Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Institute for Politics and Strategy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Risk Anal. 2023 Jul;43(7):1356-1369. doi: 10.1111/risa.14017. Epub 2022 Sep 17.
Upon shutting down operations in early 2020 due to the COVID-19 pandemic, the movie industry assembled teams of experts to help develop guidelines for returning to operation. It resulted in a joint report, The Safe Way Forward, which was created in consultation with union members and provided the basis for negotiations with the studios. A centerpiece of the report was a set of heatmaps displaying SARS-CoV-2 risks for a shoot, as a function of testing rate, community infection prevalence, community transmission rate (R0), and risk measure (either expected number of cases or probability of at least one case). We develop and demonstrate a methodology for evaluating such complex displays, in terms of how well they inform potential users, in this case, workers deciding whether the risks of a shoot are acceptable. We ask whether individuals making hypothetical return-to-work decisions can (a) read display entries, (b) compare display entries, and (c) make inferences based on display entries. Generally speaking, respondents recruited through the Amazon MTurk platform could interpret the display information accurately and make coherent decisions, suggesting that heatmaps can communicate complex risks to lay audiences. Although these heatmaps were created for practical, rather than theoretical, purposes, these results provide partial support for theoretical accounts of visual information processing and identify challenges in applying them to complex settings.
由于 COVID-19 大流行,该电影行业于 2020 年初关闭运营,之后组建了专家组来帮助制定恢复运营的指南。这促成了一份联合报告《安全前进之路》(The Safe Way Forward),该报告在与工会成员协商后制定,并为与制片厂的谈判提供了依据。报告的核心是一组热图,显示了拍摄时 SARS-CoV-2 风险,其功能是测试率、社区感染流行率、社区传播率(R0)和风险指标(预期病例数或至少一个病例的概率)。我们开发并展示了一种评估此类复杂显示的方法,根据潜在用户(在这种情况下是决定拍摄风险是否可接受的工作人员)的信息程度进行评估。我们询问做出假设性返回工作决策的个人是否可以:(a) 读取显示条目,(b) 比较显示条目,以及 (c) 根据显示条目进行推断。一般来说,通过亚马逊 MTurk 平台招募的受访者可以准确地解释显示信息并做出一致的决策,这表明热图可以向普通观众传达复杂的风险。尽管这些热图是为实际目的而不是理论目的而创建的,但这些结果为视觉信息处理的理论解释提供了部分支持,并确定了将其应用于复杂环境中的挑战。