Team Machine: A Decision Support System for Team Formation

Bergey, Paul K.; King, Mark

doi:10.1111/dsji.12027

Cited by 19 publications

(31 citation statements)

References 69 publications

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“…Studies of multi-player games have consequently focused on understanding what motivates players' choices of teammates and the relationship with performance, with one key finding being that large variations in competence within teams discourages repeated interactions (Alhazmi et al 2017). This is in line with findings from collaborative learning environments, in which approaches that focus on automatically-forming optimally balanced student teams have been found to perform better than manual allocation to the student teams by experts (Yannibelli and Amandi 2012;Bergey and King 2014). Considering feedback from student collaborations has also been found to improve the group formation process (Srba and Bielikova 2015).…”

Section: Advantages and Challenges Of Collaborative Gamesmentioning

confidence: 76%

Reflective agents for personalisation in collaborative games

2018

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The collaborative aspect of games has been shown to potentially increase player performance and engagement over time. However, collaborating players need to perform well for the team as a whole to benefit and thus teams often end up performing no better than a strong player would have performed individually. Personalisation offers a means for improving overall performance and engagement, but in collaborative games, personalisation is seldom implemented, and when it is, it is overwhelmingly passive such that the player is not guided to goal states and the effectiveness of the personalisation is not evaluated and adapted accordingly. In this paper, we propose and apply the use of reflective agents to personalisation ('reflective personalisation') in collaborative gaming for individual players within collaborative teams via a combination of individual player and team profiling in order to improve player and thus team performance and engagement. The reflective agents self-evaluate, dynamically adapting their personalisation techniques to most effectively guide players towards specific goal states, match players and form teams. We incorporate this agent-based approach within a microservices architecture, which itself is a set of collaborating services, to facilitate a scalable and portable approach that enables both player and team profiles to persist across multiple games. An experiment involving 90 players over a two-month period was used to comparatively assess three versions of a collaborative game that implemented reflective, guided, and passive personalisation for individual players within teams. Our results suggest that the proposed reflective personalisation approach improves team player performance and engagement within collaborative games over guided or passive personalisation approaches, but that it is especially effective for improving engagement.

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Section: Advantages and Challenges Of Collaborative Gamesmentioning

confidence: 76%

Reflective agents for personalisation in collaborative games

2018

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“…Proper application of sociotechnical theories in organizations to better understand human and technology interactions have also been proven as a factor of team effectiveness . The optimal human‐machine team design and formation through mathematically supported decision tools have yielded higher performance compared to manually selected teams . Fuzzy sets have been used as a useful approach for analyzing the interaction between team members and identifying the optimal team design …”

Section: Related Workmentioning

confidence: 99%

An agent‐based model for improved system of systems decision making in air transportation

Esmaeilzadeh

Grenn

Roberts

2018

Systems Engineering

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Team collaboration and decision making have a significant role in the overall performance of complex system of systems (SoS). Improved systems engineering (SE) processes and tools are needed during the systems development lifecycle to make decisions and rapidly evaluate and assess multiple design alternatives to effectively select architectural design strategies that result in the highest mission performance. Therefore, the early and consistent evaluation of these strategies and collaborative team decisions are essential. The degree of utility of alternative decision strategy designs may vary given the condition, but conditions are not always considered when these decisions are being made. This article applies the SE principles of design for change and flexibility in an agent-based model for simulation of design alternatives for systems analysis and decision making in evaluating and selecting the decision(s) for SoS comprised of collaborative teams that result in higher mission performance. The authors apply the proposed agent-based model to test the flight delay within the SoS structure of the air transportation domain. The flight delays are due to the management strategies initiated by the air traffic facilities to balance the air demand and capacity when conditions are not normal (such as severe weather). The experimental results suggest that:(1) the current strategies may result in unnecessary delays and underdelivery of flights to the airport during the hours where demand is well below capacity; (2) aggressive strategies may have the reverse effect; and (3) longer-duration strategies have a more significant impact on delays during severe weather conditions. K E Y W O R D Sagent-based simulation, system design and evaluation, system of systems 20

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“…Another type of academic research has focused on structural analysis and how different team issues constitute a whole (Basiri et al, ; Bergey and King, ). Fathian et al () developed a team formation optimization model that focuses on studying three team system variables: (i) expert skills, (ii) expert collaboration network and (iii) expert reliability.…”

Section: Existing Team Formation Solutionsmentioning

confidence: 99%

Team Formation Using a Systems Thinking Approach

Sánchez-Segura

Hadžikadić²,

Dugarte-Peña

et al. 2018

Syst. Res

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Team performance varies depending on the organization of their members, the exploitation of their synergies and their planning to optimally meet the minimum requirements with the best results. This article reports a dual-purpose methodology. First, we use the soft systems methodology to start up viability-driven teamwork based on team complexity, a better understanding of idiosyncrasy, and the synergies leveraging team behavior. The viable systems model is used to propose the essential functions of a team that effectively operates in dynamic organizational environments. Second, this research provides guidance for training teams to apply holistic thinking in their problem-solving practice by helping them to co-integrate and apply systemic approach for immersive problem solving. In essence, this article reports success drivers, generic teamwork structures and viability accomplishment references for agilely, effectively and synergistically launching teams. We wrap up by discussing the lessons learned from applying this approach to several teams and outline future developments.

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Team Machine: A Decision Support System for Team Formation

Cited by 19 publications

References 69 publications

Reflective agents for personalisation in collaborative games

Reflective agents for personalisation in collaborative games

An agent‐based model for improved system of systems decision making in air transportation

Team Formation Using a Systems Thinking Approach

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