We suggest a general method for inferring players\u2019 values from their actions\u00a0in repeated games. The method extends and improves upon the recent\u00a0suggestion of (Nekipelov et al., EC 2015) and is based on the assumption\u00a0that players are more likely to exhibit sequences of actions that have lower\u00a0regret.<\/p>\n
We evaluate this \u201cquantal regret\u201d method on two different datasets from\u00a0experiments of repeated games with controlled player values: those of (Selten\u00a0and Chmura, AER 2008) on a variety of two-player 2×2 games and our\u00a0own experiment on ad-auctions (Noti et al., WWW 2014). We find that the\u00a0quantal-regret method is consistently and significantly more precise than either\u00a0\u201cclassic\u201d econometric methods that are based on Nash equilibria, or the\u00a0\u201cmin-regret\u201d method of (Nekipelov et al., EC 2015).<\/p>\n","protected":false},"excerpt":{"rendered":"
We suggest a general method for inferring players\u2019 values from their actions\u00a0in repeated games. The method extends and improves upon the recent\u00a0suggestion of (Nekipelov et al., EC 2015) and is based on the assumption\u00a0that players are more likely to exhibit sequences of actions that have lower\u00a0regret. We evaluate this \u201cquantal regret\u201d method on two different 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