關(guān)于博弈論的英文論文翻譯，里面包含原文。
英文題目：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為2012年諾貝爾經(jīng)濟(jì)學(xué)獎(jiǎng)獲得者）
翻譯題目：學(xué)習(xí)模型和均衡模型作為有用的近似值：隨機(jī)選擇常數(shù)和博弈預(yù)測(cè)的精確性

中文摘要： 如果可以用充足的數(shù)據(jù)來(lái)拒絕一個(gè)理論值，但其結(jié)果可能是一個(gè)非常有用近似估計(jì)，實(shí)證者和理論家們?cè)谌绾卧u(píng)估這類理論時(shí)會(huì)產(chǎn)生很多的誤解。一個(gè)標(biāo)準(zhǔn)化的實(shí)證設(shè)計(jì)報(bào)告指出了通過(guò)信息類的測(cè)試案例到底能否用來(lái)拒絕某種一般性理論。相比之下，本文詳述了一個(gè)有意義的實(shí)驗(yàn)，旨在提出這個(gè)問(wèn)題:“在平均水平下，一個(gè)理論所提供近似值達(dá)到什么標(biāo)準(zhǔn)才算好”。這樣重點(diǎn)集中在一類隨機(jī)選擇博弈當(dāng)中,以及需要估計(jì)有多少對(duì)曾經(jīng)未經(jīng)查證的博弈數(shù)據(jù)實(shí)證對(duì)象要觀測(cè)。這樣做的實(shí)驗(yàn)觀測(cè)結(jié)果比找一組新的博弈主體所得出的實(shí)證結(jié)果要更好一些。我們稱這個(gè)模型為等效觀測(cè)量,并探索其屬性。
關(guān)鍵字：學(xué)習(xí)模型；均衡模型；常數(shù)和博弈；等效觀測(cè)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.