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Guzman, R., Harrison, R., Abarca, N., & Villena, M. G. (2020). A game-theoretic model of reciprocity and trust that incorporates personality traits. J. Behav. Exp. Econ., 84, 11 pp.
Abstract: We propose a game-theoretic model of reciprocity and trust that incorporates personality traits. In the model, positive and negative reciprocity are “reciprocal preferences:” parameters of heterogeneous utility functions that take into account the material welfare of others (positively if they have been kind, negatively if they have been hostile). Trust, on the other hand, is an individual bias that distorts probabilistic beliefs about the trustworthiness of others. Unlike typical game-theoretic models, our model provides an explanation for the heterogeneity of preferences and probabilistic beliefs: a person's personality traits determine both the parameters of his utility function and the magnitude of his beleif bias. We tested the model experimentally. Subjects completed a psychometric questionnaire that measures three personality traits: positive reciprocity, negative reciprocity, and trust. Subsequently, they played a sequential prisoner's dilemma with random re-matching and payoffs changing from round to round. From the subjects' psychometric scores and game behaviors we inferred the relationship between reciprocal preferences, belief biases, and personality. The results confirmed the hypotheses of the model.
Lopez, A., & Sanchez, R. (2023). Not so rebel after all: Profiling personality traits in mountain running athletes. Retos, 48, 532–544.
Abstract: In the field of sport psychology, trait theories view personality characteristics as the main determinants of behavior. This study explored personality traits in athletes of a growing sport, trail or mountain ultrarunning, a group for which previous studies have yielded inconclusive results regarding the dominant traits and which traits are associated with sporting success. The NEO-FFI questionnaire by Costa and McCrae (1992) was applied online to a sample of 86 trail runners (60 men and 26 women), who partici-pated in a competition in Chile. It sought to determine what the personality profile of these athletes was like; to identify if there were significant differences between this population and the normal population; and if there were differences among them according to gender, the distance in which they competed, the results in the competition, and the motivations they stated for running. For each comparison, a Wilcoxon Rank Sum Test was performed to measure its differences and respective statistical significance. Results showed that the personality profile of the mountain runners matched that of successful athletes in terms of high Conscientiousness and low Neuroticism, however, Openness to Experience scores showed no significant differences with the normal population. Nei-ther did significant differences appear in any of the traits between ultradistance and shorter distance runners, nor according to their motivations for running, nor between male and female runners (except in the Agreeableness dimension). Finally, a clustering of the runners was outlined according to their personality profiles and performance, to see if they fit existing classifications that distinguish between recreational and results-oriented athletes, observing that it was possible to establish distinct profiles among runners. These results are of relevance to sports practitioners, who can design tailored interventions according to athletes ' personality profiles and prevent negative consequences when these traits become associated with unhealthy behaviors. The modest gender differences found, allow questioning gender stereotypes within the sport and fostering a more equitable approach to athlete training. This knowledge can contribute to further growing the sport of trail and ultrarunning in Chile and Latin America.
Valle, M. A., Ruz, G. A., & Masias, V. H. (2017). Using self-organizing maps to model turnover of sales agents in a call center. Appl. Soft. Comput., 60, 763–774.
Abstract: This paper proposes an approach for modeling employee turnover in a call center using the versatility of supervised self-organizing maps. Two main distinct problems exist for the modeling employee turnover: first, to predict the employee turnover at a given point in the sales agent's trial period, and second to analyze the turnover behavior under different performance scenarios by using psychometric information about the sales agents. Identifying subjects susceptible to not performing well early on, or identifying personality traits in an individual that does not fit with the work style is essential to the call center industry, particularly when this industry suffers from high employee turnover rates. Self-organizing maps can model non-linear relations between different attributes and ultimately find conditions between an individual's performance and personality attributes that make him more predisposed to not remain long in an organization. Unlike other models that only consider performance attributes, this work successfully uses psychometric information that describes a sales agent's personality, which enables a better performance in predicting turnover and analyzing potential personality profiles that can identify agents with better prospects of a successful career in a call center. The application of our model is illustrated and real data are analyzed from an outbound call center. (C) 2017 Elsevier B.V. All rights reserved.