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One central goal of decision theory is to provide a rational basis for decisionmaking. The ADDAM (Adaptive Dynamic Decision Aiding Methodology) system is designed to aid the decisionmaker (DM) in performing dynamic decision tasks. The ADDAM system provides real-time dynamic assessments of multiple utilities as the DM performs a dynamic decision task. ADDAM continuously tracks the DM's decision responses and uses adaptive pattern classification techniques to learn his utilities for their outcomes. These utilities are then used to provide decision aiding in the form of maximum expected utility decision recommendations. An experimental study was conducted to evaluate the effectiveness of the decision aiding system in a realistic decision task. Aided subjects showed significantly less deviation from their own maximum expected utility and substantially less within group-variance than did unaided subjects. Aided subjects also had a greater decision output. The adaptive utility estimates upon which the aiding was based converged rapidly to stable values. The ADDAM system was found to provide an appropriate, systematic, and testable approach to decision aiding. ADDAM aids the operator by organizing his own in-context decision behavior into a systematic mathematical framework. Such aiding is applicable to a wide variety of systems in which deficiencies of human decisionmakers may be overcome by techniques to augment human memory and logic processes.