Decision-making under uncertainty is important for managing human-natural systems in a changing world. A major source of uncertainty is linked to the multi-actor settings of decisions with poorly understood values, complex relationships, and conflicting management approaches. Despite general agreement across disciplines on co-producing knowledge for viable and inclusive outcomes in a multi-actor context, there is still limited conceptual clarity and no systematic understanding on what co-production means in decision-making under uncertainty and how it can be approached. Here, we use content analysis and clustering to systematically analyse 50 decision-making cases with multiple time and spatial scales across 26 countries and in 9 different sectors in the last decade to serve two aims. The first is to synthesise the key recurring strategies that underpin high quality decision co-production across many cases of diverse features. The second is to identify important deficits and opportunities to leverage existing strategies towards flourishing co-production in support of decision-making. We find that four general strategies emerge centred around: promoting innovation for robust and equitable decisions; broadening the span of co-production across interacting systems; fostering social learning and inclusive participation; and improving pathways to impact. Additionally, five key areas that should be addressed to improve decision co-production are identified in relation to: participation diversity; collaborative action; power relationships; governance inclusivity; and transformative change. Characterising the emergent strategies and their key areas for improvement can help guide future works towards more pluralistic and integrated science and practice.@en

Progress to date toward the Sustainable Development Goals (SDGs) has fallen short of expectations and is unlikely to fully meet 2030 targets. Past assessments have mostly focused on short- and medium-term evaluations, thus limiting the ability to explore the longer-term effects of systemic interactions with time lags and delay. Here we undertake global systems modeling with a longer-term view than previous assessments in order to explore the drivers of sustainability progress and how they could play out by 2030, 2050, and 2100 under different development pathways and quantitative targets. We find that early planning for systems change to shift from business as usual to more sustainable pathways is important for accelerating progress toward increasingly ambitious targets by 2030, 2050, and 2100. These findings indicate the importance of adopting longer-term timeframes and pathways to ensure that the necessary pre-conditions are in place for sustainability beyond the current 2030 Agenda.@en

Modeling is a crucial approach for understanding the past and exploring the future of coupled human-natural systems. However, uncertainty in various forms challenges inferences from modeling results. Model-based support for decision-making has increasingly adopted an emerging exploratory approach. This approach addresses uncertainty explicitly through systematically exploring the implications of modeling assumptions, aiming to enhance the robustness of inferences from models. Despite a variety of applications, the extent and the way(s) that exploratory modeling can deal with the challenges that arise from the uncertainty and complexity of decision-making with stakeholders has not yet been systematically framed. We address this gap in two ways. First, we present a taxonomy of the ways that exploratory modeling can be used to inform robust inferences in coupled human-natural systems by mapping the technical capabilities of this approach in relation to the diversity of past applications. This subsequently guides an investigation of the practical benefits and challenges of these capabilities in handling uncertainty and complexity. Second, we discuss different ways for integrating genuine stakeholder engagement into exploratory modeling through transdisciplinary research. Finally we outline some priorities for future expansion of this research area.

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