Collaborative Research: Improving Worker Safety by Understanding Risk Compensation as a Latent Precursor of At-risk Decisions

合作研究：通过了解风险补偿作为风险决策的潜在前兆来提高工人安全

• 批准号: 2326937
• 负责人: Behzad Esmaeili
• 金额: $ 4.28万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326937&HistoricalAwards=false
• 关键词: Collaborative; Research; Improving; Worker; Safety; Collaborative; Research; Improving; Worker; Safety

Workers may fall prey to certain cognitive biases as shortcuts that result in judgment errors and risky decisions, such as risk compensation. The risk-compensation bias argues that individuals adjust their at-risk behaviors to achieve a balance between potential risks and benefits and thereby maintain a target level of risk. Derived from external (e.g., task or environmental-related) and internal (e.g., individual characteristics) sources, risk compensation ultimately influences an individual’s (deliberative, affective, and experiential) risk perception as a central predictor of health and safety-related behaviors and certain risky decisions. Decision making under risk is mainly studied at the individual level in the construction-safety setting. However, drawing on social influence and behavioral intention theories, coworkers’ risk-taking serves as an “extra motive” of risk-taking behavior among workers in the workplace. Thus, studying the risk-compensation effect in the construction environment can become more complicated given that construction workers work in groups, and coworker behavior can influence safety-related behavior. Furthermore, the effects of heat exposure and subsequent heat stress might translate into an increased risk of injury caused by physical discomfort, fatigue, and reduced vigilance that can influence worker emotional state and risk perception, and lead to cognitive failure, misperceiving hazards, and neglecting precautionary behavior. Accordingly, this multidisciplinary project addresses these gaps by integrating psychological science, artificial intelligence (AI), and advances in construction safety to deliver a novel theoretical platform and empirical process to understand the latent changes in worker decision dynamics following an intervention for greater protection from injury.The specific objectives of this study are to (1) examine the extent to which individuals’ characteristics and psychological states, along with task and environmental factors (e.g., time pressure, extreme heat) influence workers’ at-risk decisions; (2) determine the role of risk compensation bias on team risk perception, decision making, and work behavior; and (3) develop a multidimensional AI model to identify at-risk workers and interpret their risky decision-making, using limited attributes including individual, task, and environmental-related factors. To achieve these objectives, a multi-sensor immersive 360 mixed-reality environment that consists of passive haptics and environmental modalities is used to raise the workers’ sense of presence, capture their realistic responses to safety features during various current and future construction tasks. A combination of qualitative and quantitative measures serve to investigate the underlying mechanisms of workers’ risk-compensatory behaviors and decisions. The measures derive from location-tracking sensors, vision-based sensors, wireless neuropsychological and cognitive brain monitoring (fNIRS), eye-tracker, photoplethysmography (PPG) and galvanic skin response (GSR) psychophysiological sensors, semi-structured interviews, demographic, and psychographic surveys. The collected data constitutes information about workers’ behavioral changes simulated using agent-based modeling, and used to develop a multidimensional predictive model to minimize the likelihood of risk compensation and to prevent incidents and injuries. The project outcomes have the potential to impact the performance of a nationwide industry and create a novel platform for enhancing the national research and education infrastructure. They advance protection mechanisms for thousands of American workers and save estimated billions of dollars in financial costs per year in the United States.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.

工人可能会成为某些认知偏见的猎物，因为捷径会导致判断错误和风险决定，例如风险补偿。风险补偿偏见认为，个人可以调整其处于危险的行为，以在潜在风险和利益之间达到平衡，从而保持目标风险水平。风险补偿最终源自外部（例如任务或环境相关）和内部（例如个人特征）来源，最终影响个人（审议，情感和经验丰富的）风险感知，这是对健康和安全相关行为的核心预测指标和与安全相关行为的核心预测指标和某些风险决定。在施工安全环境中，面临风险的决策主要是在个人层面上研究的。但是，借助社会影响力和行为意图，同事的冒险企业是工作场所工人冒险行为的“额外动机”。考虑到建筑工人在群体中工作，并且同事行为会影响与安全相关的行为，从而研究建筑环境中的风险补偿效应可能会变得更加复杂。此外，热地暴露和随后的热应激的影响可能转化为由于身体不适，疲劳和警惕性降低而导致的伤害风险，从而影响工人的情绪状态和风险感知，并导致认知失败，误解危害，并忽略了预防行为。 According to this, this multidisciplinary project address these gaps by integrating psychological science, artificial intelligence (AI), and advances in construction safety to deliver a novel theoretical platform and empirical process to understand the latent changes in worker decision dynamics following an intervention for greater protection from injury.The specific objectives of this study are to (1) examining the extent to which individuals’ characteristics and psychological states, along with task and environmental factors (e.g., time pressure, extreme heat) influence工人的处于危险的决定； （2）确定风险补偿偏见对团队风险感知，决策和工作行为的作用； （3）使用有限的属性，包括个人，任务和与环境相关的因素，开发多维AI模型，以识别高危工人并解释其风险决策。为了实现这些目标，由被动触觉和环境方式组成的多传感器沉浸式360混合真实环境用于提高工人的存在感，在各种当前和未来的施工任务中捕获其对安全特征的现实响应。定性和定量措施的结合旨在研究工人风险弥补行为和决策的潜在机制。这些措施源自位置跟踪传感器，基于视觉的传感器，无线神经心理学和认知大脑监测（FNIRS），眼球跟踪器，光绘画学（PPG）和电力性皮肤反应（GSR）心理生理学传感器，半结构的访谈访谈，人口统计学，人口统计学和心理镜面。收集到的数据构成了有关使用基于代理的建模模拟的工人行为变化的信息，并用于开发多维预测模型，以最大程度地减少风险补偿的可能性并防止事件和伤害。该项目成果有可能影响国家行业的绩效，并创建一个新的平台来增强国家研究和教育基础设施。他们推动了数千名美国工人的保护机制，并在美国节省了估计数十亿美元的财务成本。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是值得的支持。