Digital Twin-Based Simulation for Multi-Agent Financial Risk Decision Making

Abstract

Digital twin-based simulation is emerging as a systems-level approach for understanding financial risk when markets are populated by heterogeneous algorithmic agents, human traders, institutions, and regulators. This paper examines how digital twins can support multi-agent financial risk decision making by integrating market infrastructure, agent behavior, regime detection, governance constraints, and stress-testing scenarios into a continuously updating computational representation. It argues that the central contribution of digital twins is not merely predictive accuracy but structural observability: the capacity to trace how decisions, incentives, data flows, and market mechanisms interact under normal and stressed conditions. The discussion emphasizes architecture, trust calibration, fairness, robustness, sustainability, and policy implications. It considers how multi-agent simulation can model liquidity fragmentation, herding, contagion, flash events, and regulatory intervention while acknowledging limits related to validation, data quality, adversarial behavior, and institutional adoption. Particular attention is given to market regime shifts, where historical regularities weaken and agent strategies must be recalibrated. The paper concludes that digital twin ecosystems should be governed as socio-technical infrastructures, with transparent assumptions, auditable decision pathways, and participatory oversight. Without such governance, digital twins risk becoming sophisticated but opaque instruments that concentrate power and obscure accountability. With appropriate design, however, they can improve systemic risk preparedness, support adaptive regulation, and enable more resilient financial decision making across public and private institutions.