Optimizing competence in the service of collaboration

Area of Science:

• Cognitive Science
• Social Psychology
• Human-Computer Interaction

Background:

• Effective collaboration relies on understanding collaborator competence.
• Competence is plastic, improving with task experience, posing challenges for optimal task allocation.

Purpose of the Study:

• To investigate how individuals decide whom to train or recruit for collaborative tasks.
• To model decision-making based on initial expertise, training potential, and task goals.

Main Methods:

• Four experiments involving 396 participants were conducted.
• Participants made decisions about training and recruitment for simulated collaborative tasks.
• Decision-making was analyzed against various predictive models.

Main Results:

• Participant decisions aligned with a planning model maximizing collaboration returns and minimizing training costs.
• This planning model outperformed models focused solely on current competence or short-term improvement.
• Decisions considered long-term task success potential after training.

Conclusions:

• People do not solely base recruitment/training on current competence or immediate skill gains.
• An intuitive theory of competence balances hiring/training costs with collaboration benefits.
• This suggests a sophisticated, long-term strategic approach to team formation and development.