# A "Quantal Regret" Method for Structural Econometrics in Repeated Games

arXiv: https://arxiv.org/abs/1702.04254 · HTML: https://mlsanity.com/paper/1702.04254/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2017.

## Built on by (most cited first, 3 of 3)

- [Auctions Between Regret-Minimizing Agents](https://mlsanity.com/paper/2110.11855/) (2021)
- [Games with Payments between Learning Agents](https://mlsanity.com/paper/2405.20880/) (2024)
- [From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactions](https://mlsanity.com/paper/2112.15151/) (2021)

## Abstract (arXiv)

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).