How Personality Traits Shape LLM Risk-Taking Behaviour (2025)

Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents for simulation and decision-making, necessitating a deeper understanding of their decision-making behaviour under risk. We investigate the relationship between LLMs’ personality traits and risk-propensity, applying Cumulative Prospect Theory (CPT) and the Big Five personality framework. We compare the behaviour of several LLMs to human baselines. Our findings show that the majority of the models investigated are risk-neutral rational agents, whilst displaying higher Conscientiousness and Agreeableness traits, coupled with lower Neuroticism. Interventions on Big Five traits, particularly Openness, influence the risk-propensity of several LLMs. Advanced models mirror human personality-risk patterns, suggesting that cognitive biases can be surfaced by optimal prompting. However, their distilled variants show no cognitive bias, suggesting limitations to knowledge transfer processes. Notably, Openness emerges as the most influential factor to risk-propensity, aligning with human baselines. In contrast, less advanced models demonstrate inconsistent generalization of the personality-risk relationship. This research advances our understanding of LLM behaviour under risk and highlights the potential and limitations of personality-based interventions in shaping LLM decision-making.

## introduction large language models (llms) have emerged as powerful autonomous agents demonstrating versatility across diverse domains @xcite such as software engineering @xcite @xcite , financial modelling @xcite @xcite . we find analogous relationships between traits and risk-taking in gpt-4o and claude 3 sonnet with humans. parameter α is the risk sensitivity for gains, and ρ is the spearman's correlation coefficient. @xcite @xcite . in the financial sector for example, agent-based modelling is already utilized in banking @xcite , and with the advancement of llms, we anticipate an increased application in financial simulations @xcite @xcite . the success of llms can be attributed to their ability to efficiently leverage knowledge from vast text corpora and their proficiency in natural language understanding and generation @xcite . these capabilities have led to the development of sophisticated agents with advanced decision-making abilities, incorporating linguistic-based modules for planning, memory, perception, and action @xcite . despite their growing utility and complexity, a comprehensive understanding of llms' decisionmaking processes, in the context of risk, remains an emerging area of research @xcite @xcite . concurrently, llms can be prompted to exhibit specific personality traits, such as those defined by the ocean model (openness, conscientiousness, extraversion, agreeableness, neuroticism) @xcite and can be prompted to role-play with varying personalities @xcite . this finding raises intriguing questions about the interplay between personality traits and decisionmaking processes in llms, especially in domains where risk-tolerance and personality characteristics play crucial roles, such as financial applications and real world agents. while extensive research has been conducted on the connection between human personality and risk behaviour in prospect selection @xcite @xcite @xcite @xcite @xcite , the analogous relationship in llms remains unexplored. we investigate the relationship between personality traits and risky decision-making in llms to bridge this gap. we pose the research question: do llms generalize the relationship between personality traits and risk-based decision-making in a manner analogous to humans? we explore this question by we applying two well-established frameworks: the cumulative prospect theory (cpt) from behavioural economics @xcite and the big five personality traits from psychological testing @xcite . our approach consists of three main steps. our primary contributions are: the paper is structured as follows: section 2 reviews relevant literature on personality prompting and cpt parameters in llms. section 3 outlines our approach to estimating certainty equivalents of personified llms. section 4 presents our findings on the personality traits and cpt parameters of multiple llms, including for personified versions and comparisons with baselines of human behaviour. section 5 concludes with implications of our research. ## related work we review related works in psychometric assessment of personality, risk-propensity analysis through prospect theory (pt), and personality modulation in llms. we present a comprehensive overview in appendix a. the big five model factors personality into five traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism (ocean) @xcite . human alignment with these traits is measured through surveying associations with specific personality facets of these traits @xcite . research has shown correlations between the big five traits and risk-propensity. for example, openness consistently predicts increased and reduced risk-seeking for gains and losses respectively @xcite @xcite . extraversion positively correlates with risk-seeking @xcite . neuroticism inversely relates to risk-seeking @xcite . agreeableness generally correlates with lower risk-seeking tendencies @xcite . conscientiousness inversely relates to riskseeking @xcite @xcite . machine psychology is the study of emergent cognitive behaviors in llms @xcite . recent studies have shown that cognitive biases observed in earlier models have diminished in the latest generation of llms @xcite . @xcite demonstrated improved task performance in llms when given personas, mimicking human-like developmental exploration and expert reasoning. procedural prompting must be used to prevent text generations using shortcut learning or memorization @xcite . prospect theory (pt) @xcite describes human decision-making under risk, challenging expected utility theory. cumulative prospect theory @xcite introduced a power-law model to quantify irrational decision-making behaviour. recent research has investigated pt in llms. @xcite found human-like cognitive biases in gpt-3, while @xcite observed reduced biases in more recent models like gpt-4. the latter also showed that risk-propensity is sensitive to qualitative personas. big five traits have been induced in llms with demonstrated generalization to downstream tasks @xcite . research has expanded beyond the big five to examine toxic traits @xcite and anxiety @xcite in language models. while @xcite identified inconsistencies in earlier models' personality assessments, more recent models like gpt-4 turbo exhibited greater trait consistency. @xcite demonstrated internal-consistency reliability in llms' big five scores across 2,500 variations in wording, language, format, and item order. these scores showed significantly lower variance than human norms. this low-variance pattern was reproduced across multiple architectures (gpt-3.5, gpt-4, gemini-pro, and llama-3), strongly suggesting the effect is architecture-agnostic. when deliberately manipulated through extreme trait instructions or character role-playing, personality inventories shifted predictably, confirming the scale's sensitivity to meaningful personality changes while maintaining robustness against irrelevant prompt variations. we build on these works to demonstrate how intervening on the big five personality traits of llms affects their risk-based decision-making within the cpt framework. additionally, we examine whether the relationships between personality traits and risk-taking in llms align with those observed in humans. ## experiments and results we present our experiments and results in three parts. we begin by examining the cpt parameters and personality traits of llms. next, we explore risk-taking behaviour in llms with interventions on their personality traits. finally, we investigate how openness emerges as the most influential trait in risk-taking. ## conclusion this study examines the risk-propensity behaviour of llms in relation to personality traits. advanced llms predominantly exhibit risk-neutral rational agent characteristics. however, interventions on the openness trait induce risk-propensity patterns analogous to human behaviour in models such as gpt-4o and claude 3 sonnet. notably, openness emerges as the primary determinant of riskpropensity in gpt-4o, aligning with human psychological studies. smaller model variants and legacy models demonstrate inconsistent personality-risk relationships, suggesting potential limitations in knowledge distillation processes. these findings have significant implications for the development of ai systems with human-like decision-making capabilities, particularly in multi-agent simulations and financial modelling.