Parameterising the dynamics of inter-group conflict from real world data

Abstract

Abstract-Generative modelling of inter-group relations enables probabilistic forecasting of possible conflict for scenarios where real-world data is sparse. In order for such models to have relevance and integrity, it is important to ensure that realworld data is used to parameterise the model and verify its characteristics. In this paper we investigate how real-world datasets can be mapped into generative model parameters concerning group structures and behaviours. We highlight the issues involved and present a framework for classifying potential data based on three attributes: (i) inter-group structure, (ii) inter-group actions and (iii) impact of actions. We argue that these attributes are fundamental for benchmarking and developing generative models in the context of limited existing data on inter-group interaction.

A computational framework for modelling inter-group behaviour using psychological theory

“…70 This is also true with regard to real-world data sets, 71 and understanding the relationships between visually represented actions and system parameters (e.g., real time social network metrics) in human terms is a step which is critical in aiding consistent 'tellability' and interpretation. Users with different perceptions and experiences may well be susceptible to different types of bias in making judgements on observed changes to a scenario.…”