New prompting method improves LLM simulation of human decision-making

Researchers have developed a new method called Equation-to-Behavior Prompting to guide large language models (LLMs) in simulating diverse human decision-making behaviors, moving beyond simple Bayesian updating. This approach was tested on persuasion games, showing that larger models can approximate specified cognitive models through prompting. For smaller models, a reinforcement learning technique, Equation-to-Behavior RL, significantly reduced belief errors, particularly in out-of-distribution scenarios. Training smaller models with these diverse decision-maker simulations improved their average belief change compared to training solely on Bayesian models, even when interacting with models like GPT-5-mini. AI

IMPACT Enhances LLM training and evaluation by enabling more realistic simulations of diverse human decision-making.

RANK_REASON The cluster contains an academic paper detailing a new method for improving LLM simulations of human behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Bayesian updating
• Equation-to-Behavior Prompting
• Equation-to-Behavior RL
• GPT-5-mini
• Language Models
• motivated reasoning

• paper
• model release

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