Training Collaborators for Effective Division of Labor

Area of Science:

• Cognitive Science
• Organizational Behavior
• Human-Computer Interaction

Background:

• Collaboration leverages complementary strengths through division of labor.
• Optimal division of labor requires anticipating individual skill development and future role suitability.
• Existing research on collaboration often overlooks this prospective, developmental dimension.

Purpose of the Study:

• To investigate how humans make training decisions in collaborative contexts.
• To examine the influence of long-term consequences on training choices.
• To understand the prospective dimension of division of labor.

Main Methods:

• Conducted three experiments with 600 participants.
• Participants trained two military defense teams against land and air attacks.
• Manipulated long-term goals and team competences, assessing training decisions and final role assignments.

Main Results:

• Participants divided labor and trained teams considering future competence development and task demands.
• A Planning model, predicting outcomes at deployment, best explained observed training patterns.
• The Planning model outperformed heuristics based on current competence, learning potential, fairness, or versatility.

Conclusions:

• Human training decisions in collaboration anticipate future specialization.
• Effective division of labor involves actively cultivating competences for future roles.
• This study provides foundational insights into how training enhances collaborative benefits through specialized skills.