Will LLM s Sink or Swim? Exploring Decision-Making Under Pressure (2024)

Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated their ability to simulate human-like decision-making, yet the impact of psychological pressures on their decision-making processes remains underexplored. To understand how psychological pressures influence decision-making in LLMs, we tested LLMs on various high-level tasks, using both explicit and implicit pressure prompts. Moreover, we examined LLM responses under different personas to compare with human behavior under pressure. Our findings show that pressures significantly affect LLMs’ decision-making, varying across tasks and models. Persona-based analysis suggests some models exhibit human-like sensitivity to pressure, though with some variability. Furthermore, by analyzing both the responses and reasoning patterns, we identified the values LLMs prioritize under specific social pressures. These insights deepen our understanding of LLM behavior and demonstrate the potential for more realistic social simulation experiments.

## introduction the rapid advances in large language models (llms) have shown their potential in human-like decision-making @xcite @xcite . however, current research often overlooks how llms behave under psychological pressures. understanding their responses to pressure is crucial for evaluating their real-world applicability. to accurately simulate human experiences, it's essential to study llms' decision-making under pressure, yet limited research exists on this topic. this study aims to address this gap by investigating how various psychological pressures affect llms across different decision-making tasks. we applied two main forms of pressure prompts: explicit pressure and implicit pressure. we used ex-plicit pressure prompts to induce specific pressures, including time, verbal, competitive, monitoring, and outcome pressures, and tested these on reasoning, psychometric, and game theory tasks. implicit pressure was incorporated into social decisionmaking tasks through scenario-based prompts, allowing us to observe llm responses to social pressure without direct instructions. additionally, we requested llms to provide both their generated reasoning and final outputs to obtain a comprehensive understanding of how these models respond to social pressure. additionally, to facilitate a comparison between llm responses and human behavior under psychological pressure, we conducted a persona-based analysis. since direct comparison is challenging, we equipped the llms with specific personas-such as high and low self-consciousness @xcite , communication apprehension @xcite , and fear of social isolation @xcite -to emulate individuals with these characteristics. by observing how the llms respond to pressure prompts under these personas, we aim to assess how similarly they mimic human responses, providing insights into their behavior and potential limitations. our findings reveal that psychological pressures notably affect llms' decision-making, although effects are nuanced across tasks and models. persona-based analysis shows some models exhibit human-like sensitivity to pressure, but this is not uniform across all models and pressures. these results highlight the need to consider psychological pressures, providing guidelines for more realistic simulations of human decision-making under pressure. this study makes significant contributions to the field. it is the first comprehensive investigation into the impact of various psychological pressures on llms' decision-making across multiple tasks. by analyzing persona-based reactions, we compare llm behavior to human-like responses, examining similarities and differences in decision-making under pressure. additionaly, we conducted a detailed thematic analysis of the step-by-step reasoning generated by various personas under the pressure, examining how different personas influence the reasoning processes and the resulting outputs. ## experimental setup and task design in this section, we overview the experimental setup, including the application of psychological pressures and task descriptions. ## recommendations this section provides guidelines for conducting social experiments with llms under pressure. ## conclusion in conclusion, we showed that llms are influenced by various psychological pressures using explicit and implicit prompts, though no clear trends emerged. persona-based analysis revealed both similarities and differences between llm outputs and human behavior. thematic evaluation provided insights into the response patterns of llms under pressure. these findings highlight the complex effects of psychological pressures on llms and contribute to a better understanding of their behavior in contexts involving different personas.