We study one-shot play in the set of all bimatrix games by a large population of agents. The agents never see the same game twice, but they can learn ‘across games’ by developing solution concepts that tell them how to play new games. Each agent’s individual solution concept is represented by a computer program, and natural selection is applied to derive a stochastically stable solution concept. Our aim is to develop a theory predicting how experienced agents would play in one-shot games. To use the theory, visit https://gplab.nhh.no/gamesolver.php.

中文翻译：

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冷玩：跨双子星游戏学习

我们研究了由大量特工组成的所有双矩阵游戏集中的单发游戏。代理从来不会两次见过相同的游戏，但是他们可以通过开发解决方案概念来学习“跨游戏”，告诉他们如何玩新游戏。每个代理的单独解决方案概念由计算机程序表示，并且采用自然选择来得出随机稳定的解决方案概念。我们的目标是发展一种理论，预测有经验的经纪人将如何进行单发游戏。要使用该理论，请访问https://gplab.nhh.no/gamesolver.php。