TRIBE analyzes team communications to predict performance outcomes. The system discovers hidden patterns of behaviors—and correlates these patterns with measurable results. Communication patterns serve as performance indicators, revealing early on to a task which teams could fail or succeed.
Using natural language processing and unsupervised machine learning, TRIBE identifies communication topics without predetermined categories, maps how teams distribute their attention across these topics, and finds which distributions correlate with their success or failure. The system requires only conversation data and performance metrics.
To showcase the system, we applied it to two collaborative team tasks. In controlled studies involving hundreds of teams, TRIBE could explain up to 93% of the variance in team performance, by leveraging a dimension reduction method. Teams at opposite ends of this dimension showed performance gaps exceeding 130%.
TRIBE transforms unstructured communication into quantifiable behavioral categories through sophisticated analysis techniques.
TRIBE operates through a systematic process that transforms raw communications into actionable performance predictions. The pipeline progresses through six stages, each building upon the previous to extract increasingly sophisticated behavioral insights.
Each stage uses established computational methods in novel combination. Topics emerge through various topic modeling techniques without supervision. Patterns self-organize through clustering algorithms. Performance correlations reveal themselves through dimensionality reduction. The innovation lies not in any single technique but in recognizing that team communication contains predictive signal that can be automatically extracted.
We demonstrate TRIBE's capabilities through analysis of two off-the-shelf datasets from collaborative team tasks: first, a search and defusal scenario, and then, a search and rescue scenario*.
*Artificial Social Intelligence for Successful Teams (ASIST) Program, DARPA, 2020-2024
To demonstrate TRIBE's capabilities, we analyzed data from a team task where team members collaborate in a Minecraft search and defusal scenario.
The study involved 231 three-person teams operating in a Minecraft-based simulation. Each team member was identified by color (red, blue, green) and had specialized capabilities. The task required two distinct phases:
Planning Phase: Teams gathered in a virtual shop where they could communicate verbally to strategize. They discussed approaches, assigned responsibilities, and coordinated intended actions. All verbal communication was recorded and transcribed for analysis. The timer would pause every time team entered a shop, allowing for unlimited number of team planning sessions.
Execution Phase: Teams deployed to the field where verbal communication was prohibited. They could only communicate through placing colored flags visible to teammates. Success depended entirely on the quality of their planning phase communication.
Performance was measured through objective scoring based on successful defusals, with scores ranging from -192 to 1080 points, meaning the team could take hit and lose score in unsuccessful defusals. This created ideal conditions for TRIBE analysis: natural team communication with clear, measurable outcomes.
TRIBE discovered 13 distinct communication topics in the planning conversations, and 6 behavioral pattern clusters. In order to make sense of what each cluster is focused on, we need to know what topic-combinations are forming the behavioral each cluster. The question is how can we reduce these 13 dimensions (topics) that describe the behaviors of the teams into a scale for change in communication patterns of each behavioral cluster?
Principal Component Analysis (PCA) is a statistical technique that simplifies complex data by finding the most important patterns of variation. Imagine you have 13 different measurements for each team (their emphasis on each of the 13 topics). PCA finds new dimensions—called principal components (PCs)—that capture the essential differences between teams while reducing this complexity. The first principal component (PC1) is the single dimension that explains the most variation in the data, the second (PC2) explains the next most, and so on. Each PC is a weighted combination of the original measurements, where the weights show how much each original variable contributes to that dimension.
In TRIBE's analysis, Principal Component Analysis revealed that despite 13 topics and 6 behavioral clusters, teams fundamentally varied along PC1 (the first principal component). This single axis captured nearly all performance-relevant variation. PC1 represents a specific blend of topics: teams with negative PC1 values emphasized topics 9 and 7 (completion-focused language), while teams with positive PC1 values emphasized topic 11 (role-focused language).
In this dataset, PC1 emerged as the dominant predictor, explaining 93% of performance variance. Other datasets may reveal multiple important components depending on their communication complexity. The key insight is that TRIBE automatically identifies whichever dimensions matter for performance.
Move the slider to explore how PC1 position correlates with team performance. Teams on the left (negative PC1) emphasize completion and execution topics, while teams on the right (positive PC1) focus more on role discussion. Below the sliding scale are the clusters with their corresponding average scores.
Note: The percentages show the emphasis level of each top 3 topic (out of 13 total) at the current PC1 position.
The six behavioral clusters showed dramatically different performance levels, perfectly ordered by their PC1 positions. The bold entries in the table below highlight the extreme performers—the top two highest-scoring clusters (941 and 876 points) and the lowest-scoring cluster (402 points). These extremes demonstrate the remarkable 539-point performance gap between teams with the best communication patterns versus those with the worst, representing more than a 2x difference in effectiveness.
The key to understanding PC1 lies in how different clusters emphasized different topics during planning.
Topic 9: Cluster 4 (best) devoted 42.1% of communication to this topic. Cluster 2 (worst) used it only 8%. A 5x difference.
Topic 7: Cluster 4 (best) shows 25.0% emphasis while Cluster 2 (worst) shows only 6.0%. A 4x difference.
Topic 11: Cluster 2 spent 32.1% on this topic. Cluster 4 used it only 5%. A 6x difference in opposite direction.
Teams with communication patterns mathematically similar to high performers achieved similar results. The correlation between pattern distance and performance reached r = -0.907 (p = 0.013).
This enables real-time assessment: measure a new team's topic distribution, calculate its distance from proven successful patterns, predict likely performance. The closer the match, the better the predicted outcome.
TRIBE was also applied to a Minecraft search and rescue scenario, demonstrating the system's versatility across different collaborative contexts.
This study involved 222 three-person teams performing urban search-and-rescue missions. Teams had to coordinate to locate and rescue victims while managing hazards. Unlike the defusal task, teams could communicate continuously throughout the 17-minute trials.
Communication Structure: Teams began with a 2-minute planning phase while locked in the entrance, then had 15 minutes of active gameplay with continuous voice communication.
Performance Metrics: Success was measured by victims saved and their severity levels, creating scores that reflected both speed and prioritization decisions.
While this task showed lower variance explanation, it revealed important behavioral dynamics:
TRIBE accurately found the changes in the behavior since most teams changed behavior after the first 2 minutes of planning, to taking action on the field. While the continuous communication format fostered more fluid behavioral patterns than the planning-only communication format of the defusal task, the strict 17-minute time limit imposed a constraint on behavioral expression that was absent in the defusal task, where unlimited planning sessions were allowed. This highlights how task structure shapes the manifestation of team behaviors and demonstrates TRIBE's ability to capture these dynamics.
TRIBE's domain-agnostic architecture means the same system that analyzes military planning sessions can assess corporate strategy meetings or emergency response coordination. The specific topics and patterns differ, but the underlying method remains constant: discover what teams discuss, measure how they discuss it, correlate patterns with outcomes, predict future performance.
The analyses demonstrate TRIBE's core capabilities and suggest broader applications.
Automatic Discovery: TRIBE found the critical patterns without being programmed to look for them. The system wasn't told that role discussion hurts performance or that completion focus helps. It discovered these relationships from data alone.
Dimensional Simplification: From 13 topics and countless communication patterns, TRIBE identified critical axes of variation. This reduction from high-dimensional data to actionable insight is what makes the system practical.
Quantitative Precision: The variance explanations aren't estimates—they're mathematical relationships. Given a team's behavioral scores, we can predict performance within calculable error bounds.
Domain Transfer: While different tasks show different patterns, the same architecture applies anywhere teams communicate and performance can be measured. Different domains will reveal different critical dimensions, but the method remains constant.
For more detailed technical information about TRIBE and its applications, please refer to the following publications:
1. Jalal-Kamali, A., Gurney, N.M. and Pynadath, D.V., 2025, May. Predicting Team Performance from Communications in Simulated Search-and-Rescue. In Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (pp. 2565-2567).
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2. Jalal-Kamali, A., 2024. Team-based Raw Interactions to Behavioral Ensembles (TRIBE): Predicting Team Performance from Communication Patterns (Doctoral dissertation). University of Southern California.
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