Abstract
Agent-based modeling tools commonly trade off usability against power and vice versa. On the one hand, full development environments like NetLogo feature a shallow learning curve, but have a relatively limited proprietary language. Others written in Python or Matlab, for example, have the advantage of a full-featured language with a robust community of third-party libraries, but are typically more skeletal and require more setup and boilerplate in order to write a model. Helipad is introduced to fill this gap. Helipad is a new agent-based modeling framework for Python with the goal of a shallow learning curve, extensive flexibility, minimal boilerplate, and powerful yet easy to set up visualization, in a full Python environment. We summarize Helipad’s general architecture and capabilities, and briefly preview a variety of models from a variety of disciplines, including multilevel models, matching models, network models, spatial models, and others.