Utilizing Scientific Models for Policy Forecasting

To create good policy you need responsible foresight, enabling ethical, sustainble, accountable future design. AI now can massively enable human-centered responsible foresight, in helping address uncertainty, assess risks, and set policies for creating better futures. María Pérez Ortiz's new paper "From Prediction to Foresight: The Role of AI in Designing Responsible Futures" describes responsible foresight in policy and the role of computational foresight tools. Notable approaches to using AI in responsible foresight include: 🤝 Participatory Futures for Inclusive Planning. Engaging diverse stakeholders in foresight practices democratizes the future-planning process. AI tools streamline public participation by analyzing preferences, simulating collective decisions, and creating urban plans that reflect community values, fostering equity and resilience. 🧠 Superforecasting for Precision and Insight. Superforecasting uses disciplined reasoning and probabilistic thinking to predict uncertain events. AI-powered assistants improve human forecasting accuracy by 23%, aggregating data and refining predictions through collective intelligence and advanced analytical models. 🌐 World Simulation for Systemic Insights. Advanced modeling frameworks simulate interconnected global systems, enabling policymakers to test "what-if" scenarios. AI accelerates these simulations, providing precise forecasts and dynamic platforms to visualize the long-term consequences of policy decisions across economic, social, and environmental domains. ⚙️ Simulation Intelligence for Decision Optimization. By integrating AI with high-fidelity simulations, simulation intelligence explores complex systems to uncover optimal strategies. This tool assists in crafting effective policies for urban planning, sustainable agriculture, and climate resilience, offering actionable pathways for addressing systemic challenges. 📜 AI-Assisted Narrative Techniques. Large language models contribute to speculative futures by generating detailed "value scenarios" that integrate ethical, technological, and societal considerations. These AI-driven narratives enable policymakers to visualize desirable outcomes and evaluate potential trade-offs. 🔗 Hybrid Intelligence for Enhanced Foresight. Combining human creativity with AI’s computational strengths creates a robust foresight framework. Intuitive interfaces, explainable AI, and participatory design ensure that tools remain transparent and aligned with ethical considerations, empowering policymakers to navigate complex challenges collaboratively. ♻️ Iterative Foresight with Feedback Loops. Continuous monitoring and real-time adaptation enhance foresight processes. AI’s ability to process evolving data and generate actionable insights ensures policies remain responsive, flexible, and aligned with long-term objectives. The power of AI in assisting foresight is just beginning to come to fruition.

🌍 Predicting the way forward for the Global Biodiversity Framework A new paper led by Damaris Zurell and many fantastic colleagues GEO BON https://lnkd.in/euzPSQjd The world has rallied behind the UN Biodiversity Kunming–Montreal Global Biodiversity Framework (GBF) — a landmark plan to halt biodiversity loss by 2030. We point out that the GBF's indicators track what’s already happened (aka, lagging indicators) — yet offer little insight into whether today’s actions will actually secure nature’s future. The risk is that we are looking only in the rear-view mirror. 🔮 The solution? Bring prediction to the heart of biodiversity policy. Just as climate science uses models to forecast global temperatures, extreme events, and guide emissions targets, conservation needs predictive models to test strategies before they fail. These models can: Link actions to outcomes — showing which conservation measures will work where; Balance ecological goals with economic and social realities. Anticipate time lags and cross-border impacts. Highlight data gaps and guide smarter monitoring. Provide the basis for leading indicators. To make this shift, we propose a World Biodiversity Research Programme (WBRP) — a coordinated global effort, akin to the World Climate Research Programme, to standardize and advance biodiversity modeling. Without such foresight, the GBF could end up documenting decline instead of preventing it. With it, we could turn from "writing nature’s obituary to crafting its recovery." #Biodiversity #Conservation #SciencePolicy #PredictiveModeling #Sustainability #GBF #NaturePositive

Are Climate Model Forecasts Useful for Policy Making? 🫠 Effect of Variable Choice on Reliability and Predictive Validity. For a model to be useful for policy decisions, statistical fit is insufficient. Evidence that the model provides out-of-estimation-sample forecasts that are more accurate and reliable than those from plausible alternative models, including a simple benchmark, is necessary. The UN’s IPCC advises governments with forecasts of global average temperature drawn from models based on hypotheses of causality. Specifically, manmade warming principally from car bon dioxide emissions (Anthro) tempered by the effects of volcanic eruptions (Volcanic) and by variations in the Sun’s energy (Solar). Out-of-sample forecasts from that model, with and without the IPCC’s favoured measure of Solar, were compared with forecasts from models that excluded human influence and included Volcanic and one of two independent measures of Solar. The models were used to forecast Northern Hemisphere land temperatures and—to avoid urban heat island effects—rural only temperatures. Benchmark forecasts were obtained by extrapolating estimation sample median temperatures. The independent solar models reduced forecast errors relative to those of the benchmark model for all eight combinations of four estimation periods and the two temperature variables tested. The models that included the IPCC’s Anthro variable reduced errors for only three of the eight combinations and produced extreme forecast errors from most model estimation periods. The mean correlation between estimation sample statistical fit and forecast accuracy was -0.30. Further tests might identify better models: Only one extrapolation model and only two of many possible independent solar models were tested, and combinations of forecasts from different methods were not examined. The anthropogenic models’ unreliability would appear to void policy relevance. In practice, even the models validated in this study may fail to improve accuracy relative to naïve forecasts due to uncertainty over the future causal variable values. Our findings emphasise that out-of-sample forecast errors, not statistical fit, should be used to choose between models (hypotheses).    https://lnkd.in/dqZpUgXe

Why Do Experts Keep Getting Politics Wrong? Traditional models assume humans are rational. They’re not — and that’s exactly why predictions fail. Our new research introduces the Multilevel Political Dynamics Framework (MPDF) — a model that finally embraces the messy reality of human decision-making. 🔬 Key Innovation — The Cognitive Index (CI): A “mental battery score” that tracks stress, fatigue & information overload. 👉 Counterintuitive breakthrough: when CI drops below 40, people become more predictable — and forecasting becomes more accurate. This approach correctly assessed the chances of the Iran nuclear deal and the Paris Agreement — where most models failed. 💡 Perhaps the future won’t be predicted by assuming perfect rationality… but by finally measuring all the ways we aren’t. Let’s talk if you’re working on political forecasting, diplomacy, or behavioral modeling. Raul Villamarin Rodriguez|Dr. Naveen Kolloju|Woxsen University|AI Research Centre - Woxsen University #MPDF #PoliticalScience #Forecasting #BehavioralEconomics #Diplomacy #NewResearch

What if climate simulations could be fast, accurate and interpretable? 🌍⚡ This paper explores exactly that.” In the series of Exploring #SciML Papers from our research wing Vizuara Technologies Private Limited , We will now discuss paper by Tirtha Tilak Pani et al. Climate modeling sits at the intersection of science, policy, and urgent global action. Traditional Earth System Models (ESMs) offer detailed, realistic simulations, but they demand immense computational resources — making rapid scenario exploration difficult. On the other hand, simplified models like Energy Balance Models (EBMs) are efficient but often miss key dynamics. 🔍 What we did: Our work proposes a novel framework that bridges this gap by integrating Scientific Machine Learning (SciML) with classical climate–carbon cycle models to achieve both high accuracy and mechanistic interpretability. Key highlights include: 1) Built a hybrid modeling framework using Universal Differential Equations (UDEs) that embeds physical laws with neural networks. 2) Achieved <0.2% error across key climate variables , outperforming purely data-driven Neural ODEs and classic statistical baselines like ARIMA and VAR. 3) Employed symbolic regression to extract interpretable equations, ensuring transparency and explainability, critical for science-based policy decisions. 4) Designed for computational efficiency, enabling fast exploration of emission scenarios under limited data conditions. 📊 Why it matters: This research shows that with the right SciML integration, we can rapidly simulate climate dynamics, maintain physical fidelity, and generate interpretable insights, all without the computational burden of full-scale Earth system models. This has promising implications for climate risk assessment, policy testing, and scenario planning. 🔗 Read the full paper here ��� https://lnkd.in/dd5suuFf Huge thanks to everyone involved, looking forward to feedback and collaboration! 🙌 Attaching the paper below as well. #ClimateScience #MachineLearning #ScientificML #ClimateModeling #DataScience #UDE #NeuralODE #AAAI #AAAIWorkshop #AI #ML #Climate #SciML