We consider the problem of real-time, proactive decision making for dynamic and time-critical decision-events where the choices made for multiple, individual decisions over time determine the final decision outcome of an event. We posit that the quality of such individual decisions can be significantly improved if human decision makers are provided with decision aids in the form of dynamically updated information and dependencies between the different decision variables, and the humans affecting those decision variables. In this position paper, we propose the CONRAD (CONtext aware Real-time Adaptive Decision making) system that uses computational techniques from large scale network analysis and game theory-based distributed information aggregation to develop such decision aids. CONRAD’s functionalities are implemented through three subsystems-a decision making subsystem that updates and mathematically combines information from different decision variables to predict the outcome of the decision event, a decision assessment subsystem that uses the currently predicted decision outcome to estimate the future decision trajectory and recommends information collection-related actions to the human decision maker, and, a network analysis subsystem that uses those recommended actions to dynamically update the dependencies and correlations between events and people influencing the decision variables. To the best of our knowledge, our work is one of the first attempts towards combining dynamic decision updates and using the predicted decision trajectory as a proactive feedback mechanism to dynamically update the correlations between decision variables so that human decision makers can make more strategicallyinformed and well-aligned decisions towards the desired outcome of decision events.