Agent-Based Modeling of Resource Allocation in Software Projects Based on Personality and Skill

Farhangian, Mehdi; Purvis, Martin; Purvis, Maryam; Savarimuthu, Tony

doi:10.1007/978-3-319-24804-2_9

Cited by 11 publications

(17 citation statements)

References 15 publications

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“…To our knowledge, the only computational model in the context of team composition that takes both personality and competencies into account was presented in [39]. In particular, the influence of personality on different strategies for allocating tasks is studied in this paper.…”

Section: Relation With the Multiagent Systems Literaturementioning

confidence: 99%

Synergistic team composition: A computational approach to foster diversity in teams

Andrejczuk

Bistaffa

Blum

et al. 2019

Knowledge-Based Systems

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Co-operative learning in heterogeneous teams refers to learning methods in which teams are organised both to accomplish academic tasks and for individuals to gain knowledge. Competencies, personality and the gender of team members are key factors that influence team performance. Here, we introduce a team composition problem, the so-called synergistic team composition problem (STCP), which incorporates such key factors when arranging teams. Thus, the goal of the STCP is to partition a set of individuals into a set of synergistic teams: teams that are diverse in personality and gender and whose members cover all required competencies to complete a task. Furthermore, the STCP requires that all teams are balanced in that they are expected to exhibit similar performances when completing the task. We propose two efficient algorithms to solve the STCP. Our first algorithm is based on a linear programming formulation and is appropriate to solve small instances of the problem. Our second algorithm is an anytime heuristic that is effective for large instances of the STCP. Finally, we thoroughly study the computational properties of both algorithms in an educational context when grouping students in a classroom into teams using actual-world data.

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Section: Relation With the Multiagent Systems Literaturementioning

confidence: 99%

Synergistic team composition: A computational approach to foster diversity in teams

Andrejczuk

Bistaffa

Blum

et al. 2019

Knowledge-Based Systems

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“…To choose which action to execute next, every heterogeneous agent within a team votes for its preferred candidate action. Agents vote according to a probability distribution over actions Farhangian et al (2015a) is the only previous work to our knowledge that uses both individuals' skills and personality types (measured by MBTI and Belbin (Belbin, 1993) personality tests) to compose teams. These two dimensions are used to simulate human team composition in a business environment.…”

Section: Personality: Individual Behaviour Modelsmentioning

confidence: 99%

“…While structured tasks are straightforward and do not require planning, open-ended tasks require creativity and imagination from team members. In another article, Farhangian et al (2015a) try to capture the dynamics of tasks by matching the required levels of creativity, urgency, social interaction and complexity of a task to personalities of agents. For instance, teams composed of differing attitude tendencies (associated with different personalities) are believed to outperform teams composed of like-minded people when tackling tasks requiring a high level of creativity.…”

Section: Individual-based Approachesmentioning

confidence: 99%

“…In a series of papers, Farhangian et al . (2015a, 2015b) use the MBTI scheme to model different agent personality types. Farhangian et al .…”

Section: Team Composition and Formation From A Computer Science Perspmentioning

confidence: 99%

“…Farhangian et al . (2015a) use both individuals’ skills and personality types (measured by MBTI and Belbin, 1993, personality tests) to compose teams. These two dimensions are used to simulate human team composition in a business environment.…”

Section: Team Composition and Formation From A Computer Science Perspmentioning

confidence: 99%

See 2 more Smart Citations

The composition and formation of effective teams: computer science meets organizational psychology

Andrejczuk¹,

Berger

Rodŕıguez-Aguilar³

et al. 2018

The Knowledge Engineering Review

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Nowadays the composition and formation of effective teams is highly important for both companies to assure their competitiveness and for a wide range of emerging applications exploiting multiagent collaboration (e.g. crowdsourcing, human-agent collaborations). The aim of this article is to provide an integrative perspective on team composition, team formation and their relationship with team performance. Thus, we review the contributions in both the computer science literature and the organisational psychology literature dealing with these topics. Our purpose is twofold. First, we aim at identifying the strengths and weaknesses of the contributions made by these two diverse bodies of research. Second, we pursue to identify cross-fertilisation opportunities that help both disciplines benefit from one another. Given the volume of existing literature, our review is not intended to be exhaustive. Instead, we have preferred to focus on the most significant contributions in both fields together with recent contributions that break new ground to spur innovative research.1. Capacity: individual and social capabilities of agents; and 2. Personality: individual behaviour models.

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Do the Right Task! Supporting Volunteers Timetabling with Preferences Through the Sponsor Platform

Cesta

Coraci

Cortellessa

et al. 2019

Lecture Notes in Electrical Engineering

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Agent-Based Modeling of Resource Allocation in Software Projects Based on Personality and Skill

Cited by 11 publications

References 15 publications

Synergistic team composition: A computational approach to foster diversity in teams

Synergistic team composition: A computational approach to foster diversity in teams

The composition and formation of effective teams: computer science meets organizational psychology

Do the Right Task! Supporting Volunteers Timetabling with Preferences Through the Sponsor Platform

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