  The advancement of Large Language Models (LLMs) has led to their widespread
use across a broad spectrum of tasks including decision making. Prior studies
have compared the decision making abilities of LLMs with those of humans from a
psychological perspective. However, these studies have not always properly
accounted for the sensitivity of LLMs' behavior to hyperparameters and
variations in the prompt. In this study, we examine LLMs' performance on the
Horizon decision making task studied by Binz and Schulz (2023) analyzing how
LLMs respond to variations in prompts and hyperparameters. By experimenting on
three OpenAI language models possessing different capabilities, we observe that
the decision making abilities fluctuate based on the input prompts and
temperature settings. Contrary to previous findings language models display a
human-like exploration exploitation tradeoff after simple adjustments to the
prompt.
