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.

个人描述（由申请人提供）：2006年获得UCLA社会学博士学位，2008年获得UCLA统计学硕士学位。作为加州人口研究中心的学生附属机构，我接受了全面的社会人口学和社会分层的教育，并在研究方法和分析大规模调查数据的统计技术方面接受了严格的训练。在此期间，我还开发了一种技术，将个人对社区偏好的经验估计与基于代理的模型联系起来，以探索个人居住流动性决策所隐含的隔离动态。虽然这项工作是社会研究的先锋，但它依赖于对个人居住选择行为的原始和高度不切实际的概念。这并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）是导致所调查的社会现象的潜在行为或决策过程的合理模型。在过去的一年里，我了解到，商学院市场营销系的研究人员拥有非常复杂的统计模型，可以解释人们如何驾驭环境并做出决策，这些模型借鉴了认知科学和决策理论的见解。但这些方法从未应用于人口健康，例如，人们如何选择社区、学校、工作或自助餐厅的主菜。根据我对这一领域的初步尝试，我发现营销选择模型比我过去自学的技术要难掌握得多。没有标准的统计软件，程序通常是从零开始编写的；没有单一的模型或方法方法，而是针对特定应用程序定制的松散的技术或策略“工具包”。此外，模型通常需要贝叶斯估计技术（这需要标准化软件包之外的重要专业知识）。K01指导研究科学家奖将为我提供保护时间：(1)掌握估算这些模型所涉及的统计技能，并对决策的基本理论有正式的理解；(2)将营销统计模型应用于新的实质性应用——居住流动性和伴侣选择的研究（以在线约会网站为例）；(3)建立一个方法学框架，将这些“认知上合理”的个体决策模型与基于主体的模型联系起来，以理解决策策略对总体种群动态的影响；(4)探索如何将这一统计框架应用于与健康研究相关的更广泛的决策应用。我制定了一个课程计划，提供更正式的统计技术和理论框架方面的培训，这些技术和理论框架是市场研究中决策的统计模型的基础。此外，我打算对心理学中的决策文献进行全面的概述，以补充到目前为止的自学成果。我将通过与我的主要导师Fred Feinberg的频繁互动来补充统计建模课程。我的大部分培训将在密歇根大学进行，范伯格和迪兹·鲁克斯也住在那里。我将可以使用人口研究中心、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。在资助期间，社会学和人口研究两个专业都为我提供了办公场所。我的短期目标是掌握统计学所涉及的技能