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Surrogate Scoring Rules

机译：代理评分规则

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摘要

Strictly proper scoring rules (SPSR) are incentive compatible for eliciting information about random variables from strategic agents when the principal can reward agents after the realization of the random variables. They also quantify the quality of elicited information, with more accurate predictions receiving higher scores in expectation. In this article, we extend such scoring rules to settings in which a principal elicits private probabilistic beliefs but only has access to agents' reports. We name our solution Surrogate Scoring Rules (SSR). SSR is built on a bias correction step and an error rate estimation procedure for a reference answer defined using agents' reports. We show that, with a little information about the prior distribution of the random variables, SSR in a multi-task setting recover SPSR in expectation, as if having access to the ground truth. Therefore, a salient feature of SSR is that they quantify the quality of information despite the lack of ground truth, just as SPSR do for the setting with ground truth. As a by-product, SSR induce dominant uniform strategy truthfulness in reporting. Our method is verified both theoretically and empirically using data collected from real human forecasters.
机译：严格的评分规则(SPSR)动机兼容的捕获信息的随机的从战略代理变量校长可以奖励代理后实现的随机变量。引起的质量信息,和更多准确的预测得到更高的分数期望。评分规则设置的本金抒发私人概率信念但只有访问代理的报告。解决代理(SSR)评分规则。建立在偏见校正步骤,出错率估计过程的参考答案使用代理的定义报告。一个关于先验分布的信息的随机变量,SSR多任务设置恢复SPSR期望,好像有进入地面真理。SSR的特点是它们量化质量尽管缺乏地面真理的信息,设置与地面的SPSR一样真理。统一的战略报告的真实性。验证了理论和方法从真正的人类经验使用收集的数据预测者。

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