2017/02/14 by Noam Nisan, Nisan, Noam, Gali Noti +1
Business, Management and Accounting · Computer Science · Decision Sciences · Social Sciences · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Consumer Market Behavior and Pricing #Experimental Behavioral Economics Studies #FOS: Computer and information sciences #cs.GT
paper · pdf · doi:10.48550/arxiv.1702.04254
openalex publication_date 2017/02/14 · arxiv created 2017/02/16 · arxiv updated 2017/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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).