Towards Context-Aware, Real Time and Autonomous Decision Making Using Information Aggregation and Network Analytics

We consider the problem of real-time, proactive de- cision 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 function- alities 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 strategically- informed and well-aligned decisions towards the desired outcome of decision events. I. INTRODUCTION Modern decision making scenarios are characterized by large amounts of data and information that arrive dynamically, over a short period of time, from multiple sources. Processing this data in a time-critical manner to make accurate decisions is an overwhelming task for human decision makers. Over the past few decades several decision making solutions have been proposed to aid human decision makers with tools such as intelligent or automated software that use computational methods and mathematical models of human cognitive pro- cesses to make sophisticated decisions on behalf of humans(3), (12). However, most existing decision support tools provide only limited context awareness of the decision process to the decision maker in rapidly evolving, information-rich and time- critical scenarios. This reduces the efficiency of human deci- sion makers in making accurate decisions, and, consequently, could result in erroneous decision making in critical situations. Therefore, it makes sense to investigate techniques that could alleviate the human decision makers' context awareness by presenting information relevant to the decision making process, precisely and in a timely manner, to the decision maker. To address this problem, we present the framework of a context-aware, real-time, decision making system called the CONRAD (CONtext aware Real-time Adaptive Decision making) system that focuses on enabling and enhancing the capabilities of human decision makers by developing proactive decision-aids for making high-accuracy, time-critical decisions in complex, data- and information-rich environments. The central research problem that CONRAD proposes to answer is as follows:Given a set of decision variables in the current decision context along with a set of data sources from which the decision variables can be derived and/or calculated and updated, what is a suitable set of techniques for (i) extracting relevant information, (ii) then using that information to update, correlate and aggregate the decision variables dynamically, and finally , (iii) assessing the quality of the aggregated decision outcome (prediction), so that the divergence between the aggregated decision outcome (predictions) and the desired decision outcome is successively minimized?