SEQUENTIAL CHOICE UNDER AMBIGUITY - INTUITIVE SOLUTIONS TO THE ARMED-BANDIT PROBLEM
SEQUENTIAL CHOICE UNDER AMBIGUITY - INTUITIVE SOLUTIONS TO THE ARMED-BANDIT PROBLEM
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DOI：
10.1287/mnsc.41.5.817
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发表时间：
1995-05-01
影响因子：
5.4
通讯作者：
SHI, Y
中科院分区：
文献类型：
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作者：
MEYER, RJ;SHI, Y
研究了个体在模棱两可的选择中反复做出选择时从反馈中学习的过程。我们描述了一个实验，在这个实验中，受试者在航空公司选择的背景下解决了动态决策理论中经典的武装强盗问题的一个变体。受试者被要求在两家假设的航空公司之间反复做出选择，其中一家的准点起飞概率是先验的，而另一家的准点起飞概率是模糊的，其真实值只能通过在该航空公司进行抽样旅行来发现。在固定的计划范围内，受试者试图以这样一种方式做出选择，使一次性离开的总数最大化。我们考察了实际选择模式随着时间的推移与决策者作为最优伯努利采样器所做的选择模式相一致的程度。这些数据为一些预期的——以及一些意想不到的——偏离最优状态提供了支持，包括对有希望的选择试验不足的趋势和对没有希望的选择试验过度的趋势，以及随着平均基本离境率的下降，越来越多地在航空公司之间切换的趋势。本文探讨了规范性动态决策模型的描述有效性的含义，以及之前关于模糊情况下选择的研究结果对动态设置的概括性。
The process by which individuals learn from feedback when making recurrent choices among ambiguous alternatives is explored. We describe an experiment in which subjects solve a variant of the classic armed-bandit problem of dynamic decision theory, set in the context of airline choice. Subjects are asked to make repeated choices between two hypothetical airlines, one having an on-time departure probability which is known a priori, and the other has an ambiguous probability whose true value can only be discovered by making sample trips on the airline. Subjects attempt to make choices in such a way as to maximize the total number of one-time departures over a fixed planning horizon. We examine the extent to which actual choice patterns over time are consistent with those which would be made by a decision maker acting as an optimal Bernoulli sampler. The data offer support for a number of expected-and some unexpected-departures from optimality, including a tendency to underexperiment with promising options and overexperiment with unpromising options, and a tendency to increasingly switch between airlines as the average base rate of departures decreases. Implications of the work for the descriptive validity of normative dynamic decision models is explored, as well as for the generalizability of previous findings about choice under ambiguity to dynamic settings.