9 papers
Model Capacity Frontier: A Development Framework for LLM-Supported Social Simulation
Patrick Zhao, Yuxuan Li, Hirokazu Shirado
Large language model (LLM) agents are increasingly incorporated into agent-based models (ABMs) on the premise that richer, context-sensitive human behavior can improve empirical fi…
WhatIf: Interactive Exploration of Large-Scale LLM Agent 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…
Faith in AI can narrow the futures individuals consider
Aoi Naito, Hirokazu Shirado
Artificial intelligence (AI) predictions are increasingly used to inform human decisions. Here, using a behavioral implementation of the classic Newcomb's paradox in 1,305 particip…
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…
Systematic Failures in Collective Reasoning under Distributed Information in Multi-Agent LLMs
Yuxuan Li, Aoi Naito, Hirokazu Shirado
Multi-agent systems built on large language models (LLMs) are expected to enhance decision-making by pooling distributed information, yet systematically evaluating this capability…
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…
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning
Atsushi Ueshima, Shayegan Omidshafiei, Hirokazu Shirado
Cooperation is challenging in biological systems, human societies, and multi-agent systems in general. While a group can benefit when everyone cooperates, it is tempting for each a…