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. 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.