  The wisdom of the crowd breaks down in small groups. While large flocks
exhibit swarm intelligence to evade predators, small groups display erratic
behavior, oscillating between unity and discord. We investigate these dynamics
using small groups of sheep controlled by shepherd dogs in century-old sheepdog
trials, proposing a two-parameter stochastic dynamic framework. Our model
employs pressure (stimulus intensity) and lightness (response isotropy) to
simulate herding and shedding behaviors. Light sheep rapidly achieve a stable
herding state, while heavy sheep exhibit intermittent herding and orthogonal
alignment to the dog. High response isotropy enhances group cohesion but
complicates group splitting. We construct a unified phase diagram for sheep
behavior, identifying three regimes (fleeing, flocking, and grazing) based on
group size and stimulus specificity. Increasing stimulus specificity shifts
small group behavior from grazing to fleeing, while larger groups exhibit
flocking. This transition underscores the challenge of controlling small
indecisive collectives. Introducing the Indecisive Collective Algorithm (ICA),
we show that deliberate indecisiveness and stochasticity improve control
efficiency. ICA outperforms traditional averaging-based algorithms in
high-noise settings and excels in tasks requiring group splitting. Our study
offers a foundational framework for controlling small, indecisive groups,
applicable to biochemical reactions, cell populations, and opinion dynamics.
