Collaborative Research: Sophisticated Learning and Strategic Teaching in Repeated Games
Collaborative Research: Sophisticated Learning and Strategic Teaching in Repeated Games
批准号:
0078911
负责人:
Colin Camerer
金额:
$24.46万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2000
资助国家:
美国
项目状态:
已结题
起止时间:
2000-08-01 至 2003-07-31
中文摘要
我们的研究解决了人们和组织如何在战略情况下从经验中学习,比如讨价还价,协调联合行动(团队合作),选择新产品的价格和特征,在拍卖中竞标等。在之前的研究中,我们发现了一个数学公式,它解释了人们如何从经验中学习,但公式的数值细节(它的“参数”)似乎因情况而异,好像人们以不同的方式学习。因此,我们建议探索为什么这些参数似乎有所不同。此外,大多数关于策略学习的数学理论都假设人们只回顾过去的经历。我们还建议扩展这些理论,以允许人们意识到其他人正在从经验中学习,因此能够根据过去发生的事情猜测其他人将会做什么。从这个意义上说,如果玩家是“老练的”,那么他们采取短期内不完美的行动,“教”其他正在学习采取行动的玩家,从长远来看,这些行动将使“老师”受益。这种教导可能对教师有益,但对社会有害(例如,当公司通过威胁非法报复吓跑创新的竞争对手时),或者对每个人都有益(例如,当公司向他人保证他们可以信任时)。我们的研究发展了一个精确的数学理论,关于玩家的行为有多复杂,以及他们什么时候值得教。我们用这个理论来解释实验的观察结果,并预测教学是否会在新的情况下发生。
英文摘要
Camerer #0078911Our research addresses how people and organizations learn from experiencein strategic situations like bargaining, coordinating joint actions(teamwork), choosing prices and features for new products, bidding inauctions, etc. In previous research we discovered a mathematical formulawhich explains how people appear to learn from experience, but thenumerical details of the formula (its "parameters) seem to vary fromsituation to situation, as if people are learning in different ways. Wetherefore propose to explore why these parameters seem to vary. Inaddition, most mathematical theories of strategic learning assume thatpeople only look back at past experiences. We also propose to extend thesetheories to allow for people who realize that other people are learningfrom experience, and are able to therefore outguess what others will dobased on what happened in the past. If players are "sophisticated", inthis sense, it pays for them to take actions that are not perfect in thenear-term, to "teach" other players who are learning to take actions whichwill benefit the "teachers" in the long-term. This teaching can bebeneficial for the teacher but bad for society (e.g., when firms scareaway innovative competitors by threatening illegal retaliation), orbeneficial for everyone (e.g., when firms reassure others that they can betrusted). Our research develops a precise mathematical theory of howsophisticated players behave and when it pays for them to teach. We usethe theory to explain observations from experiments and predicts whetherteaching will occur in new situations.
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How Biases in Individual Judgments Affect Market Outcome
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How Biases in Individual Judgments Affect Market Outcome
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财政年份:1985
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负责人:Colin Camerer
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依托单位:
国内基金
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