Improving performance through team composition

A simulation of specialists vs. generalists to identify optimal team structures.

The challenge

How should a team be structured to perform well under uncertainty?

Using agent-based modeling, this project identifies when specialists, generalists, or mixed teams are most effective, informing strategies for adaptive division of labor.

My role & methods

At the Swarm Lab, I built a full simulation in R with virtual workers as specialists or generalists. Specialists contributed fully to one task type and nothing to others, while generalists contributed moderately across all tasks.

I ran 100 iterations for 3,990 parameter combinations, modeling both predictable (“to-do list”) and unpredictable (“blind”) task selection, and analyzed the results using R.

Impact & recognition

Internship at the Swarm Lab graded with the highest mark as part of my MSc in Cognitive Science

Presented at various conferences, including the Annual Conference of the Animal Behavior Society (2020, 2022) and Frontiers in Applied and Computational Mathematics (2022)

Teams with different mixes of specialists and generalists (0–100% generalists) work in either predictable (to-do list selection) or unpredictable (blind selection) task environments.

In predictable environments, pure teams performed equally well, but mixed teams were less effective, especially those with about 40% generalists and 60% specialists.

Median performance (higher values = more time to complete tasks) for groups of 10 workers, shown by specialist–generalist ratio, number of tasks, and task selection type (“to-do list” vs. “blind”). Ratio 0.0 = all specialists, 1.0 = all generalists, intermediate values = mixed teams. Left to right on the ratio-axis: specialists decrease, generalists increase.

In unpredictable environments, generalists performed more consistently, showing less variability in group performance than specialists.

Performance dispersion (interquartile range) for groups of 10 workers,  shown by specialist–generalist ratio, number of tasks, and task selection type (“to-do list” vs. “blind”). Ratio 0.0 = all specialists, 1.0 = all generalists, intermediate values = mixed teams. Left to right on the ratio-axis: specialists decrease, generalists increase.