Groundwater Modeling with Stakeholders: Finding the Complexity that Matters

Article impact statement: Agent‐based models can effectively engage stakeholders in the modeling process and improve decision making in groundwater hydrology

No Stakeholder Is an Island:Human Barriers and Enablers in Participatory Environmental Modelling

Sustainability science strives to hone our ability to tackle problems that involve interconnected economic, social, and environmental systems. Addressing the root causes of these problems requires a more nuanced understanding of how human behaviour can undermine stakeholder engagement efforts towards effective conflict management and resolution. Participatory modelling—the co-production of knowledge via facilitated modelling workshops—plays a critical role in this endeavour by enabling participants to co-formulate problems and use modelling practices that aid in the description, solution, and decision-making actions of the group. While the difficulties of modelling with stakeholders are widely acknowledged, there is still a need to more concretely identify and categorize the barriers and opportunities that human behaviour presents to this type of engagement process. This review fills an important gap in participatory modelling practice by presenting five broad categories of barriers, along with strategies that can assist in overcoming them. We conclude with a series of actions and future research directions that the participatory modelling community as a whole can take to create more meaningful and behaviourally-attuned engagements that help stakeholders take concrete steps towards sustainability in natural resource management

What Prevents the Adoption of Regenerative Agriculture and What Can We Do about It? Lessons and Narratives from a Participatory Modelling Exercise in Australia

Regenerative agriculture (RegenAg) can help landholders attune their agricultural practices to the natural design of the earth’s cycles and support systems. The adoption of RegenAg, however, hinges not only on a good understanding of biophysical processes but perhaps more importantly on deep-seated values and beliefs which can become an obstacle for triggering widespread transitions towards synergistic relationships with the land. We designed and facilitated a Participatory Modelling exercise with RegenAg stakeholders in Australia—the aim was to provide a blueprint of how challenges and opportunities could be collaboratively explored in alignment with landholders’ personal views and perspectives. Fuzzy Cognitive Maps (FCM) were used to unpack and formalise landholder perspectives into a semi-quantitative shared ‘mental model’ of the barriers and enablers for adoption of RegenAg practices and to subsequently identify actions that might close the gap between the two. Five dominant narratives which encode the key drivers and pain points in the system were identified and extracted from the FCM as a way to promote the internalisation of outcomes and lessons from the engagement. The Participatory Modelling exercise revealed some of the key drivers of RegenAg in Australia, highlighting the complex forces at work and the need for coordinated actions at the institutional, social, and individual levels, across long timescales (decades). Such actions are necessary for RegenAg to play a greater role in local and regional economies and to embed balancing relationships within systems currently reliant on conventional agriculture with few internal incentives to change. Our methods and findings are relevant not only for those seeking to promote the adoption of RegenAg in Australia but also for governments and agriculturalists seeking to take a behaviorally attuned stance to engage with landholders on issues of sustainable and resilient agriculture. More broadly, the participatory process reported here demonstrates the use of bespoke virtual elicitation methods that were designed to collaborate with stakeholders under COVID-19 lockdown restrictions

Artificial Intelligence in Disaster Risk Communication: A Systematic Literature Review

Effective communication of disaster risks is crucial to provoking appropriate responses from citizens and emergency operators. With recent advancement in Artificial Intelligence (AI), several researchers have begun exploring machine learning techniques in improving disaster risk communication. This paper adopts a systematic literature approach to report on the various research activities involving the application of AI in disaster risk communication. The study found that research activities focus on two broad areas: (1) prediction and monitoring for early warning, and (2) information extraction and classification for situational awareness. These broad areas are discussed, including background information to help establish future applications of AI in disaster risk communication. The paper concludes with recommendations of several ways in which AI applications can have a broader role in disaster risk communication

Disaster Risk Communication in Culturally and Linguistically Diverse Communities: The Role of Technology

Migrants, ethnic minorities and people from culturally and linguistically diverse (CALD) communities are often more vulnerable to natural disasters due to cultural barriers and limited proficiency in the dominant language, which sometimes undermine their ability to access, interpret and respond to warnings. Technology can assist in engendering culturally and linguistically appropriate communication with CALD communities if key challenges are identified. This study contributes by reviewing relevant literature with the aim of ascertaining the most pressing challenges requiring technological interventions. Three broad issues (i.e., trust, message tailoring, and message translation) are identified and discussed, and potential solutions for addressing these issues are recommended