Predicting Team Performance from Communications in Simulated Search-and-Rescue
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929743056601088 |
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| author | Jalal-Kamali, Ali Gurney, Nikolos Pynadath, David |
| author_facet | Jalal-Kamali, Ali Gurney, Nikolos Pynadath, David |
| contents | Understanding how individual traits influence team performance is valuable, but these traits are not always directly observable. Prior research has inferred traits like trust from behavioral data. We analyze conversational data to identify team traits and their correlation with teaming outcomes. Using transcripts from a Minecraft-based search-and-rescue experiment, we apply topic modeling and clustering to uncover key interaction patterns. Our findings show that variations in teaming outcomes can be explained through these inferences, with different levels of predictive power derived from individual traits and team dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03791 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Team Performance from Communications in Simulated Search-and-Rescue Jalal-Kamali, Ali Gurney, Nikolos Pynadath, David Artificial Intelligence Understanding how individual traits influence team performance is valuable, but these traits are not always directly observable. Prior research has inferred traits like trust from behavioral data. We analyze conversational data to identify team traits and their correlation with teaming outcomes. Using transcripts from a Minecraft-based search-and-rescue experiment, we apply topic modeling and clustering to uncover key interaction patterns. Our findings show that variations in teaming outcomes can be explained through these inferences, with different levels of predictive power derived from individual traits and team dynamics. |
| title | Predicting Team Performance from Communications in Simulated Search-and-Rescue |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.03791 |