Multi-agent Financial Systems with RL: A Pension Ecosystem Case

This paper introduces a multi-agent reinforcement learning (MARL) model for the pension ecosystem, aiming to optimise the contributor’s saving and investment strategies. The multi-agent approach enables the examination of endogenous and exogenous shocks, business cycle impacts, and policy decisions on contributor behaviour. The model generates synthetic income trajectories to develop inclusive savings strategies for a broad population. Additionally, this research innovates by developing a multi-agent model capable of adapting to various environmental changes, contrasting with traditional econometric models that assume stationary employment and market dynamics. The non-stationary nature of the model allows for a more realistic representation of economic systems, enabling a better understanding of the complex interplay between agents and their responses to evolving economic conditions (A variation of this article was included as a chapter in the PhD Thesis of Ozhamaratli, F. submitted on 22 Jan 2024).