Browsing by Author "Contractor, Noshir"

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    Descubriendo colaboradores en línea: Formando equipos interdisciplinarios en una universidad argentina
    (2019) Gómez Zara, Diego Alonso; DeChurch, Leslie; Contractor, Noshir; Andreoli, Silvia
    Este estudio explora cómo y por qué las personas encuentran colaboradores usando sistemas para la formación de equipos. Basados en teorías sobre equipos y capital humano y social, describimos cómo los rasgos de los individuos y sus redes sociales influyen en los procesos de formación de equipos. Realizamos un estudio en Argentina en el que 43 profesores utilizaron una plataforma online y formaron ocho equipos interdisciplinarios. Nuestros resultados muestran que los profesores inicialmente tendían a invitar a contactos anteriores, pero finalmente, formaron equipos interdisciplinarios y cohesivos con personas desconocidas. Concluimos reflexionando sobre cómo estas plataformas pueden permitir a las personas ampliar su capital social.
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    In search of diverse and connected teams: A computational approach to assemble diverse teams based on members’ social networks
    (Public Library of Science, 2022) Gómez Zara, Diego Alonso; Das, Archan; Pawlow, Bradley; Contractor, Noshir
    Previous research shows that teams with diverse backgrounds and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar to them and fail to assemble teams with high diversity levels. We study the team formation problem by considering a pool of individuals with different skills and characteristics, and a social network that captures the familiarity among these individuals. The goal is to assign all individuals to diverse teams based on their social connections, thereby allowing them to preserve a level of familiarity. We formulate this team formation problem as a multiobjective optimization problem to split members into well-connected and diverse teams within a social network. We implement this problem employing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which finds team combinations with high familiarity and diversity levels in O(n2) time. We tested this algorithm on three empirically collected team formation datasets and against three benchmark algorithms. The experimental results confirm that the proposed algorithm successfully formed teams that have both diversity in member attributes and previous connections between members. We discuss the benefits of using computational approaches to augment team formation and composition.