Does more darkness equal more money? The relationship between the Dark Triad and Income


Bill Danielsen, Royal Roads University

In our digital, globalized economy, the impact of an insider threat on an organization is significant to the point that it is almost difficult to quantify. Insiders are trusted employees who have legitimate access an organization’s systems and information. An organizations critical need and ability to detect and prevent insider threat incidents and attacks is crucial to its corporate success and relationship with its clients. This paper will explore the complex interplay between the Dark Triad personality traits of Machiavellianism, narcissism, and psychopathology and their impact on income levels through the novel application of advanced machine learning (ML) and artificial intelligence algorithms. Through ML algorithms such as gradient descent, random forest, decision trees, and support vector machines (SVM), we aim to provide a comprehensive understanding of how the Dark Triad traits influence individuals financial success. The Dark Triad traits are well-established and recognized as influential factors in shaping interpersonal dynamics, decision-making, and overall behavior. However, their specific contributions as a collective and their impact on an individuals economic success remain largely underexplored. By leveraging the power of ML models, we seek to uncover hidden patterns and nuances in the data that will provide novel insights into the relationship between these personality traits and financial success. To conduct this analysis, we collected a diverse dataset comprising demographic information, psychometric assessments for Machiavellianism, narcissism, and psychopathy, as well as corresponding income levels. This data was collected through a survey placed on a website (www.insiderthreat.ca) and included responses from 232 individuals who were over the age of 18 and resided principally in North America. To increase the number of observations, we created a custom algorithm for the specific task of generated synthetic data that mimic the structure and characteristics of the original dataset. Through data preprocessing and hyper parameter tuning, we were able to iteratively refine our models, minimizing prediction errors while enhancing the accuracy of our results. Random Forest and decision tree algorithms were deployed to identify nonlinear relationships and evaluate complex interactions among the independent variables. These ensemble methods are superior in capturing intricate patterns within datasets, allowing us to discern the nuanced impact of each Dark Trait on income level. Through the decision-making processes inherent to these ensemble algorithms, we aim to demonstrate the underlying relationship where Dark Triad Traits influence financial success. SVM is well-established in its ability to discern subtle patterns in high-dimensional data, which further contributes to our analysis. SVMs aid in uncovering the boundaries between income categories as seen through the lens of the Dark Triad traits and provide a clearer understanding of the extent to which these traits contribute to income disparities. The integration of these diverse ML techniques allows us to triangulate our findings and enhance the robustness and reliability of our conclusions. Preliminary results suggest that the Dark Triad traits of Machiavellianism, narcissism, and psychopathy do exhibit varying degrees of influence on income levels. Individuals who are high Machiavellianism, known for traits such as their strategic and manipulative tendencies, may navigate professional environments with greater success, positively impacting their income level. Narcissistic traits, characterized by an exaggerated sense of self-worth and self-importance, may lead individuals to seek high-status positions, also influencing their income levels. Psychopathy, noted by impulse control issues and a lack of empathy, may result in divergent effects on income, with potential advantages in certain competitive environments where it may present as "fearless leadership". In conclusion, this study uses an innovative approach to examining the relationship between the Dark Triad traits and income levels by leveraging MLalgorithms. Our findings contribute to the existing literature on personality and economics but also highlight the potential of advanced analytical techniques in providing detailed and rich analysis of complex and multifaceted relationships. This research provides a foundation for future explorations into the nuanced relationship between the Dark Triad, professional success, and other yet unexplored impacts this relationship may have.

Non-presenting author: Mark Lokanan, Royal Roads University

This paper will be presented at the following session: