Design of policy digital twins incorporating multi-level agent based modelling

2026-07-15

AI Summary

This study addresses the challenge of effectively integrating digital twin technology into policymaking across multiple governance levels—urban, national, and supranational—particularly due to the lack of explicit modeling of dynamic human behavior and its impact on policy outcomes. To bridge this gap, the authors propose a novel policy-oriented digital twin architecture that deeply embeds multilevel agent-based modeling to explicitly represent how human behaviors shape policy effectiveness. They further develop adaptable design methodologies tailored to diverse governance contexts. The approach is demonstrated through a prototype system applied to an energy transition policy initiative by a UK city council, which successfully validates the framework’s capacity to enhance both the realism and practical utility of policy simulation.

Abstract

Digital twins are used across many industries to enable better decision making. However, while policy makers at all levels (including city, national and supranational scales) have expressed a desire to integrate digital twins into their workflows, this adoption has been slow to materialise. In this paper, we discuss the key issues associated with policy digital twins, and the ways in which they differ from, and are similar to, their counterparts in other areas. We describe how multi-level agent based modelling can be used within policy digital twins to include the effects of human behaviours on outcomes; an aspect that is often largely overlooked. We also describe how digital twins can be designed for policy use cases, and present as a case study the design of a policy digital twin incorporating multi-level agent based modelling to aid a UK city council (local authority) in delivering energy transition policy. After describing both the design method used and the resultant digital twin, we discuss the effectiveness of both, as well as how the ways in which different contexts might shape the future architecture of the digital twin.

Problem

• policy digital twins
• multi-level agent based modelling
• human behaviour
• energy transition policy
• decision making

Innovation

• policy digital twins
• multi-level agent based modelling
• human behaviour
• energy transition
• decision support