TRIBE

To demonstrate TRIBE's capabilities, we analyzed data from a team task where team members collaborate in a Minecraft search and defusal scenario.

Experimental Design

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.

Dimension Reduction

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.

Interactive Performance Predictor

Cluster Performance Analysis

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.

Topic Distribution Patterns

The key to understanding PC1 lies in how different clusters emphasized different topics during planning.

Critical Differences

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.

Pattern Similarity Predicts Performance

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.

Task Design

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.

Key Findings

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.