified as opposed to analytic decision rules - have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called "IMAGE" to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed.
虽然简单的启发式方法在自然主义决策情境中可能具有生态合理性和有效性,但复杂情况需要分析性决策策略、假设检验和学习。在医疗保健、军事作战规划和政府决策等领域,已经报道了次优决策策略——使用简化而非分析性决策规则。我们研究了一种名为“IMAGE”的计算工具包通过发展结构化知识和增强对复杂情况的理解来改善决策的潜力。通过(a)交互式模拟以及(b)可视化和知识表示能力,在复杂的军事车队管理任务背景下对IMAGE进行了测试。我们将IMAGE的两个版本(桌面版和沉浸式版)与基线进行比较,评估其有用性。结果表明,该工具帮助分析人员做出了更好的决策,但一旦移除认知工具,就未能增加他们对情况的结构化知识。