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

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.

员工可能会陷入某些认知偏差的陷阱，从而导致判断错误和风险决策，例如风险补偿偏差个人认为，调整他们的风险行为以实现潜在风险和收益之间的平衡，从而维持。风险补偿源自外部（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（深思熟虑的、情感的和经验的）风险感知。健康和安全相关行为以及某些风险决策的核心预测因素主要在建筑安全环境中的个人层面进行研究，但是，利用社会影响和行为意图理论，同事的风险承担可以作为风险决策的核心预测因素。因此，考虑到建筑工人是集体工作，并且同事的行为会影响安全相关行为，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，热暴露和随后的影响热应激可能会导致身体不适、疲劳和警惕性降低导致受伤风险增加，从而影响工人的情绪状态和风险感知，并通过整合心理科学导致认知失败、错误感知危险和忽视预防行为。 、人工智能 (AI) 以及建筑安全方面的进步，提供了一个新颖的理论平台和实证过程，以了解在采取干预措施以更好地免受伤害后工人决策动态的潜在变化。本研究的具体目标是 (1)检查个人特征和心理状态以及任务和环境因素（例如时间压力、酷热）对员工风险决策的影响程度；（2）确定风险补偿偏差对团队风险感知的作用，决策和工作行为；(3) 开发多维人工智能模型，利用包括个人、任务和环境相关因素在内的有限属性来识别高风险员工并解释他们的风险决策。多传感器沉浸式 360由被动触觉和环境模式组成的混合现实环境用于提高工人的存在感，捕捉他们在当前和未来的各种施工任务中对安全特征的真实反应，定性和定量措施的结合有助于调查潜在的问题。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）、眼动追踪器、光电体积描记法（PPG）和皮肤电反应。 (GSR) 心理生理传感器、半结构化访谈、人口统计和心理调查收集的数据构成了使用基于代理的建模模拟的有关工人行为变化的信息，并用于开发多维预测模型，以最大限度地减少风险补偿和风险补偿的可能性。该项目的成果有可能影响全国性行业的表现，并为加强国家研究和教育基础设施创建一个新颖的平台，为数千名美国工人推进保护机制，并节省约数十亿美元的资金。美国每年的财务费用该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。