  Using elementary distributed computing techniques we suggest an explanation
for two unexplained phenomena in regards to ant colonies, (a) a substantial
amount of ants in an ant colony are idle, and (b) the observed low
survivability of new ant colonies in nature. Ant colonies employ task
allocation, in which ants progress from one task to the other, to meet changing
demands introduced by the environment. Extending the biological task allocation
model given in [Pacala, Gordon and Godfray 1996] we present a distributed
algorithm which mimics the mechanism ants use to solve task allocation
efficiently in nature. Analyzing the time complexity of the algorithm reveals
an exponential gap on the time it takes an ant colony to satisfy a certain work
demand with and without idle ants. We provide an $O(\ln n)$ upper bound when a
constant fraction of the colony are idle ants, and a contrasting lower bound of
$\Omega(n)$ when there are no idle ants, where $n$ is the total number of ants
in the colony.
