We examine the effect of cooperative and competitive interactions on the evolution of complex strategies in a prediction game. We extend previous work to the domain of noisy games, defining a new organism and mutation model, and an accompanying novel complexity metric. We find that a mix of cooperation and competition is the most effective in driving complexity growth, confirming prior results. We also compare our complexity metric with simpler metrics such as raw strategy size, and demonstrate the effectiveness of our metric in distinguishing true complexity from mere genetic bloat.

中文翻译：

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合作竞争噪声预测游戏中不断发展的复杂性

我们研究了合作和竞争互动对预测游戏中复杂策略演变的影响。我们将之前的工作扩展到嘈杂游戏领域，定义了一个新的有机体和突变模型，以及一个伴随的新复杂性度量。我们发现合作和竞争的结合在推动复杂性增长方面最有效，证实了先前的结果。我们还将我们的复杂性度量与更简单的度量（例如原始策略大小）进行了比较，并证明了我们的度量在区分真实复杂性与单纯遗传膨胀方面的有效性。