Abstract
Agent-Based Models (ABMs) assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS) library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking) and storage requirements for different capture configurations.
Highlights
Agent-based models (ABMs) describe systems of entities capable of acting in and perceiving their environment [1]
We evaluate the ProvMASS technique using provenance queries and performance measures
The queries have been made available with sample data, as well as the simulations and provenance capture software
Summary
Agent-based models (ABMs) describe systems of entities capable of acting in and perceiving their environment [1]. The goal of an ABM is to search for explanatory insight into the collective behavior of agents (which may not be “intelligent” but obey simple rules). These agents typically simulate natural systems, rather than solving specific practical or engineering problems. ABMs facilitate a micro-simulation that models microscopic events (e.g., traffic simulation involving traffic lights, constructions, and pedestrian movements) and observe an emergent collective group behavior of many agents. Such events may be modeled at fine-granularity, yet centrally coordinated at large-scale (e.g., hundreds of thousands or even millions of agents). We focus on agent-based modeling rather than spatial simulation
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