SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models

System dynamics models can make AI training more realistic: The paper demonstrates that combining system dynamics (a field focused on modeling complex, real-world systems) with reinforcement learning creates more sophisticated training environments. This addresses a key gap in responsible AI: traditional testing methods often fail to capture how algorithmic decisions play out over time in complex, adaptive systems like society.

Low-code tools can democratize responsible AI evaluation: SDGym's "low-code" design means researchers without deep technical expertise can build custom environments to test AI decisions. This is significant for responsible AI because it enables broader participation in evaluating whether algorithms will actually work well in real-world conditions before deployment.

Long-term societal impacts can now be better simulated: The paper explicitly frames this work as addressing "the long-term impact of algorithmic interventions on society." By using the electric vehicle adoption problem as a test case, the authors show how RL agents can be trained and evaluated in environments that reflect real economic and social dynamics, rather than simplified laboratory conditions.

Collaboration between two research fields strengthens AI safety: The paper proposes that system dynamics (which emphasizes collaborative model-building with stakeholders) and reinforcement learning (which optimizes decisions) can work together. This cross-disciplinary approach helps ensure AI systems are tested against models that incorporate diverse perspectives on how the world actually works.