AI-Agent-Based Social Simulation: Validation and Alignment with Human Behavior

A 2026 AI-Science Hub project funded by the Ministry of Science and ICT. We build a framework for social simulation with LLM agents, then compare and validate it against human social behavior, including data from real deliberative polls.

The goal is simulations that are reproducible and whose populations can be checked against the people they stand in for.

Funding: Ministry of Science and ICT · AI-Science Hub
Period: 2026
Team: Prof. Jihee Kim (PI), with Prof. Nuri Kim and Prof. Joseph Seering
My role: Simulation experiments and analysis.

APPROACH

A common framework for running social simulations with LLM agents
Validation against human data, including real deliberative polls
Reproducibility: the same simulation should give the same population-level answer

RELATED PAPERS 1

Preprint2026

From Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction

Chaemin Jang, Junsik Min, Jaewoo Choi, Donggyu Lee, Haiin Lee, Junyoung Park, Namhee Kim, Hyunwoo Kim, Jungwon Kim, Juho Kim, Nuri Kim, Jihee Kim

Accepted at SocialAgent and UserSim workshops, NeurIPS 2026

Simulating a deliberating public needs agents whose opinions reflect a population and change through discussion. Across six LLMs, national surveys, controlled experiments and the 2017 Shin-Kori deliberative poll, agents argue convincingly but represent opinion too narrowly, and change positions even when they cannot see each other.

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