Research on judgment and decision making has reliably shown that humans utilize heuristic strategies when faced with stressful decision making tasks and complex environments. These heuristic strategies are a common paradigm in expert decision making and often help experienced operators make fast and accurate decisions by ignoring pieces of information. This thesis asserts that it is possible to provide decision support specifically tailored for heuristic decision making as well as identify heuristic decision making in real time; and if tailored support for heuristic decision making is offered, it will improve performance and decrease workload. To that end, this thesis will answer four research questions regarding decision making in time sensitive environments with incomplete information. Namely, what factors of information distribution most measurably impact decision making performance, and how are analytic and heuristic decision makers specifically affected? Can the manipulation of information distribution be used to successfully support heuristic decision making strategies? Can standard machine learning techniques identify the decision strategy employed by a decision maker in real time? And finally, can tailored decision support decrease the decision maker’s workload while improving their decision outcomes (accuracy, time-to-choose, etc.)?