Dependency Provenance in Agent Based Modeling

Researchers who use agent-based models (ABM) to model social patterns often focus on the model\´s aggregate phenomena. However, aggregation of individuals complicates the understanding of agent interactions and the uniqueness of individuals. We develop a method for tracing and capturing the provenance of individuals and their interactions in the Net Logo ABM, and from this create a "dependency provenance slice", which combines a data slice and a program slice to yield insights into the cause-effect relations among system behaviors. To cope with the large volume of fine-grained provenance traces, we propose use-inspired filters to reduce the amount of provenance, and a provenance slicing technique called "non-preprocessing provenance slicing" that directly queries over provenance traces without recovering all provenance entities and dependencies beforehand. We evaluate performance and utility using a well known ecological Net Logo model called "wolf-sheep-predation".