Given the rapid proliferation of smartphone applications and data aggregation websites, in many situations people can use decision aids to guide their choices. For example, they may consider whether to use a navigation device to check the fastest route or whether to use a price comparison website to find the cheapest deal. In what circumstances will subjects use a costly comparison decision aid (which I refer to as “checking”) to choose for them? In six studies, I investigate the impact of the number of available alternatives and checking's attractiveness on the decision to check. While at first increasing the attractiveness of checking led to higher checking rates, a further increase in the number of available alternatives (and thus checking's attractiveness) did not increase the checking rate. Surprisingly, even when checking had a 40% higher expected value compared with not checking, the observed checking rate was below 45%, contrary to risk and ambiguity aversion predictions. Furthermore, labeling the checking alternative as the default had no impact on its choice rate. I find large individual differences in decisions to check. Surprisingly, subjects' initial decisions had high predictive power over their subsequent checking rates, even after 100 trials with full feedback. I propose two simple learning models that capture well the aggregated results.

中文翻译：

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决定是否选择其他选择以及对违约行为的敏感性有限

鉴于智能手机应用程序和数据聚合网站的迅速普及，在许多情况下，人们可以使用决策辅助工具来指导他们的选择。例如，他们可能会考虑使用导航设备检查最快的路线，还是使用价格比较网站来查找最便宜的交易。在什么情况下，受试者会使用昂贵的比较决策辅助工具（我称之为“检查”）为他们选择？在六项研究中，我调查了可用替代品的数量以及检查的吸引力对检查决定的影响。虽然首先增加了检查的吸引力会导致更高的检查率，但可用替代方法的数量（进而是检查的吸引力）的进一步增加并没有增加检查率。出奇，即使检查的期望值比未检查的期望值高40％，观察到的检查率也低于45％，这与风险和歧义厌恶的预测相反。此外，将检查选项标记为默认选项不会对其选择率产生影响。我发现要检查的决定存在很大的个体差异。出乎意料的是，即使经过100次有充分反馈的试验，受试者的最初决定仍对其随后的检查率具有较高的预测能力。我提出了两个简单的学习模型，它们可以很好地捕获汇总结果。出乎意料的是，即使经过100次有充分反馈的试验，受试者的最初决定对其随后的检查率也具有很高的预测能力。我提出了两个简单的学习模型，它们可以很好地捕获汇总结果。出乎意料的是，即使经过100次有充分反馈的试验，受试者的最初决定对其随后的检查率也具有很高的预测能力。我提出了两个简单的学习模型，它们可以很好地捕获汇总结果。