Building Standards for Agent-Based Models: A Proposal of Guidelines for Decision-Making on the Definition of Parameters and Sensitivity Analysis Methods

Agent-based models (ABMs) require critical decisions regarding the selection of parameters and probability distributions, so that simulations properly represent the phenomena under investigation. In practice, however, researchers employ diverse strategies to approach the challenges, and no shared standard exists for parameterization or for validation through sensitivity analysis. This paper contributes to the literature by synthesizing the current state of the art and proposing guidelines for the decision-making processes involved in the definition of two key elements in the construction of functional ABMs, i.e., parameters and probability distributions, in addition to their validation through sensitivity analysis. A scoping literature review focusing on construction, application, and protocols for agent-based modeling in the social sciences was conducted, followed by a critical synthesis of studies to extract strategies and generate recommendations on the subject. Drawing on this synthesis, we develop a Methodological Alignment Index that quantifies the fit between a model’s stated objective and its parameterization and sensitivity-analysis choices, complemented by a visual evidence map combining keyword co-occurrence and cross-tabulation analyses. The findings indicate an absence of standardization in the definition of parameters and probability distributions within ABM research, and a structural misalignment between model purpose and methodological rigor: only about a third of the studies reviewed adopt methods aligned with their stated objective, a gap that has persisted over the past decade. Decision-support and predictive models are prominent in the literature, yet they frequently lack robust parameterization strategies or advanced sensitivity-analysis techniques. These findings emphasize the need for standardized guidelines to align methodological choices with model objectives in ABM applications within the social sciences. In response to this gap, we present a framework for the selection of parameters and probability distributions in the development of ABMs—covering decision-making guidelines, validation strategies, and documentation standards to enhance reproducibility—and demonstrate its application on independent published cases from the reviewed corpus.