5 papers
WhatIf: Interactive Exploration of LLM-Powered Social Simulations for Policy Reasoning
Yuxuan Li, Kyzyl Monteiro, Hirokazu Shirado +1
Policymakers in domains such as emergency management, public health, and urban planning must make decisions under deep uncertainty, where outcomes depend on how large populations i…
Martingale Score: An Unsupervised Metric for Bayesian Rationality in LLM Reasoning
Zhonghao He, Tianyi Qiu, Hirokazu Shirado +1
Recent advances in reasoning techniques have substantially improved the performance of large language models (LLMs), raising expectations for their ability to provide accurate, tru…
What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparedness
Yuxuan Li, Sauvik Das, Hirokazu Shirado
Policymakers must often act under conditions of deep uncertainty, such as emergency response, where predicting the specific impacts of a policy apriori is implausible. Large Langua…
Spontaneous Giving and Calculated Greed in Language Models
Yuxuan Li, Hirokazu Shirado
Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear wheth…
Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models
Yuxuan Li, Hirokazu Shirado, Sauvik Das
While advances in fairness and alignment have helped mitigate overt biases exhibited by large language models (LLMs) when explicitly prompted, we hypothesize that these models may…