  Convergence of resource allocation algorithms is well covered in the
literature as convergence to a steady state is important due to stability and
performance. However, research is lacking when it comes to the propagation of
change that occur in a network due to new nodes arriving or old nodes leaving
or updating their allocation. As change can propagate through the network in a
manner similar to how domino pieces falls, we call this propagation of change
the domino effect. In this paper we investigate how change at one node can
affect other nodes for a simple power control algorithm. We provide analytical
results from a deterministic network as well as a Poisson distributed network
through percolation theory and provide simulation results that highlight some
aspects of the domino effect. The difficulty of mitigating this domino effect
lies in the fact that to avoid it, one needs to have a margin of tolerance for
changes in the network. However, a high margin leads to poor system performance
in a steady-state and therefore one has to consider a trade-off between
performance and propagation of change.
