The achievement of global sustainability agendas, such as the Sustainable Development Goals, relies on transformational change across society, economy, and environment that are co‐created in a transdisciplinary exercise by all stakeholders. Within this context, environmental and societal change is increasingly understood and represented via participatory modeling for genuine engagement with multiple collaborators in the modeling process. Despite the diversity of participatory modeling methods to promote engagement and co‐creation, it remains uncertain what the extent and modes of participation are in different contexts, and how to select the suitable methods to use in a given situation. Based on a review of available methods and specification of potential contextual requirements, we propose a unifying framework to guide how collaborators of different backgrounds can work together and evaluate the suitability of participatory modeling methods for co‐creating sustainability pathways. The evaluation of method suitability promises the integration of concepts and approaches necessary to address the complexities of problems at hand while ensuring robust methodologies based on well‐tested evidence and negotiated among participants. Using two illustrative case studies, we demonstrate how to explore and evaluate the choice of methods for participatory modeling in varying contexts. The insights gained can inform creative participatory approaches to pathway development through tailored combinations of methods that best serve the specific sustainability context of particular case studies.

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decision-making efficiency. This paper introduces Reinforcement Learning (RL) that employs closed-loop control as a new exploration method that enables automated adaptive policy-making for planning under uncertainty. To investigate its performance, we compare RL with a widely-used exploration method, Multi-Objective Evolutionary Algorithm (MOEA), in two hypothetical problems via computational experiments. Our results indicate the complementarity of the two methods. RL makes better use of its exploration history, hence always providing higher efficiency and providing better policy robustness in the presence of parameter uncertainty. MOEA quantifies objective uncertainty in a more intuitive way, hence providing better robustness to objective uncertainty. These findings will help researchers choose appropriate methods in different applications.

A shift towards sustainable urban water management is widely advocated but poorly understood. There is a growing body of literature claiming that social learning is of high importance in restructuring conventional systems. In particular, governance experimentation, which explicitly aims for social learning, has been suggested as an approach for enabling the translation of sustainability ideas into practice. This type of experimentation requires a very different dynamic within societal relations and necessitates a changed role for professionals engaged in such a process. This empirically focused paper investigates a contemporary governance experiment, the Cooks River Sustainability Initiative, and determines its outcome in terms of enabling social learning for attaining sustainable water practice in an urban catchment. Drawing on the qualitative insights of the actors directly involved in this novel process, this paper provides evidence of changes in individual and collective understanding generated through diverse forms of social interaction. Furthermore, the research reveals perceived key-factors that foster and/or hamper the execution of this new form of experimentation, including project complexity, resource intensity and leadership. Overall, this paper highlights that, while implementation of governance experimentation in a conventional setting can be highly challenging, it can also be highly rewarding in terms of learning.