The Cognitive Psychology of Sensitivity to Human Fatalities

Abstract

Year after year, thousands of human lives are permanently extinguished by large-scale events such as disasters, epidemics, and armed conflicts, while millions more are claimed by frequent small-scale events, such road accidents and fires. Preventing these fatalities should be a priority for policymakers, because death is irreversible, and survival necessarily precedes other forms of human welfare. Minimizing fatalities requires that policymakers respond to deadly events and that they effectively focus their attention, efforts, and life-saving resources on preventing the largest (aggregated) death tolls. However, the deployment of any major intervention, even one designed to save lives, requires sufficient political will, which in turn depends on people being moved to action by the number of lives at risk. All else being equal, responses to deadly events should be proportional to their potential death tolls, and support for life-saving interventions should grow steadily with the number of lives they are expected to save. Unfortunately, people's reactions to deadly events generally fall short of these principles and instead reveal systematic limitations in their ability to properly evaluate human fatalities. This article discusses some of the cognitive factors that govern, and often skew, the way people perceive and respond to deadly events. I show that our failures to correctly prioritize and address the biggest causes of human casualties are driven by limitations and biases in perception, attention, and categorization. Understanding these psychological fallibilities will help policymakers recognize, and ultimately guard against, biases that hinder the deployment of important life-saving policies.

From “sense of number” to “sense of magnitude”: The role of continuous magnitudes in numerical cognition

“…For example, a restaurant patron may wish to evaluate the number of calories associated with each item on the menu, whereas a policy maker considering measures to increase road safety needs to evaluate their potential reductions in human fatalities. As with financial gains and losses, subjective evaluations of non-monetary outcomes tend to exhibit diminishing sensitivity (e.g., Olivola 2015;Slovic 2007). Moreover, the role of numerical magnitude evaluation in decision making extends beyond assigning (dis)utilities to the outcomes themselves, as one also needs to consider their likelihoods of occurrence and when (in time) they are expected to occur.…”

Public risk perception and emotion on Twitter during the Covid-19 pandemic

“…Both the Weber–Fechner law and power-law relationships between the Death NLS and the daily number of reported deaths accurately model the data. Each captures the phenomenon in which “the first few fatalities in an ongoing event elicit more concern than those occurring later on” (Olivola 2015 ). By way of comparison, we present in Table 5 the normalised root mean squared errors (NRMSE), defined as for these models, in addition to a linear model between and as a baseline “null” model.…”

Numerical magnitude evaluation as a foundation for decision making

“…For example, a restaurant patron may wish to evaluate the number of calories associated with each item on the menu, whereas a policy maker considering measures to increase road safety needs to evaluate their potential reductions in human fatalities. As with financial gains and losses, subjective evaluations of non-monetary outcomes tend to exhibit diminishing sensitivity (e.g., Olivola 2015; Slovic 2007). Moreover, the role of numerical magnitude evaluation in decision making extends beyond assigning (dis)utilities to the outcomes themselves, as one also needs to consider their likelihoods of occurrence and when (in time) they are expected to occur.…”