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.

为了建模个人在不确定性下的选择，理论家通常假设选择使个人的效用最大化。 尽管经常对观察到的行为进行良好的描述，但在某些情况下，人们选择根据其相关奖励的概率选择替代方案。 这种概率匹配的行为是最佳的。 概率匹配行为和最佳行为都将取决于如果个人将选择基于采样算法，则可以根据做出决策的时间（在此产生更多最佳决策）。 在这项博士学位论文的提高授予中，PI将测试这种算法是否负责观察到的选择，此外，人们是否在最佳的次优（即，在他们何时或更低的情况下决定何时或更低的决定，都可以在这些假设方面评估其在何时及时进行贸易，从而在其何时进行折衷的情况下，将在其何时及时进行跨度的跨性能，从而在其方面进行了迅速的跨度确定，从而在何时进行了验证和准确的确定性方面进行了确定的确定性。然后，对受试者进行了跨决策领域的测试。