  Best-response mechanisms (Nisan, Schapira, Valiant, Zohar, 2011) provide a
unifying framework for studying various distributed protocols in which the
participants are instructed to repeatedly best respond to each others'
strategies. Two fundamental features of these mechanisms are convergence and
incentive compatibility.
  This work investigates convergence and incentive compatibility conditions of
such mechanisms when players are not guaranteed to always best respond but they
rather play an imperfect best-response strategy. That is, at every time step
every player deviates from the prescribed best-response strategy according to
some probability parameter. The results explain to what extent convergence and
incentive compatibility depend on the assumption that players never make
mistakes, and how robust such protocols are to "noise" or "mistakes".
