Democratic Resilience: Collective Decision Making in Multi-Agent AI Systems

As AI systems increasingly participate in decision-making processes that affect societal outcomes, questions arise about the nature and integrity of their collective behavior [1]. While human governance systems have long grappled with balancing power, fairness, and representation, AI collectives, whether in autonomous vehicles, distributed policy engines, or multi-agent simulations are often governed by rigid algorithms that lack embedded democratic safeguards. This project explores how collective AI systems respond to changes in structure and incentives, with a focus on democratic resilience. By introducing variables such as agent diversity, unequal voting power, and adversarial actors, we analyze the conditions under which AI decision-making mirrors or diverges from democratic norms. Our goal is to surface mechanisms that preserve fairness and robustness in AI collectives, even in the face of manipulation or systemic imbalance.