Bayesian Multi-Agent Learning for Uncertainty-Aware Financial Decision Making
Keywords:
Bayesian learning; multi-agent systems; financial decision making; uncertainty quantification; market regime shifts; AI governance; systemic risk; socio-technical infrastructureAbstract
Financial decision making increasingly depends on distributed artificial intelligence systems that operate in nonstationary, adversarial, and partially observable environments. Bayesian multi-agent learning offers a principled framework for representing uncertainty, coordinating heterogeneous actors, and adapting to market regime shifts, yet its translation into financial infrastructure raises structural questions about architecture, governance, robustness, fairness, and sustainability. This paper develops a system-level analysis of uncertainty-aware financial decision making by integrating Bayesian inference, multi-agent reinforcement learning, and socio-technical governance perspectives. It argues that uncertainty quantification should not be treated as a purely statistical add-on but as an architectural and institutional property that shapes how financial agents communicate, escalate decisions, and allocate risk. The discussion examines centralized and decentralized coordination trade-offs, the role of trust calibration under regime change, the limits of regulatory frameworks designed for static models, and the distributional consequences of deploying adaptive agents in credit, trading, insurance, and treasury functions. It also considers operational resilience, data infrastructure, model auditability, and cross-border policy coordination. The paper concludes that Bayesian multi-agent learning can improve financial resilience only when coupled with calibrated uncertainty reporting, transparent escalation pathways, and governance mechanisms that preserve human accountability. Without such coupling, uncertainty-aware agents may amplify correlated behavior, obscure responsibility, and intensify systemic fragility. The analysis contributes a forward-looking research agenda for designing financial intelligence as a governed, sustainable, and socially legible infrastructure rather than as a collection of isolated predictive models.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
