Logit neural-network utility
提出一种名为Logit神经网络效用（NU）的随机选择模型，利用行为神经元捕捉确定性效应和参照依赖等行为现象，在包含正负奖金的彩票选择问题上，其样本外预测优于期望效用理论和累积前景理论。
We introduce stochastic choice models that feature neural networks, one of which is called the logit neural-network utility (NU) model. We show how to use simple neurons, referred to as behavioral neurons, to capture behavioral effects, such as the certainty effect and reference dependence. We find that simple logit NU models with natural interpretation provide better out-of-sample predictions than expected utility theory and cumulative prospect theory, especially for choice problems that involve lotteries with both positive and negative prizes. We also find that the use of behavioral neurons mitigates overfitting and significantly improves our models’ performance, consistent with numerous successes in introducing useful inductive biases in the machine-learning literature.