  We suggest a general method for inferring players' values from their actions
in repeated games. The method extends and improves upon the recent suggestion
of (Nekipelov et al., EC 2015) and is based on the assumption that players are
more likely to exhibit sequences of actions that have lower regret.
  We evaluate this "quantal regret" method on two different datasets from
experiments of repeated games with controlled player values: those of (Selten
and Chmura, AER 2008) on a variety of two-player 2x2 games and our own
experiment on ad-auctions (Noti et al., WWW 2014). We find that the quantal
regret method is consistently and significantly more precise than either
"classic" econometric methods that are based on Nash equilibria, or the
"min-regret" method of (Nekipelov et al., EC 2015).
