This dissertation aims to expand the current one-dimensional game theory based model to a multidimensional model for multi-actor predictive analytics and generalize the concept of position to address problems where actors’ positions are distributed over a position spectrum. The one-dimensional models are used for the problems where actors are interacting in a single issue space only. This is less than an ideal assumption since, in most cases, players’ strategies may depend on the dynamics of multiple issues when dealing with other players. In this research, the one-dimensional model is expanded to N-Dimensional model by considering different positions, and separate salience values, across different axes for the players. The model predicts the outcome for a given problem by taking into account stakeholder’s positions in different dimensions and their conflicting perspectives. Furthermore, we generalize the concept of position in the model to include continuous positions for the actors throughout the position spectrum, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between positions of actors. The proposed models are able to attain the same results as the previous one-dimensional models. In addition, to illustrate the capability of the proposed models, multiple case studies are designed and examined to assess the models’ capability and explainability.