Understanding team collapse via probabilistic graphical models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866929245403480064 |
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| author | Nikolaou, Iasonas Pelechrinis, Konstantinos Terzi, Evimaria |
| author_facet | Nikolaou, Iasonas Pelechrinis, Konstantinos Terzi, Evimaria |
| contents | In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational perspective. We use simulations and real-world experiments to find the main causes of team collapse. We also provide the principles of building resilient teams, i.e., teams that avoid collapsing. Finally, we use our model to analyze the structure of NBA teams and dive deeper into games of interest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10243 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding team collapse via probabilistic graphical models Nikolaou, Iasonas Pelechrinis, Konstantinos Terzi, Evimaria Physics and Society Machine Learning Social and Information Networks In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational perspective. We use simulations and real-world experiments to find the main causes of team collapse. We also provide the principles of building resilient teams, i.e., teams that avoid collapsing. Finally, we use our model to analyze the structure of NBA teams and dive deeper into games of interest. |
| title | Understanding team collapse via probabilistic graphical models |
| topic | Physics and Society Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2402.10243 |