EAGER: A Mathematical Model of Privacy Decisions: A Behavioral Economic Perspective

EAGER：隐私决策的数学模型：行为经济学视角

• 批准号: 1544090
• 负责人: Fariborz Farahmand
• 金额: $ 27.58万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544090&HistoricalAwards=false
• 关键词: EAGER; Mathematical; Model; Privacy; Decisions

When making decisions about information privacy, people do not always act rationally according to their best interests. It is thus important to understand why people express concerns about privacy, but often act contrary to their stated intentions. This research investigates individuals' privacy decisions, from a behavioral economic perspective, by: 1) investigating how two systems of thinking in human minds, affective (system 1) and cognitive (system 2), operate during privacy decisions; 2) developing affective-cognitive algorithms to mathematically describe the operation of systems 1 and 2 in privacy decisions; and 3) testing and evaluating the accuracy of these algorithms. This is the first step in forming a new mathematical theory of privacy that can describe how people actually make privacy decisions versus how they are expected to make such decisions. Using techniques from discrete mathematics, theoretical computer science and behavioral economics, and adapting the concept of bounded rationality, the PIs will develop affective-cognitive algorithms to model human experienced-utility in privacy decisions (i.e., risks and benefits they perceive in disclosing privacy). This is fundamentally different from the existing mathematical models of privacy decisions that assume humans have stable preferences and always choose the option with the highest expected utility (i.e., the option with the maximum privacy). The PIs use techniques from behavioral game theory to rigorously test and evaluate the accuracy of their algorithms. The PIs apply findings of behavioral economics, mathematical psychology, and previous research on information privacy to translate operation of systems 1 and 2 into mathematical models. This research is intended to bridge three streams of research, namely information privacy, theoretical computer science, and behavioral and experimental economics. The impacts of this research have translational potential for application to real online environments and implementation in decision support tools, such as recommender systems.

人们在进行信息隐私决策时，并不总是根据自己的最大利益理性行事。因此，理解为什么人们表达对隐私的担忧，但往往违背他们的意图是很重要的。本研究从行为经济学的角度探讨了个人隐私决策，主要包括：1）探讨了人类思维中的两个思维系统，情感思维系统，（1）认知2）开发情感认知算法以数学地描述系统1和2在隐私决策中的操作; 3）对算法的精度进行测试和评价。这是形成新的隐私数学理论的第一步，该理论可以描述人们实际上如何做出隐私决策，而不是期望他们如何做出这样的决策。利用离散数学、理论计算机科学和行为经济学的技术，并适应有限理性的概念，PI将开发情感认知算法来模拟隐私决策中的人类经验效用（即，他们认为披露隐私的风险和好处）。这与现有的隐私决策数学模型有根本的不同，这些模型假设人类具有稳定的偏好，并且总是选择具有最高预期效用的选项（即，具有最大隐私的选项）。PI使用行为博弈论的技术来严格测试和评估其算法的准确性。PI应用行为经济学，数学心理学和以前对信息隐私的研究成果，将系统1和2的操作转化为数学模型。 这项研究旨在弥合三个研究流，即信息隐私，理论计算机科学，行为和实验经济学。这项研究的影响具有应用于真实的在线环境和决策支持工具（例如推荐系统）实施的转化潜力。