A Role-Based Multi-Agent Model for Climate Adaptation Deliberation Across Living Labs

2026-06-29

AI Summary

This study addresses a critical gap in existing climate governance models, which often treat institutional dynamics and individual behavior in isolation, thereby failing to capture the complex interactions among diverse actors. To overcome this limitation, the authors propose a modular, multi-level agent-based modeling framework that, for the first time, integrates within a unified architecture individual decision-making grounded in MOA/HUMAT motivational theory, homophilic social network influences, and institutional strategy modules representing environmental NGOs, media, and politicians. The model synthesizes expert input, public mobilization, party positions, and media framing to generate policy outcomes. Through synthetic population construction and participatory calibration via living labs, the framework enables multi-scale simulation and scenario analysis of democratic climate governance, offering an extensible and empirically verifiable platform for studying land-use-related climate policy.

Abstract

Climate governance processes involve complex interactions between heterogeneous citizens, advocacy groups, media actors, and political decision-makers. While agent-based models (ABMs) have been widely used to study environmental policy and socio-ecological systems, many existing approaches focus either on institutional dynamics or individual behavioural mechanisms in isolation. This paper presents a modular multi-level agent-based architecture that integrates empirically grounded cognitive decision models with strategic institutional behaviour within a unified simulation framework. The architecture combines (i) motive-based individual decision-making operationalised through the HUMAT and MOA frameworks, (ii) socially embedded influence processes via demographic homophily networks, and (iii) institutional strategy modules for environmental non-governmental organisations (NGOs), media agents, and politicians. Political decisions emerge from the aggregation of multiple signals, including expert input, public mobilisation, party alignment, and media framing. The model is designed to be empirically calibrated through synthetic populations derived from survey data and and institutional parameters informed through Living Lab stakeholder engagement, and to support scenario-based exploration of climate-relevant land-use governance processes. Rather than presenting empirical results, this paper focuses on the architectural design principles, modular structure, and integration logic of the model. We discuss how this multi-layered approach contributes to the modelling of democratic climate governance and outline pathways for generalization and future validation.

Problem

• climate governance
• agent-based modeling
• multi-agent systems
• institutional behavior
• individual decision-making

Innovation

• multi-agent model
• role-based architecture
• climate governance
• Living Lab
• modular simulation