Understanding Cumulative Risk Perception from Judgments and Choices: An Application to Flood Risks

A Description-Experience Framework of the Psychology of Risk

The modern world holds countless risks for humanity, both large-scale and intimately personal-from cyberwarfare, pandemics, and climate change to sexually transmitted diseases and drug use and abuse. Many risks have prompted institutional, regulatory, and technological countermeasures, the success of which depends to some extent on how individuals learn about the risks in question. We distinguish between two powerful but imperfect teachers of risk. First, people may learn by consulting symbolic and descriptive material, such as warnings, statistics, and images. More often than not, however, a risk's fluidity defies precise description. Second, people may learn about risks through personal experience. Responses to risk can differ systematically depending on whether people learn through one mode, both, or neither. One reason for these differences-and by no means the only reason-is the discrepancy in the cognitive impact that rare events (typically the risk event) and common events (typically the nonoccurrence of the risk event) have on the decision maker. We propose a description-experience framework that highlights not only the impact of each mode of learning but also the effects of their interplay on individuals' and collectives' responses to risk. We outline numerous research questions and themes suggested by this framework.

Perception Bias Effects on Healthcare Management in COVID-19 Pandemic: An Application of Cumulative Prospect Theory

Coronavirus disease 2019 (COVID-19) has posed severe threats to human safety in the healthcare sector, particularly in residents in long-term care facilities (LTCFs) at a higher risk of morbidity and mortality. This study aims to draw on cumulative prospect theory (CPT) to develop a decision model to explore LTCF administrators’ risk perceptions and management decisions toward this pandemic. This study employed the policy Delphi method and survey data to examine managers’ perceptions and attitudes and explore the effects of sociodemographic characteristics on healthcare decisions. The findings show that participants exhibited risk aversion for small losses but became risk-neutral when considering devastating damages. LTCF managers exhibited perception bias that led to over- and under-estimation of the occurrence of infection risk. The contextual determinants, including LTCF type, scale, and strategy, simultaneously affect leaders’ risk perception toward consequences and probabilities. Specifically, cost-leadership facilities behave in a loss-averse way, whereas hybrid-strategy LTCFs appear biased in measuring probabilities. This study is the first research that proposes a CPT model to predict administrators’ risk perception under varying mixed gain–loss circumstances involving considerations of healthcare and society in the pandemic context. This study extends the application of CPT into organizational-level decisions. The results highlight that managers counteract their perception bias and subjective estimation to avoid inappropriate decisions in healthcare operations and risk governance for a future health emergency.

Effects of Probabilistic Risk Situation Awareness Tool (RSAT) on Aeronautical Weather-Hazard Decision Making

We argue that providing cumulative risk as an estimate of the uncertainty in dynamically changing risky environments can help decision-makers meet mission-critical goals. Specifically, we constructed a simplified aviation-like weather decision-making task incorporating Next-Generation Radar (NEXRAD) images of convective weather. NEXRAD radar images provide information about geographically referenced precipitation. NEXRAD radar images are used by both pilots and laypeople to support decision-making about the level of risk posed by future weather-hazard movements. Using NEXRAD, people and professionals have to infer the uncertainty in the meteorological information to understand current hazards and extrapolate future conditions. Recent advancements in meteorology modeling afford the possibility of providing uncertainty information concerning hazardous weather for the current flight. Although there are systematic biases that plague people’s use of uncertainty information, there is evidence that presenting forecast uncertainty can improve weather-related decision-making. The current study augments NEXRAD by providing flight-path risk, referred to as the Risk Situational Awareness Tool (RSAT). RSAT provides the probability that a route will come within 20 NMI radius (FAA recommended safety distance) of hazardous weather within the next 45 min of flight. The study evaluates four NEXRAD displays integrated with RSAT, providing varying levels of support. The “no” support condition has no RSAT (the NEXRAD only condition). The “baseline” support condition employs an RSAT whose accuracy is consistent with current capability in meteorological modeling. The “moderate” support condition applies an RSAT whose accuracy is likely at the top of what is achievable in meteorology in the near future. The “high” support condition provides a level of support that is likely unachievable in an aviation weather decision-making context without considerable technological innovation. The results indicate that the operators relied on the RSAT and improved their performance as a consequence. We discuss the implications of the findings for the safe introduction of probabilistic tools in future general aviation cockpits and other dynamic decision-making contexts. Moreover, we discuss how the results contribute to research in the fields of dynamic risk and uncertainty, risk situation awareness, cumulative risk, and risk communication.