Digital Homunculi and Institutional Design: Breaking Through the Experimentation Bottleneck
Petr Specian

TL;DR
This paper proposes using GenAI-powered agents, called digital homunculi, to simulate human behavior in institutional settings, potentially overcoming the slow and costly traditional experimentation process in democracy research.
Contribution
It introduces the digital homunculi methodology, leveraging large language models to generate synthetic data for testing institutional innovations rapidly and ethically.
Findings
Digital homunculi can elicit human-like behavior in multi-agent simulations.
Proposes validation strategies including behavioral back-testing.
Outlines infrastructure needs for rigorous evaluation.
Abstract
Democracy research faces a longstanding experimentation bottleneck. Potential institutional innovations remain untested because human-subject studies are slow, expensive, and ethically fraught. This paper argues that digital homunculi, that is, GenAI-powered agents role-playing humans in diverse institutional settings, could offer a way to break through the bottleneck. In contrast to the legacy agent-based modeling, building complexity from transparent simple rules, the digital homunculi methodology aims to extract latent human behavioral knowledge from opaque large language models. To this ends, it designs multi-agent interactions as elicitation devices to trigger in LLMs human-like behavior that can be recorded as synthetic data. However, the validity of synthetic data remains an open question. Success requires that accurate, coherent, transferable models of humans ('little humans' -…
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Taxonomy
TopicsEthics and Social Impacts of AI
