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首页> 外文期刊>Management science: Journal of the Institute of Management Sciences >David vs. Goliath: An Analysis of Asymmetric Mixed-Strategy Games and Experimental Evidence
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David vs. Goliath: An Analysis of Asymmetric Mixed-Strategy Games and Experimental Evidence

机译:David vs. Goliath:不对称混合策略博弈分析和实验证据

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摘要

Mixed strategies are widely used to model strategic situations in diverse fields such as economics, marketing, political science, and biology. However, some of the implications of asymmetric mixed-strategy solutions are counterintuitive. We develop a stylized model of patent race to examine some of these implications. In our model two firms compete to develop a product and obtain a patent. However, one firm values the patent more because of its market advantages, such as brand reputation and distribution network. Contrary to some intuition, we find that the firm that values the patent less is likely to invest more aggressively in developing the product and will also win the patent more often. We argue that the reason for these counterintuitive results is inherent in the very concept of mixed strategy solution. In a laboratory test, we examine whether subjects' behavior conforms to the equilibrium predictions. We find that the aggregate behavior of our subjects is consistent with the game-theoretic predictions. With the help of the experience-weighted attraction (EWA) learning model proposed by Camerer and Ho (1999), we show that adaptive learning can account for the investment behavior of our subjects. We find that the EWA learning model tracks the investment decisions of our subjects well, whether we hold out trials or an entire group of subjects.
机译:混合策略被广泛用于在诸如经济学,市场营销,政治学和生物学之类的不同领域中对战略形势进行建模。但是,非对称混合策略解决方案的某些含义是违反直觉的。我们开发了专利竞赛的程式化模型,以研究其中的某些含义。在我们的模型中,两家公司竞争开发产品并获得专利。但是,一家公司因其市场优势(例如品牌声誉和分销网络)而更加看重该专利。与某些直觉相反,我们发现,对专利的重视程度较低的公司可能会更积极地投资于产品开发,而且还会赢得更多专利。我们认为,产生这些违反直觉的结果的原因是混合策略解决方案的固有概念。在实验室测试中,我们检查对象的行为是否符合均衡预测。我们发现,我们受试者的总体行为与博弈论的预测是一致的。借助于Camerer和Ho(1999)提出的经验加权吸引力(EWA)学习模型,我们证明了自适应学习可以解释我们学科的投资行为。我们发现EWA学习模型可以很好地跟踪我们科目的投资决策,无论是进行试验还是整个科目。

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