How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language Models (2024)

Abstract: Psychological evidence reveals the influence of personality traits on decision-making. For instance, agreeableness is generally associated with positive outcomes in negotiations, whereas neuroticism is often linked to less favorable outcomes. This paper introduces a simulation framework centered on large language model (LLM) agents endowed with synthesized personality traits. The agents negotiate within bargaining domains and possess customizable personalities and objectives. The experimental results show that the behavioral tendencies of LLM-based simulations can reproduce behavioral patterns observed in human negotiations. The contribution is twofold. First, we propose a simulation methodology that investigates the alignment between the linguistic and economic capabilities of LLM agents. Secondly, we offer empirical insights into the strategic impacts of Big Five personality traits on the outcomes of bilateral negotiations. We also provide an in-depth analysis based on simulated bargaining dialogues to reveal intriguing behaviors, including deceitful and compromising behaviors.

## introduction large language models (llms) have demonstrated their capacity to emulate diverse human traits @xcite . such models can simulate intricate behaviors and provide valuable insights into various aspects of human cognition. decision-making is an example of cognitive processes that have long fascinated psychologists and economists. economic theory posits that decisions assume a certain level of rationality and comprehension of available options @xcite . however, behaviorists contend that humans are not entirely rational but are influenced by psychological factors @xcite , cognitive biases @xcite , and personality traits @xcite . in this paper, we investigate the extent to which llms can simulate human decision-making across individuals with varying personality traits. we specifically focus on negotiations between two llm agents with customizable personality traits. evidence suggests that certain personality traits may give advantages in negotiation settings @xcite @xcite . for instance, agreeableness tends to result in a slight disadvantage in competitive negotiations while being an advantage in cooperative settings @xcite . in the context of llms, we attempt to answer this long-standing question in psychology: "how do personality traits affect the outcomes of negotiations?" to address this question, we propose a negotiation simulation framework incorporating llm agents possessing synthetic personality traits and predefined negotiation objectives (figure 1 ). 1 for synthetic personalities, we use in-context learning to configure the agents with customizable personality profiles. here, the profiles are based on big five personality theory @xcite . for the negotiation objectives, we give llm agents instructions that specify the negotiation task and goals. the llm agents then engage in a negotiation by exchanging offers in the form of dialogue. specifically, we consider a competitive bargaining scenario between a buyer and a seller agent. in each round of the negotiation dialogue, we extract the offered price and strategy (if any) made in the dialogue utterances for evaluation and analysis. by varying the personality traits of the agents, we observe changes in the behavioral patterns and outcomes of the negotiations. we investigated which personality traits lead to better/worse outcomes. more importantly, we want to see whether the llm-based simulation results align with the findings of research conducted on human subjects. our experimental results show that the tendencies in llm-based simulation generally align with behaviors observed in human experiments. this includes the effect of personality traits on negotiation outcomes and the relationship between personality traits and strategies. a case study based on synthesized bargaining dialogue reveals intriguing behavioral patterns such as deceiving behaviors, emotional appeal, and take-it-or-leave-it strategies. the results obtained in this work illustrate that llms can not only mimic styles of talking but are also capable of capturing human decision-making patterns. the contribution of the paper is twofold. first, we propose a simulation framework that leverages llm agents with linguistic and economic capabilities. second, we offer insights into the effect of big five personality traits on simulated negotiation outcomes and compare these findings to empirical experiments from psychology. ## related work recent advances in llms now allow the simulation of various human behaviors @xcite @xcite . decision-making is a particular type of behavior that is still challenging to reproduce using llm agents because it relies on reasoning capabilities that they lack @xcite . decisions generally entail choosing an action from various options in response to a particular situation, often reflecting personal preferences or beliefs @xcite . moreover, real-world decisions are challenging because they are susceptible to environmental and cognitive constraints (phillips-wren and adya, 2020). since decision-making problems are wide, we choose to focus on negotiations as an example that we claim can be studied comprehensibly using llms. in a negotiation, two parties exchange bids and attempt to reach a mutual agreement @xcite . looking at negotiations from the economics perspective, we often presuppose several assumptions, such as rationality @xcite . this assumption often fails when the negotiations are conducted through natural language, which conveys various aspects that cannot be studied economically, such as emotions or personality traits. there is evidence showing the effect of big five personality traits on decisions @xcite @xcite @xcite . moreover, certain personality traits are considered disadvantageous in negotiations @xcite . extraversion and agreeableness, for example, constitute liabilities in competitive bargaining problems while being advantageous in cooperative settings @xcite . herein, we adopt an indirect economic analysis of negotiations to see whether personality instructions to llms really translate to genuine behavioral change in the underlying agents. we reproduce some of the known results of psychology in addition to results that are unique to llms. our analysis of the rationality of the llm agents relies on economic metrics used in negotiation research @xcite . recent methods are tailored to llms and include the use of games and theory-of-mind (tom) modeling @xcite @xcite . it is important to note that the simulation of human behavior using llms poses several risks, including the risk of data contamination @xcite . for instance, if a dataset derived from a personality questionnaire is used to train a language model, the resulting personality scores may not accurately indicate whether the llm's behavioral responses align with the personality traits specified in the prompt. to reduce the effect of such risks on our experimental design, we adopted canonical bargaining problems that are well-studied in negotiation research @xcite . this choice renders the behavioral analysis of the llm agents more tractable, particularly under the effect of personality traits. ## methodology this section introduces our simulation framework based on llm negotiation agents with synthetic personality traits. in section 3.1, we formulate the negotiation model. in section 3.2, we introduce the method to configure llm negotiation agents by providing the instructions to set the personality traits and negotiation objectives. we then describe the process of simulating negotiation dialogues with the llm agents in section 3.3. ## experiments in this section, we first provide details on the experimental settings (section 4.1) and the evaluation metrics (section 4.2). ## results and analysis in this section, we conduct an analysis of the negotiation outcomes based on the dialogues generated in the negotiation simulations. ## conclusion this study introduced a novel simulation framework that integrates llm negotiation agents with synthesized big five personality traits. these agents were deployed in traditional bargaining scenarios to simulate negotiation dialogues. our experimental findings indicate that the behavioral patterns demonstrated by the llm agents closely mirror those exhibited by human negotiators. furthermore, a detailed analysis of synthesized bargaining dialogues highlighted distinct strategies and behaviors, including deceit, compromise, and stake assessment. these analyses augment our understanding of linguistic and economic agent interactions. our contributions are twofold. firstly, we established a robust simulation methodology to examine the linguistic and economic dynamics of llm agents. secondly, we provided comprehensive empirical evidence on the strategic impact of big five personality traits on negotiation outcomes, offering valuable insights into the mechanics of artificial negotiation agents. the findings of this work have potential applications such as personalized assistants, chatbots, social dynamics simulations, and so on.