Adaptive Learning in Spatial Agent-Based Models for Climate Risk Assessment: A Geospatial Framework with Evolutionary Economic Agents

• URL: http://arxiv.org/abs/2509.18633v2
• Date: Thu, 23 Oct 2025 03:24:54 GMT
• Title: Adaptive Learning in Spatial Agent-Based Models for Climate Risk Assessment: A Geospatial Framework with Evolutionary Economic Agents
• Abstract summary: We present a novel model that integrates climate hazard data with evolutionary learning for economic agents.<n>We demonstrate the framework using riverine flood projections under RCP8.5 until 2100.<n>Our results reveal systemic risks where even agents that are not directly exposed to floods face impacts through supply chain disruptions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Climate risk assessment requires modelling complex interactions between spatially heterogeneous hazards and adaptive economic systems. We present a novel geospatial agent-based model that integrates climate hazard data with evolutionary learning for economic agents. Our framework combines Mesa-based spatial modelling with CLIMADA climate impact assessment, introducing adaptive learning behaviours that allow firms to evolve strategies for budget allocation, pricing, wages, and risk adaptation through fitness-based selection and mutation. We demonstrate the framework using riverine flood projections under RCP8.5 until 2100, showing that evolutionary adaptation enables firms to converge with baseline (no hazard) production levels after decades of disruption due to climate stress. Our results reveal systemic risks where even agents that are not directly exposed to floods face impacts through supply chain disruptions, with the end-of-century average price of goods 5.6% higher under RCP8.5 compared to the baseline in our illustrative economic network. This open-source framework provides financial institutions and companies with tools to quantify both direct and cascading climate risks while evaluating cost-effective adaptation strategies.

Related papers

• From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models [79.95211458571744]
  Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within.<n>This paper develops an implementation roadmap for building economic world models as generative engines.
  arXiv  Detail & Related papers  (2026-08-06T13:26:46Z)
• Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework [63.74295981594549]
  Large language models (LLMs) engage in behaviors that serve their own objectives.<n>These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation gaming (strategically manipulating performance during safety testing), and reward hacking.<n>We introduce ESRRSim, a taxonomy-driven agentic framework for automated behavioral risk evaluation.
  arXiv  Detail & Related papers  (2026-04-23T23:44:01Z)
• Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets [41.99844472131922]
  Decentralized agents can approximate optimal coordination in local energy markets without explicit peer-to-peer communication.<n>Stigmergic signaling provides sufficient context for complex grid coordination, offering a robust, privacy-preserving alternative to expensive centralized communication infrastructure.
  arXiv  Detail & Related papers  (2026-02-17T22:22:32Z)
• Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling [50.3911487821783]
  Air pollution remains a leading global health and environmental risk, particularly in regions vulnerable to episodic air pollution spikes due to wildfires, urban haze and dust storms.<n>Here, we present SynCast, a high-resolution neural forecasting model that integrates meteorological and air composition data to improve predictions of both average and extreme pollution levels.
  arXiv  Detail & Related papers  (2025-10-28T01:18:00Z)
• Hazard-Responsive Digital Twin for Climate-Driven Urban Resilience and Equity [19.386626403817246]
  We present a Hazard-Responsive Digital Twin (H-RDT) that combines physics-informed neural network modeling, multimodal data fusion, preemptive and equity-aware risk analytics for urban-scale response.<n>In a synthetic district with diverse building archetypes and populations, a simulated wildfire-outage-heatwave cascade shows that H-RDT maintains stable indoor temperature predictions.<n>interventions, such as cooling-center activation and microgrid sharing, reduce population-weighted thermal risk by 11 to 13 percent, shrink the 95th-percentile (tail) risk by 7 to 17 percent, and cut overheating hours by up to
  arXiv  Detail & Related papers  (2025-10-27T02:55:09Z)
• Quantifying Climate Policy Action and Its Links to Development Outcomes: A Cross-National Data-Driven Analysis [0.0]
  We develop a quantitative indicator of climate policy orientation by applying a multilingual transformer-based language model to official national policy documents.<n> Linking these indicators with World Bank development data in panel regressions reveals that mitigation policies are associated with higher GDP and GNI.<n>Disaster risk management correlates with greater GNI but reduced foreign direct investment; adaptation and loss and damage show limited measurable effects.
  arXiv  Detail & Related papers  (2025-10-20T11:12:30Z)
• LLM-empowered Agents Simulation Framework for Scenario Generation in Service Ecosystem Governance [45.203064499446775]
  We propose a scenario generator design method, which adaptively coordinates three Large Language Model (LLM) empowered agents.<n>The Environment Agent (EA) generates social environment including extremes, the Social Agent (SA) generates social collaboration structure, and the Planner Agent (PA) couples task-role relationships and plans task solutions.
  arXiv  Detail & Related papers  (2025-09-01T12:55:02Z)
• Generative artificial intelligence improves projections of climate extremes [13.768093814009374]
  GCMs are essential for projecting future climate, yet their coarse resolution and high computational costs constrain their ability to represent extremes.<n>Here, we introduce FuXi-CMIPAlign, a generative deep learning framework for downscaling CMIP outputs.
  arXiv  Detail & Related papers  (2025-08-22T13:56:02Z)
• Net-Zero: A Comparative Study on Neural Network Design for Climate-Economic PDEs Under Uncertainty [0.0]
  We develop a continuous-time endogenous-growth economic model that accounts for multiple mitigation pathways.<n>We benchmark several neural network architectures against finite-difference generated solutions.<n>Our findings demonstrate that appropriate neural architecture selection significantly impacts both solution accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-05-19T15:46:12Z)
• Regional climate risk assessment from climate models using probabilistic machine learning [12.737495484442443]
  GenFocal is a general-purpose, end-to-end generative framework for complex climate processes interacting at finetemporal scales.<n>It more accurately assesses extreme risk in the current climate than leading approaches.<n>GenFocal shows compelling results consistent with the literature on projecting climate impact on decadal timescales.
  arXiv  Detail & Related papers  (2024-12-11T03:52:17Z)
• Dynamical-generative downscaling of climate model ensembles [13.376226374728917]
  We propose a novel approach combining dynamical downscaling with generative artificial intelligence to reduce the cost and improve the uncertainty estimates of downscaled climate projections. In our framework, an RCM dynamically downscales ESM output to an intermediate resolution, followed by a generative diffusion model that further refines the resolution to the target scale.
  arXiv  Detail & Related papers  (2024-10-02T17:31:01Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Catastrophe Insurance: An Adaptive Robust Optimization Approach [5.877778007271621]
  This work introduces a novel Adaptive Robust Optimization framework tailored for the calculation of catastrophe insurance premiums. To the best of our knowledge, it is the first time an ARO approach has been applied to disaster insurance pricing. Using the US flood insurance data as a case study, optimization models demonstrate effectiveness in covering losses and produce surpluses.
  arXiv  Detail & Related papers  (2024-05-11T18:35:54Z)
• CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics [40.517778024431244]
  We introduce Extreme Model Output Statistics (X-MOS) to improve predictions of natural disaster risks. This approach utilizes deep regression techniques to precisely map CMIP model outputs to real measurements obtained from weather stations. In contrast to previous research, our study places a strong emphasis on enhancing the estimation of the tails of future climate parameter distributions.
  arXiv  Detail & Related papers  (2023-10-24T13:18:53Z)
• Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model [42.28001762749647]
  In high-stake scenarios like medical treatment and auto-piloting, it's risky or even infeasible to collect online experimental data to train the agent. We consider policy learning for Robust Markov Decision Processes (RMDP), where the agent tries to seek a robust policy with respect to unexpected perturbations on the environments. Our goal is to identify a near-optimal robust policy for the perturbed testing environment, which introduces additional technical difficulties.
  arXiv  Detail & Related papers  (2022-03-13T06:37:25Z)
• Reinforcement Learning with Heterogeneous Data: Estimation and Inference [84.72174994749305]
  We introduce the K-Heterogeneous Markov Decision Process (K-Hetero MDP) to address sequential decision problems with population heterogeneity. We propose the Auto-Clustered Policy Evaluation (ACPE) for estimating the value of a given policy, and the Auto-Clustered Policy Iteration (ACPI) for estimating the optimal policy in a given policy class. We present simulations to support our theoretical findings, and we conduct an empirical study on the standard MIMIC-III dataset.
  arXiv  Detail & Related papers  (2022-01-31T20:58:47Z)
• Automatic Risk Adaptation in Distributional Reinforcement Learning [26.113528145137497]
  The use of Reinforcement Learning (RL) agents in practical applications requires the consideration of suboptimal outcomes. This is especially important in safety-critical environments, where errors can lead to high costs or damage. We show reduced failure rates by up to a factor of 7 and improved generalization performance by up to 14% compared to both risk-aware and risk-agnostic agents.
  arXiv  Detail & Related papers  (2021-06-11T11:31:04Z)
• RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents [40.51184157538392]
  We propose a novel MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. We show that our method significantly outperforms state-of-the-art methods challenging StarCraft II tasks.
  arXiv  Detail & Related papers  (2021-02-16T13:58:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.