Behavioral game theorists all use experimental data to evaluate predictive models of human behavior. However, they differ greatly in their choice of loss function for these evaluations, with error rate, negative log-likelihood, cross-entropy, Brier score, and L2 error all being common choices. We attempt to offer a principled answer to the question of which loss functions make sense for this task, formalizing desiderata that we argue loss functions should satisfy. We construct a family of loss functions, which we dub "diagonal bounded Bregman divergences", that satisfy all of these axioms and includes the squared L2 error. In fact, the squared L2 error is the only acceptable loss that is relatively commonly used in practice; we thus recommend its continued use to behavioral game theorists.

翻译：行为博弈理论研究者均使用实验数据来评估人类行为的预测模型。然而，他们在选择用于评估的损失函数上存在显著差异，常见的选择包括错误率、负对数似然、交叉熵、布里尔分数和L2误差。我们试图为哪些损失函数适用于此任务提供原则性回答，并形式化我们认为损失函数应满足的若干期望性质。我们构建了一族损失函数，称之为"对角有界布雷格曼散度"，这些函数满足所有公理，并包含平方L2误差。事实上，平方L2误差是实践中相对常用的唯一可接受损失函数；因此，我们建议行为博弈理论研究者继续使用它。