学习模型和均衡模型作为有用的近似值：随机选择常数和博弈预测的精确性（外文翻译）.zip
学习模型和均衡模型作为有用的近似值：随机选择常数和博弈预测的精确性（外文翻译）,关于博弈论的英文论文翻译，里面包含原文。英文题目：learning and equilibrium as useful approximations: accuracy of prediction on randomly selected constant sum games作者：ido erev · alvin e....
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原文档由会员 iloverson 发布
关于博弈论的英文论文翻译，里面包含原文。
英文题目：Learning and equilibrium as useful approximations: Accuracy of prediction on randomly selected constant sum games
作者：Ido Erev · Alvin E. Roth · Robert L. Slonim ·Greg Barron
（其中Roth为2012年诺贝尔经济学奖获得者）
翻译题目：学习模型和均衡模型作为有用的近似值：随机选择常数和博弈预测的精确性
中文摘要： 如果可以用充足的数据来拒绝一个理论值，但其结果可能是一个非常有用近似估计，实证者和理论家们在如何评估这类理论时会产生很多的误解。一个标准化的实证设计报告指出了通过信息类的测试案例到底能否用来拒绝某种一般性理论。相比之下，本文详述了一个有意义的实验，旨在提出这个问题:“在平均水平下，一个理论所提供近似值达到什么标准才算好”。这样重点集中在一类随机选择博弈当中,以及需要估计有多少对曾经未经查证的博弈数据实证对象要观测。这样做的实验观测结果比找一组新的博弈主体所得出的实证结果要更好一些。我们称这个模型为等效观测量,并探索其属性。
关键字：学习模型；均衡模型；常数和博弈；等效观测量ENO
英文再要：Abstract There is a good deal of miscommunication among experimenters and theorists about how to eva luate a theory that can be rejected by sufficient data, but may nevertheless be a useful approximation. A standard experimental design reports whether a general theory can be rejected on an informative test case. This paper, in contrast, reports an experiment designed to meaningfully pose the question:
“how good an approximation does a theory provide on average.” It focuses on a class of randomly selected games, and estimates how many pairs of experimental subjects would have to be observed playing a previously unexamined game before the mean of the experimental observations would provide a better prediction than
the theory about the behavior of a new pair of subjects playing this game. We call this quantity the model’s equivalent number of observations, and explore its properties.
英文题目：Learning and equilibrium as useful approximations: Accuracy of prediction on randomly selected constant sum games
作者：Ido Erev · Alvin E. Roth · Robert L. Slonim ·Greg Barron
（其中Roth为2012年诺贝尔经济学奖获得者）
翻译题目：学习模型和均衡模型作为有用的近似值：随机选择常数和博弈预测的精确性
中文摘要： 如果可以用充足的数据来拒绝一个理论值，但其结果可能是一个非常有用近似估计，实证者和理论家们在如何评估这类理论时会产生很多的误解。一个标准化的实证设计报告指出了通过信息类的测试案例到底能否用来拒绝某种一般性理论。相比之下，本文详述了一个有意义的实验，旨在提出这个问题:“在平均水平下，一个理论所提供近似值达到什么标准才算好”。这样重点集中在一类随机选择博弈当中,以及需要估计有多少对曾经未经查证的博弈数据实证对象要观测。这样做的实验观测结果比找一组新的博弈主体所得出的实证结果要更好一些。我们称这个模型为等效观测量,并探索其属性。
关键字：学习模型；均衡模型；常数和博弈；等效观测量ENO
英文再要：Abstract There is a good deal of miscommunication among experimenters and theorists about how to eva luate a theory that can be rejected by sufficient data, but may nevertheless be a useful approximation. A standard experimental design reports whether a general theory can be rejected on an informative test case. This paper, in contrast, reports an experiment designed to meaningfully pose the question:
“how good an approximation does a theory provide on average.” It focuses on a class of randomly selected games, and estimates how many pairs of experimental subjects would have to be observed playing a previously unexamined game before the mean of the experimental observations would provide a better prediction than
the theory about the behavior of a new pair of subjects playing this game. We call this quantity the model’s equivalent number of observations, and explore its properties.
