The past few years have seen several works establishing PAC frameworks for solving various problems in economic domains; these include optimal auction design, approximate optima of submodular functions, stable partitions and payoff divisions in cooperative games and more. In this work, we provide a unified learning-theoretic methodology for modeling these problems, and establish some useful tools for determining whether a given economic solution concept can be learned from data. Our learning theoretic framework generalizes a notion of function space dimension --- the graph dimension --- adapting it to the solution concept learning domain. We identify sufficient conditions for the PAC learnability of solution concepts, and show that results in existing works can be immediately derived using our general methodology. Finally, we apply our methods in other economic domains, yielding a novel notion of PAC competitive equilibrium and PAC Condorcet winners.

该论文提出了一种统一的学习理论方法来建模经济问题，通过适应解决方案概念学习域的函数空间维数的概念 - 图形维度 - 建立工具，识别了解决方案概念的PAC可学习性的充分条件，并说明现有作品中的结果可以立即使用我们的方法来推导。最后，我们将我们的方法应用于其他经济领域，得出了PAC竞争均衡和PAC Condorcet胜者的新概念。

基于分布学习的博弈论解决方案框架