论文标题
无限大型策略空间实验中的启发式方法
Heuristics in experiments with infinitely large strategy spaces
论文作者
论文摘要
我们介绍了一种新方法，该方法可以在简单的重复游戏中检测到具有无限较大策略空间的简单重复游戏中的融合开始，从而揭示了决策中使用的启发式方法。该方法通过约束特殊有限的策略子集（称为解耦策略）来起作用。我们通过引入预测措施ΔD：积极的解耦策略（建议购买）和负面的脱钩策略（建议销售）来展示如何应用该技术来了解金融市场实验中的价格形成。使用ΔD，我们说明该方法在一系列实验中如何（在某些特殊时间）以高成功率预测参与者的行为
We introduce a new methodology that enables detection of the onset of convergence towards Nash equilibria in simple repeated games with infinitely large strategy spaces, thereby revealing the heuristics used in decision-making. The method works by constraining on a special finite subset of strategies, called decoupled strategies. We show how the technique can be applied to understand price formation in financial market experiments by introducing a predictive measure ΔD: the different between positive decoupled strategies (recommending to buy) and negative decoupled strategies (recommending to sell). Using ΔD we illustrate how the method can predict (at certain special times) participants' actions with a high success rate in a series of experiments
