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

TL;DR

This paper introduces a modular multi-level agent-based architecture that integrates cognitive decision models and institutional dynamics to simulate complex climate governance processes.

Contribution

It presents a novel integrated framework combining individual motives, social influence, and institutional strategies within a unified ABM for climate governance.

Findings

Design principles and modular structure of the architecture are detailed.

The model supports scenario exploration of land-use governance.

Framework aims for empirical calibration and future validation.

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.