The limits of identification in discrete choice
揭示了在识别随机效用模型中，可允许的偏好数量存在严格上界，随着备选方案数量增大，可识别偏好比例迅速趋近于零，并提出了一个更弱的充分条件。
This paper uncovers tight bounds on the number of preferences permissible in identified random utility models. We show that as the number of alternatives in a discrete choice model becomes large, the fraction of preferences admissible in an identified model rapidly tends to zero. We propose a novel sufficient condition ensuring identification, which is strictly weaker than some of those existing in the literature. While this sufficient condition reaches our upper bound, an example demonstrates that this condition is not necessary for identification. Using our new condition, we show that the classic “Latin Square” example from social choice theory is identified from stochastic choice data.