How People Understand Risk Matrices, and How Matrix Design Can Improve their Use: Findings from Randomized Controlled Studies

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.

Do colored cells in risk matrices affect decision-making and risk perception? Insights from randomized controlled studies

Risk matrices communicate the likelihood and potential impact of risks and are often used to inform decision-making around risk mitigations. The merits and demerits of risk matrices in general have been discussed extensively, yet little attention has been paid to the potential influence of color in risk matrices on their users. We draw from fuzzy-trace theory and hypothesize that when color is present, individuals are likely to place greater value on reducing risks that cross color boundaries (i.e., the boundary-crossing effect), leading to sub-optimal decision making. In two randomized controlled studies, employing forced-choice and willingness-to-pay measures to investigate the boundary-crossing effect in two different color formats for risk matrices, we find preliminary evidence to support our hypotheses that color can influence decision making. The evidence also suggests that the boundary-crossing effect is only present in, or is stronger for, higher numeracy individuals. We therefore recommend that designers should consider avoiding color in risk matrices, particularly in situations where these are likely to be used by highly numerate individuals, if the communication goal is to inform in an unbiased way.

Determination of Air Traffic Complexity Most Influential Parameters Based on Machine Learning Models

Today, aircraft demand is exceeding the capacity of the Air Traffic Control (ATC) system. As a result, airspace is becoming a very complex environment to control. The complexity of airspace is thus closely related to the workload of controllers and is a topic of great interest. The major concern is that variables that are related to complexity are currently recognised, but there is still a debate about how to define complexity. This paper attempts to define which variables determine airspace complexity. To do so, a novel methodology based on the use of machine learning models is used. In this way, it tries to overcome one of the main disadvantages of the current complexity models: the subjectivity of the models based on expert opinion. This study has determined that the main indicator that defines complexity is the number of aircraft in the sector, together with the occupancy of the traffic flows and the vertical distribution of aircraft. This research can help numerous studies on both air traffic complexity assessment and Air Traffic Controller (ATCO) workload studies. This model can also help to study the behaviour of air traffic and to verify that there is symmetry in structure and the origin of the complexity in the different ATC sectors. This would have a great benefit on ATM, as it would allow progress to be made in solving the existing capacity problem.