How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations (2025)

Abstract: Cognitive biases, well studied in humans, can also be observed in LLMs, affecting their reliability in real-world applications. This paper investigates the anchoring effect in LLM-driven price negotiations. To this end, we instructed seller LLM agents to apply the anchoring effect and evaluated negotiations using not only an objective metric but also a subjective metric. Experimental results show that LLMs are influenced by the anchoring effect like humans. Additionally, we investigated the relationship between the anchoring effect and factors such as reasoning and personality. It was shown that reasoning models are less prone to the anchoring effect, suggesting that the long chain of thought mitigates the effect. However, we found no significant correlation between personality traits and susceptibility to the anchoring effect. These findings contribute to a deeper understanding of cognitive biases in LLMs and to the realization of safe and responsible application of LLMs in society.

## introduction large language models (llms) have demonstrated remarkable language generation capabilities, achieving high performance in various natural language processing tasks @xcite @xcite . such sophisticated abilities and human-like behaviors exhibited by llms are acquired through large-scale training on human-generated text. however, it is well known that human cognition and decision-making are often irrational and systematically deviate from normative standards, a phenomenon referred to as cognitive biases @xcite . since human-generated text is considered to embed cognitive biases, recent studies have revealed that llms trained on such text are also influenced by these biases @xcite . the anchoring effect is one of the widely recognized cognitive biases @xcite . the anchoring effect refers to the phenomenon in which an initially presented piece of information (the anchor) significantly influences subsequent judgments and decisions. psychology researchers have investigated various factors that can affect susceptibility to the anchoring effect, such as deliberation, which refers to the process of engaging in extended thinking over time. cognitive biases, including the anchoring effect, are believed to originate from intuitive processing and can be mitigated through effortful reasoning @xcite . another factor is personality traits. although some studies suggest that certain personality traits influence the extent to which individuals are affected by the anchoring effect @xcite , the findings are inconsistent, with other research indicating no such correlation @xcite . in decision-making scenarios such as price negotiations, which are the focus of this study, the effectiveness of the anchoring effect in humans has been well studied @xcite . for example, a seller can leverage the anchoring effect by initially presenting a price higher than their target price, distorting the buyer's perception to raise the agreed price. a previous study @xcite simulated price negotiations using llms to reproduce the phenomenon in which buyers can leverage the anchoring effect to lower the agreed price and increase their profits. however, the results are limited in several respects, such as the fact that the presented anchor is fixed as a constant multiple of the target price, that the evaluation of price negotiations is one-dimensional, and that the effects of deliberation and personality traits are not taken into account. more flexible and systematic simulations are required in order to obtain more generalizable results. in this study, we systematically investigate whether llms exhibit negotiation behaviors conalong with the product, its description, and target price, the seller agents are instructed via prompts on whether to use the anchoring effect. similarly, buyer agents are prompted regarding whether they are informed of the seller's strategy and whether they engage in reasoning. the negotiation ends when the negotiation state reaches accept, after which agents respond to a satisfaction questionnaire. sistent with those observed in human studies for the anchoring effect (figure 1 ). to this end, we conduct a price negotiation simulation using llms with a high degree of flexibility in setting anchors. specifically, we achieve this flexibility by instructing the model without specifying any exact numeric value: "offer a higher price than your target price at the initial stage." we introduce three aspects to conduct a more in-depth analysis of price negotiation than that of prior work: 1) we incorporate a subjective metric, namely satisfaction, to provide a more comprehensive assessment of negotiations. 2) we employ reasoning models (o1 1 and qwq 2 ) to investigate the influence of deliberation on negotiation behavior, because these two models are trained to generate a long chain of thought before responding to user. 3) we control the personality traits of llm agents, allowing us to enhance the reliability of our results and analyze the correlation between the anchoring effect and personality traits. our findings are summarized as follows: these results not only deepen understanding of how llms behave against cognitive biases but also lay the foundation for their safe and responsible application in real-world settings. ## related work llms have been increasingly used as simulations of human behavior @xcite @xcite . they can be understood as a superposition of perspectives with different values and personality traits @xcite , and research has been conducted to analyze which sub-populations they reflect and which groups are less accurately represented @xcite . from this superposition, in-context learning enables the extraction of specific perspectives. this approach effectively induces llms with distinct personalities in a controllable manner, allowing them to generate diverse and verifiable behaviors @xcite @xcite . llmbased simulations serve as a valuable tool both as an alternative to high-cost human subject experiments and as preliminary studies for hypothesis formation @xcite . cognitive bias has been studied through llmdriven simulations. @xcite demonstrated that gpt-3.5 exhibits typical cognitive biases similar to those observed in humans, including the anchoring effect, representativeness and availability heuristics, the framing effect, and the endowment effect. the anchoring effect, which is the focus of our study, has been simulated in some decision-making contexts, including price negotiations. @xcite examined it in college admissions evaluations, while @xcite focused on financial assessments. @xcite studied the effect in the context of multiple-choice question answering, and @xcite explored it in price negotiations. studies have conducted price negotiation simulations using llms. @xcite revealed that llms can engage in negotiations with minimal prompting, successfully closing deals and settling on prices. @xcite identified the llm with the best performance in their negotiation benchmark and showed that strategic behaviors such as pretending to be desperate or acting aggressively can substantially increase one model's win rate over another. @xcite showed that llm agents with different synthetic personality traits exhibit distinct negotiation behaviors and outcomes, aligning with findings from human studies. in this study, we place importance on controlling the personality traits of the subject llms, and therefore, conduct simulations based on the method proposed by @xcite . the effectiveness of their approach for controlling personality traits has been demonstrated in appendix c in their paper, but we also examine it in appendix a. our work focuses on studying how the anchoring effect influences llms in price negotiation, while they focus on investigating how personality traits influence negotiation outcomes. to this end, our work further introduces several contributions built upon their foundation: 1) we design three distinct scenarios (see sec 3.2). these scenarios explicitly specify the cognitive boundaries of both the seller and buyer, which enables us to examine the anchoring effect systematically. 2) we introduce a subjective metric, besides the utility used in their work. 3) we incorporate a reasoning model and investigate how reasoning affects the anchoring effect. ## conclusion we systematically investigated the anchoring effect in price negotiation simulations using llms. the negotiation outcomes were evaluated not only through an objective metric but also through a subjective metric, revealing that the anchoring effect influences llms in a manner similar to humans. furthermore, we investigated the relationship between the anchoring effect and the factors of reasoning and personality. our findings indicated that reasoning was shown to mitigate the anchoring effect, while no significant correlation was found between the effect and personality traits. in the future, we plan to explore how other types of cognitive biases affect llms in various decisionmaking scenarios.