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Scientific modelling is a prime means to generate understanding and provide much-needed information to support public decision-making in the fluid area of sustainability. A growing, diverse sustainability modelling literature, however, does not readily lend itself to standard validation procedures, which are typically rooted in the positivist principles of empirical verification and predictive success. Yet, to be useful to decision-makers, models, including their outputs and the processes through which they are established must be, and must be seen to be “valid.” This study explores what model validity means in a problem space with increasingly interlinked and fast-moving challenges. We examine validation perspectives through ontological, epistemic, and methodological lenses, for a range of modelling approaches that can be considered as “complexity-compatible.” The worldview taken in complexity-compatible modelling departs from the more standard modelling assumptions of complete objectivity and full predictability. Drawing on different insights from complexity science, systems thinking, economics, and mathematics, we suggest a ten-dimensional framework for progressing on model validity when investigating sustainability concerns. As such, we develop a widened view of the meaning of model validity for sustainability. It includes (i) acknowledging that several facets of validation are critical for the successful modelling of the sustainability of complex systems; (ii) tackling the thorny issues of uncertainty, subjectivity, and unpredictability; (iii) exploring the realism of model assumptions and mechanisms; (iv) embracing the role of stakeholder engagement and scrutiny throughout the modelling process; and (v) considering model purpose when assessing model validity. We wish to widen the debate on the meaning of model validity in a constructive way. We conclude that consideration of all these elements is necessary to enable sustainability models to support, more effectively, decision-making for complex interdependent systems.

Recently, there has been a strong interest in the climate emergency and the human health impacts of climate change. Although estimates have been quoted, the modeling methods used have either been simplistic or opaque, making it difficult for policy makers to have confidence in these estimates. Providing central estimates of health impacts, without any quantification of their uncertainty, is deficient because such an approach does not acknowledge the inherent uncertainty in extreme environmental exposures associated with spiraling climate change and related health impacts. Furthermore, presenting only the uncertainty bounds around central estimates, without information on how the uncertainty in each of the model parameters and assumptions contribute to the total uncertainty, is insufficient because this approach hides those parameters and assumptions which contribute most to the total uncertainty. We propose a framework for calculating the catastrophic human health impacts of spiraling climate change and the associated uncertainties. Our framework comprises three building blocks: (A) a climate model to simulate the environmental exposure extremes of spiraling climate change; (B) a health impact model which estimates the health burdens of the extremes of environmental exposures; and (C) an analytical mathematical method which characterizes the uncertainty in (A) and (B), propagates the uncertainty in-between and through these models, and attributes the proportion of uncertainty in the health outcomes to model assumptions and parameter values. Once applied, our framework can be of significant value to policy makers because it handles uncertainty transparently while taking into account the complex interactions between climate and human health.

This systematic review aims to synthesize how agent-based modeling (ABM) has been used in migration and modern slavery research and provide the basis to model development for social science researchers exploring the use of ABM. We searched five bibliographic databases using two terminology categories: (1) migration or modern slavery terminology; (2) complex system methods terminology. Two reviewers conducted independent article screening. Peer-reviewed articles presenting original migration or modern slavery ABMs were included. Data extraction included model development steps and model characteristics. The dataset was synthesized and compared across studies. We identified 28 articles for inclusion. Many of the ABMs tested theories and about half were based on empirical data. Model development varied considerably and reported methods were extremely opaque. Only five studies used a structured development framework. The most common model involved agents deciding whether and where to migrate and attempting migration. Climate change was a common exogenous scenario modeled. Most of the ABMs did not undergo any sensitivity analysis or validation.ABM has a greater capacity to account for heterogeneous and dynamic decision-making than more frequently applied methods in research on migration and modern slavery. However, there is still a paucity of studies adopting ABM methods. These reviewed ABMs highlight gaps in the reporting and implementing of model development. ABM is a promising technique to address many urgent and complex questions in research on migration and modern slavery to better support decision-makers, but addressing current methodological gaps is a critical first step.