论文标题
从信息成本预测选择
Predicting Choice from Information Costs
论文作者
论文摘要
在做出决定之前，代理会获得昂贵的灵活信号。我们探讨了对代理信息成本的程度知识有助于预测她的行为。我们确定了一个不可能的结果：仅学习成本就不会产生对选择的可检验限制，而不会对行动的依赖性公用事业施加限制。相比之下，菜单中的选择通常会独特地固定在所有子菜单中。为了证明后者的结果，我们定义了迭代可分化的成本函数，这是一种可与一阶技术合适的类班级。最后，我们针对多人数据集构建了严格的测试，以与给定的成本一致。
An agent acquires a costly flexible signal before making a decision. We explore to what degree knowledge of the agent's information costs helps predict her behavior. We establish an impossibility result: learning costs alone generate no testable restrictions on choice without also imposing constraints on actions' state-dependent utilities. By contrast, choices from a menu often uniquely pin down the agent's decisions in all submenus. To prove the latter result, we define iteratively differentiable cost functions, a tractable class amenable to first-order techniques. Finally, we construct tight tests for a multi-menu data set to be consistent with a given cost.
