Towards Qualitative Reasoning for Policy Decision Support in Demonstrations

Abstract

In this paper we describe a method for modeling social behavior of large groups, and apply it to the problem of predicting potential violence during demonstrations. We use qualitative reasoning techniques which to our knowledge have never been applied to modeling crowd behaviors, nor in particular to demonstrations. Such modeling may not only contribute to the police decision making process, but can also provide a great opportunity to test existing theories in social science. We incrementally present and compare three qualitative models, based on social science theories. The results show that while two of these models fail to predict the outcomes of real-world events reported and analyzed in the literature, one model provide a good results. Moreover, in this paper we examine whether machine learning techniques such as decision trees may provide better predictions than QR models. While the results show that the machine learning techniques provide accurate predictions, a slightly better prediction than our QR model, we claim that QR approach is sensitive to changes in contrast to decision tree, and can account for what if scenarios. Thus, using QR approach is better for reasoning regarding the potential violence level to improve the police decision making process.