Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or choice situation) depend on the previous choices of the same individual (as well as the alternative attributes in the previous menus). Nevertheless, the literature is indecisive on whether the parameter estimates in such cases are consistent or not. In this paper, we discuss cases where the estimates are consistent and those where they are not. We provide a theoretical explanation for this discrepancy and discuss the implications on the design of these systems and on model estimation. We conclude that endogeneity is not a concern when the likelihood function properly accounts for the data generation process. This can be achieved when the system is initialized exogenously and all the data are used in the estimation. In line with previous literature, Monte Carlo results suggest that, even when exogenous initialization is missing, empirical bias decreases with the number of choices per individual. We conclude by discussing the practical implications and extensions of this research.

内生性由于多种因素而在离散选择模型中产生，并导致模型参数的估计不一致。在诸如基于选择的推荐系统和适应性陈述偏好（ASP）调查之类的适应性选择上下文中，内生性是可预期的，因为在特定菜单（或选择情况）下呈现给个人的属性取决于同一个人的先前选择（如以及之前菜单中的替代属性）。然而，文献对于这种情况下的参数估计是否一致尚不确定。在本文中，我们讨论了估计值一致和不一致的情况。我们为这种差异提供了理论解释，并讨论了对这些系统的设计和模型估计的影响。我们得出结论，当似然函数正确地说明了数据生成过程时，内生性就无关紧要。当系统进行外部初始化并将所有数据用于估算时，可以实现此目的。与以前的文献一致，蒙特卡洛的结果表明，即使缺少外部初始化，经验偏差也会随着每个人的选择数量而减少。最后，我们讨论了这项研究的实际意义和扩展。经验偏差随着每个人的选择数量而减少。最后，我们讨论了这项研究的实际意义和扩展。经验偏差随着每个人的选择数量而减少。最后，我们讨论了这项研究的实际意义和扩展。