RAPID: Analyzing forced habit change from COVID-19 using large-scale data

RAPID：使用大规模数据分析 COVID-19 导致的被迫习惯改变

• 批准号: 2031287
• 负责人: Colin Camerer
• 金额: $ 17.43万
• 依托单位: California Institute of Technology
• 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: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031287&HistoricalAwards=false
• 关键词: RAPID; Analyzing; forced; habit; change

During the COVID-19 pandemic, people were forced to try new routine at school, work, and home — “forced exploration”. That is, habits used to guide how we work, conduct our daily routine, exercise, eat, go to school, and interact with friends and neighbors, Habits are formed to make the same routines effortless, to save time and mental effort, when routine choices work well. However, while the mental savings from habits are a benefit, when people are choosing habitually, they may not be exploring other options which could be even better— that is the hidden cost of habit. Forced exploration can actually be beneficial if it shows people better ways of school, work, and social interaction. This kind of exploration is like going to your favorite restaurant and finding out they have run out of your favorite dish — now you have to try something new, which you would not have explored without the disruption. This wave of forced exploration raises important questions: What new habits are formed that will persist— what will be the “new normal”? Consider, for example, wearing a face-mask outside of the house. This is exactly the kind of “muscle memory” behavior that usually habitizes— it can be triggered while stepping out of your car, or entering a store, and quickly becomes automatic and effortless. Whether a lot of other people are wearing masks or not can also be a trigger that prompts habit (in either direction).The same question arises across the board: Will people go back to movie theaters (or stay home with streaming)? Will restaurants fully reopen or will home delivery take over? Will knowledge firms switch to more remote “tele-work”? Will schools find better mixtures of home learning and in-school activity? This project will analyze two different kinds of big data to test whether or not this kind of forced exploration really does result in new habits.In social sciences, habits are usually modelled mathematically using a simple equation in which the more an activity has been done in the past, the more it is done in the future. This is called a "reduced form" approach because it reduces a biologically complicated mechanism to something much simpler. It is a good starting point but cannot answer questions such as "What if past behavior is disrupted?” This research project uses a new approach to habits based on animal learning and human cognitive neuroscience. The starting point is that habits have developed to save effort ⎯— both physical and mental. The “neural autopilot” framework proposed here predicts that individuals develop habits for actions which, after repeated decisions, have proven to be reliably rewarding. Such habitual behavior drains fewer physical and mental resources. At the same time, when people are habitized⎯- about exercise, eating, or work — they ignore new goods and activities they would prefer if they actually tried them. While the neural autopilot approach has been tested in many lab studies of animal and human habituation, it has never been systematically explored using a large amount of data about how people actually behave in everyday life. An ideal test of this model is in a field setting where choice sets are artificially truncated, so people resort to new choices; and that is exactly what happened during the ongoing lockdowns. This project will use data from Weibo chat data and Fitbit fitness and sleep tracking. These large sets of data contain fine-grained measurements of behavior. Using this data, we will develop and test a statistical neural autopilot model, to recover values for the model’s main parameters. The parameters are numbers that measure, for each person, how fast habits are formed and the threshold to break out of a habit and explore something that might be better. The estimated parameter values will be used to make predictions about which habits acquired during the pandemic will persist, and which behavior will revert to pre-pandemic patterns.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.

在新冠肺炎大流行期间，人们被迫在学校、工作和家里尝试新的日常生活--“被迫探索”。也就是说，习惯被用来指导我们如何工作，如何进行我们的日常工作，锻炼，饮食，上学，以及与朋友和邻居互动，习惯的形成是为了让同样的例行公事变得毫不费力，节省时间和脑力，当例行公事选择奏效时。然而，虽然从习惯中节省精神是一种好处，但当人们习惯性地做出选择时，他们可能不会探索其他可能更好的选择--这就是习惯的隐性成本。强迫探索实际上是有益的，如果它向人们展示了更好的学习、工作和社交方式。这种探索就像去你最喜欢的餐厅，发现他们已经用完了你最喜欢的菜--现在你必须尝试一些新的东西，如果没有中断，你就不会去尝试。这股被迫探索的浪潮提出了重要的问题：形成了哪些将持续存在的新习惯--什么将是“新常态”？比如，你可以在屋外戴上口罩。这正是通常会出现的那种“肌肉记忆”行为--当你走出车外或走进商店时，它会被触发，很快就会变得自动和毫不费力。其他许多人是否戴着口罩也可能引发习惯(无论是哪种情况)。同样的问题也普遍存在：人们会回到电影院(还是呆在家里看流媒体)？餐厅将全面重新开业，还是上门送货上门？知识型企业会转向更远程的“远程办公”吗？学校会找到更好的家庭学习和校内活动的结合吗？这个项目将分析两种不同类型的大数据，以测试这种强迫探索是否真的会导致新的习惯。在社会科学中，习惯通常是用一个简单的等式进行数学建模的，在这个等式中，过去做的活动越多，将来做的就越多。这种方法被称为“简化形式”，因为它将生物学上复杂的机制简化为简单得多的机制。这是一个很好的起点，但不能回答“如果过去的行为被打乱了怎么办？”这项研究项目使用了一种基于动物学习和人类认知神经科学的新方法来研究习惯。⎯的出发点是养成了省力的习惯--无论是身体上的还是精神上的。这里提出的“神经自动驾驶”框架预测，个人会养成习惯，在反复做出决定后，这些行为被证明是可靠的回报。这种习惯性行为耗费的体力和脑力资源较少。与此同时，当人们被⎯迷住时--关于锻炼、饮食或工作--他们会忽视新的商品和活动，如果他们真的尝试了它们，他们会更喜欢它们。虽然神经自动驾驶方法已经在许多关于动物和人类习惯化的实验室研究中进行了测试，但它从未被系统地探索过，使用的是关于人们在日常生活中实际行为的大量数据。对该模型的理想测试是在一个选择集被人为截断的现场环境中，因此人们求助于新的选择；而这正是正在进行的封锁期间发生的情况。该项目将使用来自微博聊天数据以及Fitbit健身和睡眠跟踪的数据。这些庞大的数据集包含对行为的细粒度测量。利用这些数据，我们将开发和测试一个统计神经自动驾驶模型，以恢复模型的主要参数的值。这些参数是衡量每个人养成习惯的速度的数字，以及摆脱习惯并探索可能更好的东西的门槛。估计参数值将用于预测在大流行期间养成的哪些习惯将持续下去，以及哪些行为将恢复到大流行前的模式。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。