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Predicting Human Decisions with LLMs by gurumann ss is a document available to read on EtoBox.

This paper investigates the capabilities of large language models (LLMs) in predicting human action strategies in decision-making tasks and compares their performance with a cognitive instance-based learning (IBL) model. The findings indicate that LLMs excel in rapidly incorporating feedback to improve prediction accuracy, while the IBL model better captures human exploratory behaviors and cognitive biases. The study suggests that integrating LLMs with cognitive architectures could enhance the understanding

Author

gurumann ss

Language

EN