An Experimental Examination of Models of Bounded Rationality

经济学之外对“理性选择理论”的怀疑是基于这样一种信念，即人们会犯错误，并期望他人以一种无法完全预测的方式行事。经济学家已经开始认真对待这些批评，最近的理论研究已经确定了许多情况，在这些情况下，即使是对完全理性决策的微小偏离，也会导致预期收益的巨大变化。“有限理性”的影响在市场或游戏中尤为重要，因为预期收益函数的斜率对不影响纳什均衡底层结构的参数很敏感。本项目使用实验室实验来评估经济学家广泛关注的一系列情况下的行为：不完全价格竞争、协调、拍卖和互动模型。在每种情况下，均衡和动态学习模型的理论预测都是在一个广义的背景下推导出来的，这个背景跨越了一个极端的完全理性和另一个极端的完全随机决策。任何看过嘈杂的实验室数据的人都知道，大多数人的行为处于这两个极端之间的某个地方，这种直觉可以通过指定模型来评估，这种模型允许对误差进行计量经济学估计，并从实验室数据中学习参数。这些实验是由“嘈杂的”均衡和学习模型精心设计的，因此，处理方式的变化对纳什均衡没有影响，但它们确实改变了基于有限理性模型的预测。在这个建议中报告的初始数据模式很好地符合动态轨迹和最终平衡水平的预测。本研究的一个目标是提供一个数据库，以促进进一步的战略行为理论工作，这些工作是由仔细记录的经验规律指导的。

Skepticism about "rational choice theory" outside of economics is based on the conviction that people make mistakes and expect others to behave in a manner that is not perfectly predictable. Economists have started to take these criticisms seriously, and recent theoretical research has identified numerous situations in which even small departures from perfectly-rational decision making can cause a dramatic change in the landscape of expected payoffs. The effects of "bounded rationality" are particularly important in markets or games where the slopes of expected payoff functions are sensitive to parameters that do not affect the underlying structure of the Nash equilibria. This project uses laboratory experiments to evaluate behavior in a series of situations of wide interest to economists: imperfect price competition, coordination, auctions, and interactive models. In each case, the theoretical predictions of both equilibrium and dynamic learning models are derived in a generalized context that spans perfect rationality in one extreme and perfectly random decision making in the other. Anyone who has looked at noisy laboratory data knows that most human behavior lies somewhere in between these extremes, and this intuition can be evaluated by specifying models in a manner that permits econometric estimation of error and learning parameters from laboratory data.The experiments are carefully motivated by "noisy" equilibrium and learning models, so that the treatment changes have no effect on the Nash equilibria, but they do alter the predictions based on models of bounded rationality. Initial data patterns reported in this proposal conform nicely to both the dynamic trajectories and eventual equilibrium levels predicted. One goal of this research is to provide a data base that will stimulate further theoretical work on strategic behavior that is guided by carefully documented empirical regularities.