Cognitively Plausible Models of Decision Making

认知上合理的决策模型

• 批准号: 8677404
• 负责人: Elizabeth Eve Bruch
• 金额: $ 13.07万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8677404
• 关键词: Alcohol consumption; Architecture; Area; Bayesian Analysis; Behavior; Behavioral; Behavioral Model; Businesses; California; Cause of Death; Choice Behavior; Cognitive; Cognitive Science; Complex; Computer software; Conceptions; Coupled; Data; Data Set; Decision Making; Decision Theory; Demography; Diet; Disease; Disease Management; Doctor of Philosophy; Eating; Education; Educational process of instructing; Environment; Estimation Techniques; Evolution; Exercise; Family; Feedback; Food; Goals; Grant; Habits; Health; Health Care Costs; Healthcare; Household; Human; Individual; Injury; Intervention; Investigation; K-Series Research Career Programs; Learning; Link; Literature; Los Angeles; Market Research; Marketing; Mentors; Methods; Michigan; Modeling; Neighborhoods; Occupations; Outcome; Partner in relationship; Pattern; Persons; Policies; Population Dynamics; Population Research; Population Study; Prevention; Price; Process; Psychological Techniques; Psychologist; Psychology; Recommendation; Research; Research Methodology; Research Personnel; Research Project Grants; Research Scientist Award; Residential Mobility; Resources; Schools; Science; Sex Behavior; Shapes; Site; Social Environment; Social Sciences; Sociology; Sorting - Cell Movement; Specific qualifier value; Staging; Statistical Models; Stratification; Students; Surveys; System; Techniques; Time; Tobacco use; Training; United States; Universities; Work; Writing; base; behavior change; cost; data management; heuristics; high risk sexual behavior; insight; learned behavior; population health; preference; programs; prototype; public health relevance; public health research; residence; response; screening; segregation; simulation; skills; social; social cognition; social integration; social separation; statistics; theories; web site

DESCRIPTION (provided by applicant): I earned a Ph.D. in Sociology from UCLA in 2006, and an M.S. in statistics from UCLA in 2008. As a student affiliate of the California Center for Population Research, I received a comprehensive education in social demography and social stratification, and rigorous training in research methodology and statistical techniques for analyzing large-scale survey data. During this time, I also developed a technique for linking empirical estimates of individuals' preferences for neighborhoods with agent-based models to explore the segregation dynamics implied by individuals' residential mobility decisions. While this work is at the vanguard of social research, it relies on a primitive and highly unrealistic conception of individuals' residential choice behavior. This is not unique to my research. The statistical models used in quantitative social science and public health research are rarely (if ever) a plausible model of the underlying behavior or decision-making process that gave rise to the social phenomenon under investigation. Over the past year, I have learned that researchers in departments of Marketing at business schools have highly sophisticated statistical models of how people navigate their environment and make decisions, which draw on insights from cognitive science and decision theory. But these methods have never been applied in population health, for example, to how people choose among neighborhoods, schools, jobs, or entrees in a cafeteria. Based on my initial forays into this area, I have found marketing choice models are orders of magnitude more difficult to master than techniques I have taught myself in the past. There is no standard statistical software, and the programs are usually written from scratch; there is no single model or methodological approach, but rather a loose "toolkit" of techniques or strategies that are customized to a specific application. In addition, the models often require Bayesian estimation techniques (which require significant expertise outside of standardized software packages). The K01 Mentored Research Scientist Award would provide protected time for me to: (1) master the statistical skills involved in estimating these models, an gain a formal understanding of the underlying theories of decision-making; (2) adapt the marketing statistical models to new substantive applications-the study of residential mobility and mate choice (as observed on an online dating site); (3) develop a methodological framework for linking these "cognitively plausible" models of individual decision-making with agent-based models to understand the implications of decision strategies for aggregate population dynamics; and (4) explore how this statistical framework may be applied to a broader range of decision-making applications relevant to health research. I have developed a course plan to provide more formal training in the statistical techniques and theoretical frameworks that underpin the statistical models of decision-making used in marketing research. In addition, I intend to get a comprehensive overview of the decision literature in psychology to supplement what has up to now been self-study. I will supplement the coursework in statistical modeling with frequent interactions with my primary mentor, Fred Feinberg. The bulk of my training will take place at the University of Michigan, where Feinberg and Diez Roux are also in residence. I will have access to the data management, computing, and administrative resources of the Population Studies Center, the Department of Sociology, and the Center for the Study of Complex Systems. Both Sociology and Population Studies have provided me with office space for the duration of the grant period. My short-term goals are to master the statistical skills involved in estimating these models and gain a formal understanding of the underlying theories of decision-making; and also to adapt the marketing statistical models to new substantive applications-the study of residential mobility and mate choice (as observed on an online dating site). In the longer term, my goals are to develop a methodological framework for linking these "cognitively plausible" models of individual decision-making with agent-based models to understand the implications of decision strategies for aggregate patterns of social integration or separation; and to explore how this statistical framework may be applied to a broader range of decision-making applications relevant to health research. My proposed research projects apply the choice-modeling framework in two areas of research: mate choice (as observed on an online dating website) and neighborhood choice. These are two specific instances of a general class of choice problems where people choose-with varying degrees of habit or deliberation- from a moderate to large number of potential options. When confronted with this sort of choice problem, decision theorists have repeatedly shown that people tend to invoke "screening rules" to simplify the choice problem. I will use data from the Los Angeles Family and Neighborhood Survey to estimate cognitively plausible choice models to capture these screening rules at multiple stages. My mate choice project makes use of a rich dataset I recently acquired from an online dating. I will first estimate a multi-stage choice model aimed at identifying the rules used at each stage, and explore how strategies for mate search and mate choice differ across demographic areas. I will later extend our models to allow for learning and adaptive responses. In both the neighborhood and mate choice case, the cognitively plausible choice models will be coupled with realistic agent-based models to explore the co-evolution of individual behavior and the social environment.

描述（由申请人提供）：我获得了博士学位。 2006年加州大学洛杉矶分校的社会学学士学位和硕士学位在加州大学洛杉矶分校（UCLA）的统计数据中，作为加利福尼亚人口研究中心的学生分支机构，我接受了社会人口统计学和社会分层的全面教育，以及用于分析大规模调查数据的研究方法和统计技术的严格培训。在此期间，我还开发了一种技术，用于将基于代理模型的社区偏好的经验估计联系起来，以探索个人的住宅流动性决策所隐含的隔离动力学。尽管这项工作是社会研究的先锋，但它依赖于个人住宅选择行为的原始且高度不切实际的概念。这不是我的研究所独有的。定量社会科学和公共卫生研究中使用的统计模型很少（如果有的话）是基本行为或决策过程的合理模型，这引起了正在调查的社会现象。在过去的一年中，我了解到，商学院市场营销部门的研究人员具有高度复杂的统计模型，说明人们如何在环境中驾驭环境和做出决策，这取决于认知科学和决策理论的见解。但是，这些方法从未应用于人口健康，例如，人们如何在自助餐厅中的社区，学校，工作或主菜中选择。 根据我进入这一领域的最初涉足，我发现营销选择模型比我过去自学的技术要掌握的数量级要难得多。没有标准的统计软件，这些程序通常是从头开始写的。没有单一的模型或方法论方法，而是针对特定应用程序自定义的技术或策略的“工具包”。此外，模型通常需要贝叶斯估计技术（在标准化软件包之外需要大量的专业知识）。 K01指导的研究科学家奖将为我提供受保护的时间：（1）掌握估计这些模型所涉及的统计技能，从而正式了解决策的基本理论； （2）将营销统计模型调整到新的实质性应用程序中 - 对住宅流动性和伴侣选择的研究（如在在线约会网站上观察到的）； （3）开发一个方法论框架，以将这些个人决策的“认知合理”模型与基于代理的模型联系起来，以了解决策策略对总体人口动态的影响； （4）探讨如何将该统计框架应用于与健康研究有关的更广泛的决策应用程序。我已经制定了一项课程计划，以提供更多的统计技术和理论框架培训，这些培训是营销研究中使用的决策统计模型的基础。此外，我打算对心理学的决策文献进行全面的概述，以补充现在已经自学的事情。我将通过与我的主要导师弗雷德·费恩伯格（Fred Feinberg）进行频繁相互作用来补充统计建模的课程。我的大部分培训将在密歇根大学进行，费恩伯格和戴兹·鲁克斯也在那里。我将可以访问人口研究中心，社会学系和复杂系统研究中心的数据管理，计算和行政资源。在赠款期间，社会学和人口研究都为我提供了办公空间。 我的短期目标是掌握涉及的统计技能 估计这些模型并对决策的基本理论进行正式理解；并将营销统计模型适应新的实质应用程序 - 对住宅移动性和伴侣选择的研究（如在在线约会网站上观察到的）。从长远来看，我的目标是开发一个方法学框架，以将这些个人决策的“认知合理”模型与基于代理的模型联系起来，以了解决策策略对社会整合或分离的总体模式的含义；和 探索如何将该统计框架应用于与健康研究有关的更广泛的决策应用程序。我提出的研究项目在两个研究领域中应用了选择模型框架：伴侣选择（如在在线约会网站上观察到）和邻里选择。这是一般选择问题的两个特定实例，人们从中等到大量的潜在选择中选择不同程度的习惯或审议的人。当面对这种选择问题时，决策理论家反复表明人们倾向于调用“筛选规则”以简化选择问题。我将使用洛杉矶家族和社区调查的数据来估算认知上合理的选择模型，以在多个阶段捕获这些筛选规则。我的伴侣选择项目利用了我最近从在线约会中获得的丰富数据集。我将首先估算一个多阶段选择模型，旨在确定所使用的规则 在每个阶段，并探索伴侣搜索和伴侣选择的策略在人口统计领域如何不同。稍后，我将扩展我们的模型，以允许学习和自适应响应。在邻里和伴侣选择案例中，认知合理的选择模型将与基于现实的代理模型相结合，以探索个人行为和社会环境的共同发展。