Doctoral Dissertation Research in DRMS: Boundedly optimal sampling for decisions under uncertainty

DRMS 博士论文研究：不确定性下决策的有限最优抽样

• 批准号: 0850414
• 负责人: Nancy Kanwisher
• 金额: $ 2.34万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-15 至 2010-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0850414&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; DRMS; Boundedly

To model an individual's choices under uncertainty, theorists typically assume the choices made maximize the individual's utility. While frequently a good description of observed behavior, there are instances where people instead choose alternatives in proportion to their associated probabilities of reward. This probability matching behavior is sub-optimal. Probability matching behavior and optimal behavior would both result depending on the time available to make decisions (where more time produces more optimal decisions) if individuals base their choices on a sampling algorithm. In this Doctoral Dissertation Improvement grant, the PI will test whether such an algorithm is responsible for observed choices and, furthermore, whether people are optimally suboptimal (i.e., optimal in their decision regarding when to be more, or less, optimal.To test these hypotheses, experimental subjects will be assessed in terms of how flexible they are at making tradeoffs between speed and accuracy in motor decisions under uncertainty and how generic decision processes are across decision domains. Subjects are then tested for whether their decisions under cognitive stress deteriorate to probability-matching, as predicted by the proposed algorithm. Finally, subjects will be tested using fMRI to determine whether one brain structure represents expected utility arising from different sources of uncertainty. This research holds promise for reconciling models of humans as ideal agents with established failures and limitations of human decision-making.

为了对不确定性下个人的选择进行建模，理论家通常假设所做的选择最大化了个人的效用。 虽然通常可以很好地描述观察到的行为，但在某些情况下，人们会根据相关的奖励概率来选择替代方案。 这种概率匹配行为是次优的。 如果个人的选择基于抽样算法，则概率匹配行为和最优行为都将取决于可用于做出决策的时间（其中更多时间会产生更最优的决策）。 在这项博士论文改进资助中，PI将测试这样的算法是否对观察到的选择负责，此外，人们是否处于最佳次优状态（即，在关于何时更多或更少最优的决策中处于最佳状态）。为了测试这些假设，实验对象将根据他们在不确定性下在运动决策的速度和准确性之间进行权衡的灵活性以及跨决策领域的通用决策过程进行评估，然后测试他们在认知压力下的决策是否恶化。最后，将使用功能磁共振成像来测试受试者，以确定一种大脑结构是否代表不同来源的不确定性所产生的预期效用，这项研究有望将人类作为理想代理人的模型与既定的失败相协调。以及人类决策的局限性。