Computational principal–agent problems
Computational principal–agent problems
复制标题
计算委托代理问题
DOI：
10.3982/te1815
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发表时间：
2018
影响因子：
1.7
通讯作者：
S. Micali
中科院分区：
文献类型：
--
作者：
Pablo Azar;S. Micali
收集和处理大量数据在我们的社会中变得越来越重要。分布X。在我们的模型中，学习输入X的每个组件都是昂贵的，但是一旦已知x，我们将输出f（x）的成本为零。代理商的学习成本始终低于本金的相应成本。 - 使代理商以准确性e报告正确的值f（x）。
Collecting and processing large amounts of data is becoming increasingly crucial in our society. We model this task as evaluating a function f over a large vector x = (x1, . . . , xn), which is unknown, but drawn from a publicly known distribution X. In our model learning each component of the input x is costly, but computing the output f(x) has zero cost once x is known. We consider the problem of a principal who wishes to delegate the evaluation of f to an agent, whose cost of learning any number of components of x is always lower than the corresponding cost of the principal. We prove that, for every continuous function f and every e > 0, the principal can— by learning a single component x1 of x—incentivize the agent to report the correct value f(x) with accuracy e.