Determinants of Probability Neglect and Risk Attitudes for Disaster Risk: An Online Experimental Study of Flood Insurance Demand among Homeowners

Drivers of natural disaster risk-reduction actions and their temporal dynamics: Insights from surveys during an imminent hurricane threat and its aftermath

To improve preparedness for natural disasters, it is imperative to understand the factors that enable individual risk-reduction actions. This study offers such insights using innovative real-time (N = 871) and repeated (N = 255) surveys of a sample of coastal residents in Florida regarding flood preparations and their drivers during an imminent threat posed by Hurricane Dorian and its aftermath. Compared with commonly employed cross-sectional surveys, our methodology better represents relationships between preparedness actions undertaken during the disaster threat and their drivers derived from an extended version of Protection Motivation Theory (PMT). The repeated survey allows for examining temporal dynamics in these drivers. Our results confirm the importance of coping appraisals and show that risk perceptions relate more strongly to emergency protection decisions made during the period of the disaster threat than to decisions made well before. Moreover, we find that several personal characteristics that we add to the standard PMT framework significantly relate to undertaking preparedness actions, especially locus of control and social norms. Significant changes in key explanatory variables occur following the disaster threat, including a decline in risk perception, a potential learning effect in coping appraisals, and a decline in risk aversion. Our results confirm the advantage of the real-time and repeated survey approach in understanding both short- and long-term disaster preparedness actions.

Decision threshold models in medical decision making: a scoping literature review

BACKGROUND

Decision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured through the decision thresholds. This systematic scoping review sought to characterize the literature on non-expected utility decision thresholds in medical decision-making by identifying commonly used theoretical paradigms and contextual and subjective factors that inform decision thresholds.

METHODS

A structured search designed around three concepts-individual decision-maker, decision threshold, and medical decision-was conducted in MEDLINE (Ovid) and Scopus databases from inception to July 2023. ProQuest (Dissertations and Theses) database was searched to August 2023. The protocol, developed a priori, was registered on Open Science Framework and PRISMA-ScR guidelines were followed for reporting on this study. Titles and abstracts of 1,618 articles and the full texts for the 228 included articles were reviewed by two independent reviewers. 95 articles were included in the analysis. A single reviewer used a pilot-tested data collection tool to extract study and author characteristics, article type, objectives, theoretical paradigm, contextual or subjective factors, decision-maker, and type of medical decision.

RESULTS

Of the 95 included articles, 68 identified a theoretical paradigm in their approach to decision thresholds. The most common paradigms included regret theory, hybrid theory, and dual processing theory. Contextual and subjective factors that influence decision thresholds were identified in 44 articles.

CONCLUSIONS

Our scoping review is the first to systematically characterizes the available literature on decision thresholds within medical decision-making. This study offers an important characterization of the literature through the identification of the theoretical paradigms for non-expected utility decision thresholds. Moreover, this study provides insight into the various contextual and subjective factors that have been documented within the literature to influence decision thresholds, as well as these factors juxtapose theoretical paradigms.

The effect of likelihood and impact information on public response to severe weather warnings

Meteorological services are increasingly moving away from issuing weather warnings based on the exceedance of meteorological thresholds (e.g., windspeed), toward risk-based (or "impact-based") approaches. The UK Met Office's National Severe Weather Warning Service has been a pioneer of this approach, issuing yellow, amber, and red warnings based on an integrated evaluation of information about the likelihood of occurrence and potential impact severity. However, although this approach is inherently probabilistic, probabilistic information does not currently accompany public weather warning communications. In this study, we explored whether providing information about the likelihood and impact severity of forecast weather affected subjective judgments of likelihood, severity, concern, trust in forecast, and intention to take protective action. In a mixed-factorial online experiment, 550 UK residents from 2 regions with different weather profiles were randomly assigned to 1 of 3 Warning Format conditions (Color-only, Text, Risk Matrix) and presented with 3 warnings: high-probability/moderate-impact (amber HPMI); low-probability/high-impact (amber); high-probability/high-impact (red). Amongst those presented with information about probability and impact severity, red high-likelihood/high-impact warnings elicited the strongest ratings on all dependent variables, followed by amber HPMI warnings. Amber low-likelihood/high-impact warnings elicited the lowest perceived likelihood, severity, concern, trust, and intention to take protective responses. Taken together, this indicates that UK residents are sensitive to probabilistic information for amber warnings, and that communicating that severe events are unlikely to occur reduces perceived risk, trust in the warning, and behavioral intention, even though potential impacts could be severe. We discuss the practical implications of this for weather warning communication.

The role of perceived risk on dishonest decision making during a pandemic

The COVID-19 pandemic presented serious risks to the health and financial wellbeing of millions of people across the world. While many individuals adapted to these challenges through a variety of prosocial and protective behaviors (e.g., social distancing, working from home), many others also engaged in dishonest behaviors (e.g., lying to obtain vaccines or furlough payments). Hence, the COVID-19 pandemic provided a unique context in which to obtain a better understanding of the relationship between risk and dishonesty. Across three preregistered studies, we assessed whether objective risk and perceived risk influenced the decision to behave dishonestly in order to gain access to vaccines and furlough payments during a pandemic. We also assessed the extent to which such dishonesty was deterred by the probability of the dishonesty being detected. We found that heightened health risk perceptions were positively related with lying to obtain a vaccine (Studies 1 and 2), but found no evidence of the same relationship between financial risk perceptions and lying to access furlough payments (Study 2). We also found that the probability of dishonesty being detected had a negative relationship with dishonest behavior (Study 3). In addition, across the three studies, we found that (i) dishonesty was consistently evident in approximately one-third of all of our samples, and (ii) greater dishonesty was associated with older age. We discuss how our findings could be utilized by policy makers to better deter and detect dishonest behaviors during future similar crises.

Behavioral biases and heuristics in perceptions of COVID-19 risks and prevention decisions

This study adds to an emerging literature on the factors associated with individual perceptions of COVID-19 risks and decision-making processes related to prevention behaviors. We conducted a survey in the Netherlands (N = 3600) in June-July 2020 when the first peak of COVID-19 infections, hospitalizations, and deaths had passed, and lockdown measures had been eased. Dutch policies relied heavily on individual prevention behaviors to mitigate a second infection wave. We examine whether biases and heuristics that have been observed in how people perceive and respond to other risks also apply to the newly emergent risks posed by COVID-19. The results indicate that people simplify risk using threshold models and that risk perceptions are related with personal experiences with COVID-19 and experiences of close others, supporting the availability heuristic. We also observe that prevention behavior is more strongly associated with COVID-19 risk perceptions and feelings toward the risk than with local indicators of COVID-19 risks, and that prevention behavior is related with herding. Support for government lockdown measures is consistent with preferences that may contribute to the not-in-my-term-of-office bias. In addition, we offer insights into the role of trust, worry, and demographic characteristics in shaping perceptions of COVID-19 risks and how these factors relate with individual prevention behaviors and support for government prevention measures. We provide several lessons for the design of policies that limit COVID-19 risks, including risk communication strategies and appeals to social norms. Perhaps more importantly, our analysis allows for learning lessons to mitigate the risks of future pandemics.

Build Back Better and Long-Term Housing Recovery: Assessing Community Housing Resilience and the Role of Insurance Post Disaster

The purpose of this research is to better understand community housing resilience and the role of insurance using a Build Back Better Long-term Recovery Housing framework to analyze approaches and effects on long-term housing rebuilding and recovery. A comparative case study approach is taken to assess insurance policies and outcomes following Hurricane Katrina in New Orleans and the Canterbury earthquake sequence in Christchurch, New Zealand, both affluent urban communities with strong insurance markets. Framed within the context of “Build Back Better”, the community housing and insurance resilience assessment is based on five key indicators; governance, community resources, risk reduction, housing rebuilding funding (funding and speed of funding), and time compression (built environment and periods of recovery time). Public and private insurance schemes for both case studies are identified and are considered together with analysis of insurance claims and other sources of financial support. The findings and results show that recovery is the result of highly interdependent Build Back Better processes. The data suggests that insurance and governance systems greatly influences the onset and overall speed of recovery (time compression), thereby performing a major role in long-term recovery. This research provides an original contribution to disaster recovery knowledge by analyzing insurance claims from two well-documented natural disasters. Additionally, the paper proposes for the singular definition of community housing resilience.

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.

Individual hurricane evacuation intentions during the COVID-19 pandemic: insights for risk communication and emergency management policies

The U.S. 2020 hurricane season was extraordinary because of a record number of named storms coinciding with the COVID-19 pandemic. This study draws lessons on how individual hurricane preparedness is influenced by the additional risk stemming from a pandemic, which turns out to be a combination of perceptions of flood and pandemic risks that have opposite effects on preparedness behavior. We conducted a survey in early June 2020 of 600 respondents in flood-prone areas in Florida to obtain insights into households' risk perceptions and preparedness for the upcoming hurricane season under COVID-19. The results show that concerns over COVID-19 dominated flood risk perceptions and negatively impacted people's evacuation intentions. Whereas hotel costs were the main obstacle to evacuating during Hurricane Dorian in 2019 in the same geographic study area, the main evacuation obstacle identified in the 2020 hurricane season is COVID-19. Our statistical analyses investigating the factors influencing evacuation intentions show that older individuals are less likely to evacuate under a voluntary order, because they are more concerned about the consequences of becoming infected by COVID-19. We observe similar findings based on a real-time survey we conducted in Florida with another group of respondents under the threat of Hurricane Eta at the end of the hurricane season in November 2020. We discuss the implications of our findings for risk communication and emergency management policies that aim to improve hurricane preparedness when dealing with additional health risks such as a pandemic, a situation that may be exacerbated under the future climate.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11069-021-05064-2.

Flood risk behaviors of United States riverine metropolitan areas are driven by local hydrology and shaped by race

Flooding risk results from complex interactions between hydrological hazards (e.g., riverine inundation during periods of heavy rainfall), exposure, vulnerability (e.g., the potential for structural damage or loss of life), and resilience (how well we recover, learn from, and adapt to past floods). Building on recent coupled conceptualizations of these complex interactions, we characterize human-flood interactions (collective memory and risk-enduring attitude) at a more comprehensive scale than has been attempted to date across 50 US metropolitan statistical areas with a sociohydrologic (SH) model calibrated with accessible local data (historical records of annual peak streamflow, flood insurance loss claims, active insurance policy records, and population density). A cluster analysis on calibrated SH model parameter sets for metropolitan areas identified two dominant behaviors: 1) "risk-enduring" cities with lower flooding defenses and longer memory of past flood loss events and 2) "risk-averse" cities with higher flooding defenses and reduced memory of past flooding. These divergent behaviors correlated with differences in local stream flashiness indices (i.e., the frequency and rapidity of daily changes in streamflow), maximum dam heights, and the proportion of White to non-White residents in US metropolitan areas. Risk-averse cities tended to exist within regions characterized by flashier streamflow conditions, larger dams, and larger proportions of White residents. Our research supports the development of SH models in urban metropolitan areas and the design of risk management strategies that consider both demographically heterogeneous populations, changing flood defenses, and temporal changes in community risk perceptions and tolerance.

Lessons for climate policy from behavioral biases towards COVID-19 and climate change risks

COVID-19 and climate change share several striking similarities in terms of causes and consequences. For instance, COVID-19 and climate change affect deprived and vulnerable communities the most, which implies that effectively designed policies that mitigate these risks may also reduce the widening inequalities that they cause. Both problems can be characterized as low-probability-high consequence (LP-HC) risks, which are associated with various behavioral biases that imply that individual behavior deviates from rational risk assessments by experts and optimal preparedness strategies. One could view the COVID-19 pandemic as a rapid learning experiment about how to cope more effectively with climate change and develop actions for reducing its impacts before it is too late. However, the ensuing question relates to whether the COVID-19 crisis and its aftermath will speed up climate change mitigation and adaptation policies, which depends on how individuals perceive and take action to reduce LP-HC risks. Using insights into behavioral biases in individual decisions about LP-HC risks based on decades of empirical research in psychology and behavioral economics, we illustrate how parallels can be drawn between decision-making processes about COVID-19 and climate change. In particular, we discuss six important risk-related behavioral biases in the context of individual decision making about these two global challenges to derive lessons for climate policy. We contend that the impacts from climate change can be mitigated if we proactively draw lessons from the pandemic, and implement policies that work with, instead of against, an individual's risk perceptions and biases. We conclude with recommendations for communication policies that make people pay attention to climate change risks and for linking government responses to the COVID-19 crisis and its aftermath with environmental sustainability and climate action.