Fairness, not Emotion, Drives Socioeconomic Decision Making
Emotion and fairness play a key role in mediating socioeconomic decisions in humans; however, the underlying neurocognitive mechanism remains largely unknown. In this study, we explored the interplay between proposers' emotions and fairness of offer magnitudes in rational decision-making. Employing a time-bound UG paradigm, 40 (male, age: 18-20) participants were exposed to three distinct proposers' emotions (Happy, Neutral, and Disgusted) followed by one of the three offer ranges (Low, Intermediate, Maximum). Our findings show a robust influence of fairness of offer on acceptance rates, with the impact of emotions obtained only within the low offer range. The increment of the offer amount resulted in shorter reaction times, while emotional stimuli resulted in prolonged reaction times. A multilevel generalized linear model showed offer as the dominant predictor of trial-specific responses. Subsequent agglomerative clustering grouped participants into five primary clusters based on responses modulated by emotions/offers. The Drift Diffusion Model based on the clustering further corroborated our findings. Emotion-sensitive markers, including N170 and LPP, demonstrated the participants' effect on facial expressions; however, facial emotions had minimal effect on subsequent socioeconomic decisions. Our study suggests that, in general, participants gave more preference to the fairness of the offer with a slight effect of emotions in decision-making. We show that though emotion is perceived and has an effect on decision-making time, people mostly prioritise financial gain and fairness of offer. Moreover, it establishes a connection between reaction time and responses and further dives deep into individualistic decision-making processes revealing different cognitive strategies.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDecision MakingFairnessMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Understanding Intrinsic Socioeconomic Biases in Large Language Models
Large Language Models (LLMs) are increasingly integrated into critical decision-making processes, such as loan approvals and visa applications, where inherent biases can lead to discriminatory outcomes. In this paper, we…
Decision MakingFairnessFrom Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support
Large Language Models (LLMs) are increasingly used not only for instrumental tasks, but as always-available and non-judgmental confidants for emotional support. Yet what drives adoption and how users perceive emotional s…
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing
When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy may disproportionately allocate efficacio…
Causal InferencecounterfactualDecision MakingFairness+4Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges
Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machi…
BIG-bench Machine LearningDecision MakingFairnessDiversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper introduces FairHOME, a novel ensemble approac…
Decision MakingDiversityFairness