A cognitive hierarchical framework for evaluating emergency response activities

We describe a system model for determining decision making strategies based upon the ability to perform data mining and pattern discovery utilizing open source information to prepare for specific events or situations from multiple information sources. Within this paper, we discuss the development of a method for determining actionable information. We have integrated open source information linking to human sentiment and manipulated other user selectable interlinking relative probabilities for events based upon current knowledge. Probabilistic predictions are critical in practice on many decision making applications because optimizing the user experience requires being able to compute the expected utilities of mutually exclusive pieces of content. Hierarchy game theory for decision making is valuable where two or more agents seek their own goals, possibilities of conflicts, competition and cooperation. The quality of the knowledge extracted from the information available is restricted by complexity of the model. Hierarchy game theory framework enables complex modeling of data in probabilistic modeling. However, applicability to big data is complicated by the difficulties of inference in complex probabilistic models, and by computational constraints. We focus on applying probabilistic models to evaluating emergency response activities. We specifically evaluate adversarial competition to help decide and plan how much to give in our emergency response example to capture the position of highest donor nation using mixed probabilities from game theory. Our contribution in this paper is to combine linear programming, hierarchical game theory with uncertainty modeling as a tool in order to plan for activities based on open source intelligence.