A Multi-Level Agent-Based Architecture for Climate Governance Integrating Cognitive and Institutional Dynamics

2026-05-08

AI Summary

Existing climate governance models often decouple institutional dynamics from individual behavior, failing to capture the complex interactions among diverse actors. This study proposes a novel multilevel agent-based model (ABM) that systematically integrates empirically grounded cognitive decision-making mechanisms—derived from the HUMAT and MOA frameworks—with homophilic social network effects and institutional strategy modules representing environmental organizations, media, and politicians. Political decision processes are simulated through multi-source signal aggregation. The model is calibrated using synthetic population data and parameterized through stakeholder engagement in a Living Lab setting, yielding a modular, scalable, and empirically verifiable simulation platform capable of dynamic modeling and scenario analysis of land-use policies under democratic climate governance.

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
• cognitive dynamics
• institutional dynamics
• multi-level integration

Innovation

• agent-based modeling
• cognitive decision-making
• institutional dynamics
• climate governance
• multi-level architecture