Multi-agent Spatial Simulation Research Articles

Multiagent Spatial Simulation of Autonomous Taxis for Urban Commute: Travel Economics and Environmental Impacts

AbstractWith the likelihood of autonomous vehicle technologies in public transport and taxi systems increasing, their impact on commuting in real-world road networks is insufficiently studied. In t...

Data Provenance for Agent-Based Models in a Distributed Memory

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.