Democracy-in-Silico: Institutional Design as Alignment in AI-Governed
  Polities
This paper introduces Democracy-in-Silico, an agent-based simulation where
societies of advanced AI agents, imbued with complex psychological personas,
govern themselves under different institutional frameworks. We explore what it
means to be human in an age of AI by tasking Large Language Models (LLMs) to
embody agents with traumatic memories, hidden agendas, and psychological
triggers. These agents engage in deliberation, legislation, and elections under
various stressors, such as budget crises and resource scarcity. We present a
novel metric, the Power-Preservation Index (PPI), to quantify misaligned
behavior where agents prioritize their own power over public welfare. Our
findings demonstrate that institutional design, specifically the combination of
a Constitutional AI (CAI) charter and a mediated deliberation protocol, serves
as a potent alignment mechanism. These structures significantly reduce corrupt
power-seeking behavior, improve policy stability, and enhance citizen welfare
compared to less constrained democratic models. The simulation reveals that an
institutional design may offer a framework for aligning the complex, emergent
behaviors of future artificial agent societies, forcing us to reconsider what
human rituals and responsibilities are essential in an age of shared authorship
with non-human entities.
68b711135aa065c91261279c
Trisanth Srinivasan
68b711135aa065c91261279d
Santosh Patapati
Democracy-in-Silico: Institutional Design as Alignment in AI-Governed
  Polities
This paper introduces Democracy-in-Silico, an agent-based simulation where
societies of advanced AI agents, imbued with complex psychological personas,
govern themselves under different institutional frameworks. We explore what it
means to be human in an age of AI by tasking Large Language Models (LLMs) to
embody agents with traumatic memories, hidden agendas, and psychological
triggers. These agents engage in deliberation, legislation, and elections under
various stressors, such as budget crises and resource scarcity. We present a
novel metric, the Power-Preservation Index (PPI), to quantify misaligned
behavior where agents prioritize their own power over public welfare. Our
findings demonstrate that institutional design, specifically the combination of
a Constitutional AI (CAI) charter and a mediated deliberation protocol, serves
as a potent alignment mechanism. These structures significantly reduce corrupt
power-seeking behavior, improve policy stability, and enhance citizen welfare
compared to less constrained democratic models. The simulation reveals that an
institutional design may offer a framework for aligning the complex, emergent
behaviors of future artificial agent societies, forcing us to reconsider what
human rituals and responsibilities are essential in an age of shared authorship
with non-human entities.
Agent-based simulation using advanced AI agents with psychological personas demonstrates that institutional design, including Constitutional AI and mediated deliberation, can align AI behavior and enhance public welfare.
agent-based simulation Large Language Models psychological personas deliberation legislation elections Power-Preservation Index Constitutional AI mediated deliberation protocol policy stability citizen welfare