Level-0 meta-models for predicting human behavior in games
Level-0 meta-models for predicting human behavior in games
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DOI：
10.1145/2600057.2602907
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
J. R. Wright;Kevin Leyton-Brown
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文献类型：
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作者：
J. R. Wright;Kevin Leyton-Brown
行为博弈论试图描述现实中的人(与理想化的“理性”代理人相比)在战略情况下的行为方式。我们自己最近的工作已经确定迭代模型(如量子认知层次结构)是预测人类在不重复的同时移动游戏中玩游戏的最先进水平[Wright和Leyton-Brown 2012]。迭代模型预测，代理反复地对对手进行推理，建立在称为0级的非战略行为规范的基础上。原则上，建模师可以自由选择对给定设置有意义的任何0级行为描述；然而，在实践中，几乎所有现有工作都将此行为指定为动作上的统一分布。在大多数游戏中，即使是非战略代理人也不可能统一随机地选择一个行动，也不可能有其他代理人期望他们这样做。更准确的0级行为模型有可能显著改善对人类行为的预测，因为相当一部分代理可能直接玩0级策略，此外，因为迭代模型使所有更高级别的策略都对0级策略做出响应。我们的工作考虑了0级行为的“元模型”：在给定任意博弈的情况下，0级代理构建动作概率分布的方式的模型。我们评估了许多这样的元模型，每个模型都只基于可以从任何范式游戏中计算出的一般特征进行预测。我们评估了将每个新的0级元模型与各种迭代模型相结合的效果，在许多情况下观察到模型的预测精度有了很大的改善。最后，我们推荐了一个在所有方面都取得优异性能的元模型：需要估计五个权重的特征的线性加权。
Behavioral game theory seeks to describe the way actual people (as compared to idealized, ``rational'' agents) act in strategic situations. Our own recent work has identified iterative models (such as quantal cognitive hierarchy) as the state of the art for predicting human play in unrepeated, simultaneous-move games [Wright and Leyton-Brown 2012]. Iterative models predict that agents reason iteratively about their opponents, building up from a specification of nonstrategic behavior called level-0. The modeler is in principle free to choose any description of level-0 behavior that makes sense for the given setting; however, in practice almost all existing work specifies this behavior as a uniform distribution over actions. In most games it is not plausible that even nonstrategic agents would choose an action uniformly at random, nor that other agents would expect them to do so. A more accurate model for level-0 behavior has the potential to dramatically improve predictions of human behavior, since a substantial fraction of agents may play level-0 strategies directly, and furthermore since iterative models ground all higher-level strategies in responses to the level-0 strategy. Our work considers ``meta-models'' of level-0 behavior: models of the way in which level-0 agents construct a probability distribution over actions, given an arbitrary game. We evaluated many such meta-models, each of which makes its prediction based only on general features that can be computed from any normal form game. We evaluated the effects of combining each new level-0 meta-model with various iterative models, and in many cases observed large improvements in the models' predictive accuracies. In the end, we recommend a meta-model that achieved excellent performance across the board: a linear weighting of features that requires the estimation of five weights.