Quo vadis, agent-based modelling tools?

Agent-based models (ABMs) are an increasingly popular choice for simulating large systems of interacting components, and have been applied across a wide variety of natural and environmental systems. However, ABMs can be incredibly disparate and often opaque in their formulation, implementation, and analysis. This can impede critical assessment and re-implementation, and jeopardize the reproducibility and conclusions of ABM studies. In this review, we survey recent work towards standardization in ABM methodology in several aspects: model description and documentation, model implementation, and model analysis and inference. Based on a critical review of the literature, focused on ABMs of environmental and natural systems, we describe a recurrent trade-off between flexibility and standardization in ABM methodology. We find that standard protocols for model documentation are beginning to establish, although their uptake by the ABM community is inhibited by their sometimes excessive level of detail. We highlight how implementation options now exist at all points along a spectrum from ad hoc, 'from scratch' implementations, to specific software offering 'off-the-shelf' ABM implementations. We outline how the main focal points of ABM analysis (behavioural and inferential analysis) are facing similar issues with similar approaches. While this active development of ABM analysis techniques brings additional methods to our analysis toolbox, it does not contribute to the development of a standardized framework, since the performance and design of these methods tends to be highly problem-specific. We therefore recommend that agent-based modellers should consider multiple approaches simultaneously when analysing their model. Well-documented software packages, and critical comparative reviews of such, will be important facilitators in these advances. ABMs can additionally make better use of developments in other fields working with high-dimensional problems, such as Bayesian statistics and machine learning.