Agentic World Analysis (AWA) - an alternative way to explore systems and support decision making

To address increasingly pressing sustainability challenges, various approaches have been developed to foresee possible futures, identify failure modes, detect vulnerabilities, and test potential mitigations. However, environmental systems are highly complex. Especially when coupled with human processes, the scale of uncertainties becomes intractable. To address this challenge, we propose a new approach - Agentic World Analysis (AWA)- combining the strengths of simulation modelling and expert elicitation. The concept of AWA is defined by three properties: 1) AWA uses an agentic AI system to mimic an expert panel that studies the world; 2) AWA projects futures iteratively through analysing scenario trees and learning from this analysis to improve decisions; 3) AWA is auditable. Based on these requirements, we implemented the World Engine by Generative Agents (WEGA) as a possible application of the AWA approach and demonstrated its functionality with a real-world case study: the Nitrogen Crisis in the Netherlands. WEGA autonomously constructed the context, identified key stakeholders and uncertainties, created expert agents, and generated future scenarios. As a result, two pathways from 2026 to 2041 were proposed, sharing a common assumption that social acceptance of nitrogen mitigation policies is low, while differing in how successful the restoration is according to the implementation of nitrogen data monitoring. The pathways are evaluated in multiple dimensions to assess their logical coherence and quality. The evaluation also actively exposes strengths and weaknesses to provide ways for testing the validity of the policies proposed. We discussed scaling up scenario analyses to enable massive pathway exploration, the trade-offs of using AWA and other approaches, and common concerns regarding AI systems.