Broader Applicability to Other Contexts啥意思

"Broader Applicability to Other Contexts" 的意思是指某个事物、方法或技术可以适用于更广泛的场景和情境，而不仅仅局限于某个特定领域或情况。例如，某个算法被开发出来可以适用于不同类型的数据集，或者某个产品设计可以适用于不同类型的用户群体。这种广泛适用性可以使事物更有价值，并且具有更多的应用前景。

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Broader Applicability to Other Contexts啥意思

"Broader applicability to other contexts" means that a particular solution or technology can be used in a wider range of situations or environments beyond its original intended use. It suggests that the solution or technology has the potential to be adapted to various contexts and can be useful in different scenarios. It is a desirable quality for a technology or solution as it can increase its versatility and usefulness.

We consider a persuasion problem between a sender and a receiver where utility may be nonlinear in the latter’s belief; we call such receivers risk conscious. Such utility models arise when the receiver exhibits systematic biases away from expected utility maximization, such as uncertainty aversion (e.g., from sensitivity to the variance of the waiting time for a service). Because of this nonlinearity, the standard approach to finding the optimal persuasion mechanism using revelation principle fails. To overcome this difficulty, we use the underlying geometry of the problem to develop a convex optimization framework to find the optimal persuasion mechanism. We define the notion of full persuasion and use our framework to characterize conditions under which full persuasion can be achieved. We use our approach to study binary persuasion, where the receiver has two actions and the sender strictly prefers one of them at every state. Under a convexity assumption, we show that the binary persuasion problem reduces to a linear program and establish a canonical set of signals where each signal either reveals the state or induces in the receiver uncertainty between two states. Finally, we discuss the broader applicability of our methods to more general contexts, and we illustrate our methodology by studying information sharing of waiting times in service systems翻译一下

我们考虑一个发件人和接收者之间的说服问题，其中接收者的效用可能在其信念中是非线性的；我们把这样的接收者称为风险感知者。这种效用模型出现在接收者偏离预期效用最大化的系统性偏差中，例如不确定性规避（例如，对于等待服务的等待时间方差的敏感性）。由于这种非线性，使用启示原则寻找最优说服机制的标准方法失败了。为了克服这个难点，我们利用问题的基本几何形状，开发了一个凸优化框架来找到最优的说服机制。我们定义了完全说服的概念，并使用我们的框架来表征实现完全说服的条件。我们使用我们的方法研究了二元说服问题，其中接收者有两个行动，发件人在每个状态下都严格偏好其中一个。在凸性假设下，我们证明了二元说服问题可以简化为线性规划，并建立了一个规范信号集，其中每个信号要么揭示状态，要么在接收者两个状态之间引起不确定性。最后，我们讨论了我们的方法对更一般情境的广泛适用性，并通过研究服务系统中等待时间的信息共享来说明我们的方法论。