权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Imprecise Inference from Sequentially Presented Evidence

从顺序呈现的证据中得出不精确的推论

基本信息

批准号：
1949418
负责人：
Michael Woodford
金额：
$ 45万
依托单位：
Columbia University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2024-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949418&HistoricalAwards=false
关键词：
Imprecise Inference Sequentially Presented Evidence

项目摘要

Understanding how people make decisions is crucial to advancing our understanding of economic mechanisms. Rational-choice theory, despite successes in accounting for some aspects of human decision making under uncertainty, fails to capture certain recurrent patterns observed in behavior, such as biases and apparent randomness in choices. Some of these deviations from optimal behavior suggest that information is processed in the brain in a way that introduces imprecision, similar to the imprecision in sensory perception. A growing literature on perceptual judgments argues that in sensory contexts, these imprecisions often actually represent efficient adaptations, given constraints on the available cognitive resources. This raises a question as to whether patterns of imprecision in the case of higher-level cognitive processing (comparisons of numerical magnitudes, estimates of the average values of fluctuating series, and inference about unknown variables) cannot be understood in a similar way. We investigate this hypothesis through an array of experiments in which human subjects are asked to make decisions on the basis of multiple pieces of information, presented sequentially. Decisions made on the basis of sequential evidence are not only important in actual economic life, but also have the advantage as an object of study that they allow us to investigate how each piece of information is separately taken into account in the decision. This reveals the relative allocation of cognitive resources to the processing of each piece of evidence, which in turn sheds light on the underlying constraints faced by the brain. Overall, the main goal of our research is to provide an account of human judgments and economic decisions that is founded on normative principles, and that can be generalized to many problems of human decision-making.In more technical terms, we investigate, in the context of economic decisions, the neurocognitive hypothesis that the brain faces a problem of constrained optimization: that of allocating its limited information-processing resources to achieve the best possible decisions. From this general principle, we derive testable quantitative models of cognitive processing and decision making that make specific predictions regarding the bias and randomness in human choices. In particular, these suboptimal patterns are predicted to depend on prior beliefs about the probability distribution from which the presented evidence is drawn, and on the rewards associated with different responses in a given context. To test this hypothesis, we design six experiments, in which we manipulate both the prior and the reward structure, and we examine the degree to which our proposed models of constrained information processing capture the behavioral patterns found in experimental data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

理解人们如何做决定对于我们进一步理解经济机制至关重要。尽管个体选择理论成功地解释了人类在不确定性条件下决策的某些方面，但它未能捕捉到在行为中观察到的某些反复出现的模式，如选择中的偏见和明显的随机性。其中一些偏离最佳行为的行为表明，信息在大脑中的处理方式引入了不精确性，类似于感官知觉中的不精确性。越来越多的关于感知判断的文献认为，在感官环境中，这些不精确实际上往往代表了有效的适应，因为可用的认知资源受到限制。这就提出了一个问题，即在更高层次的认知处理（数值大小的比较、波动序列的平均值的估计以及对未知变量的推断）中，不精确的模式是否不能以类似的方式理解。我们通过一系列实验来研究这一假设，在这些实验中，人类受试者被要求根据顺序呈现的多条信息做出决定。根据连续证据作出的决策不仅在实际经济生活中很重要，而且作为研究对象，它们还具有这样的优势，即它们使我们能够研究在决策中如何分别考虑每一条信息。这揭示了认知资源在处理每一个证据时的相对分配，这反过来又揭示了大脑面临的潜在限制。总的来说，我们研究的主要目标是提供一个基于规范性原则的人类判断和经济决策的解释，并且可以推广到人类决策的许多问题。在更专业的术语中，我们在经济决策的背景下研究神经认知假设，即大脑面临约束优化问题：即分配有限的信息处理资源以实现最佳决策。从这个一般原则，我们得出可测试的定量模型的认知处理和决策，使具体的预测有关的偏见和随机性，在人类的选择。特别是，这些次优模式的预测依赖于先验的概率分布的信念，从所提出的证据，并在一个给定的上下文中与不同的反应相关联的奖励。为了验证这一假设，我们设计了六个实验，在其中，我们操纵的先验和奖励结构，我们检查的程度，我们提出的模型约束信息处理捕获的行为模式中发现的实验data.This奖反映了NSF的法定使命，并已被认为是值得支持的，通过使用基金会的智力价值和更广泛的影响审查标准进行评估。