  Ever since Axelrod's seminal work, tournaments served as the main benchmark
for evaluating strategies in the Iterated Prisoner's Dilemma (IPD). In this
work, we first introduce a strategy for the IPD which outperforms previous
tournament champions when evaluated against the 239 strategies in the Axelrod
library, at noise levels in the IPD ranging from 0% to 10%. The basic idea
behind our strategy is to start playing a version of tit-for-tat which forgives
unprovoked defections if their rate is not significantly above the noise level,
while building a (memory-1) model of the opponent; then switch to a strategy
which is optimally adapted to the model of the opponent. We then argue that the
above strategy (like other prominent strategies) lacks a couple of desirable
properties which are not well tested for by tournaments, but which will be
relevant in other contexts: we want our strategy to be self-cooperating, i.e.,
cooperate with a clone with high probability, even at high noise levels; and we
want it to be cooperation-inducing, i.e., optimal play against it should entail
cooperating with high probability. We show that we can guarantee these
properties, at a modest cost in tournament performance, by reverting from the
strategy adapted to the opponent to the forgiving tit-for-tat strategy under
suitable conditions
