Risk perception is the subjective judgement that people make about the characteristics and severity of a risk. Risk perceptions often differ from statistical assessments of risk since they are affected by a wide range of affective (emotions, feelings, moods, etc.), cognitive (gravity of events, media coverage, risk-mitigating measures, etc.), contextual (framing of risk information, availability of alternative information sources, etc.), and individual (personality traits, previous experience, age, etc.) factors. Several theories have been proposed to explain why different people make different estimates of the dangerousness of risks. Three major families of theory have been developed: psychology approaches (heuristics and cognitive), anthropology/sociology approaches (cultural theory) and interdisciplinary approaches (social amplification of risk framework).
Early theories The study of risk perception arose out of the observation that experts and lay people often disagreed about how risky various technologies and natural hazards were. The mid 1960s saw the rapid rise of nuclear technologies and the promise of clean and safe energy. However, public perception shifted against this new technology. Fears of both longitudinal dangers to the environment and immediate disasters creating radioactive wastelands turned the public against this new technology. The scientific and governmental communities asked why public perception was against the use of nuclear energy when all the scientific experts were declaring how safe it really was. The problem, as non-experts perceived it, was a difference between scientific facts and an exaggerated public perception of the dangers. A key early paper was written in 1969 by Chauncey Starr. Starr used a revealed preference approach to find out what risks are considered acceptable by society. He assumed that society had reached equilibrium in its judgment of risks, so whatever risk levels actually existed in society were acceptable. His major finding was that people will accept risks 1,000 times greater if they are voluntary (e.g. driving a car) than if they are involuntary (e.g. a nuclear disaster). This early approach assumed that individuals behave rationally by weighing information before making a decision, and that individuals have exaggerated fears due to inadequate or incorrect information. Implied in this assumption is that additional information can help people understand true risk and hence lessen their opinion of danger. While researchers in the engineering school did pioneer research in risk perception, by adapting theories from economics, it has little use in a practical setting. Numerous studies have rejected the belief that additional information alone will shift perceptions.
Psychological approach The psychological approach began with research in trying to understand how people process information. These early works maintained that people use cognitive heuristics in sorting and simplifying information, leading to biases in comprehension. Later work built on this foundation and became the psychometric paradigm. This approach identifies numerous factors responsible for influencing individual perceptions of risk, including dread, novelty, stigma, and other factors. Research also shows that risk perceptions are influenced by the emotional state of the perceiver. The valence theory of risk perception only differentiates between positive emotions, such as happiness and optimism, and negative ones, such as fear and anger. According to valence theory, positive emotions lead to optimistic risk perceptions whereas negative emotions influence a more pessimistic view of risk. Research also has found that, whereas risk and benefit tend to be positively correlated across hazardous activities in the world, they are negatively correlated in people's minds and judgements.
Heuristics and biases The earliest psychometric research was done by psychologists Daniel Kahneman and Amos Tversky, who performed a series of gambling experiments to see how people evaluated probabilities. Their major finding was that people use a number of heuristics to evaluate information. These heuristics are usually useful shortcuts for thinking, but they may lead to inaccurate judgments in some situations – in which case they become cognitive biases.
Representativeness: is usually employed when people are asked to judge the probability that an object or event belongs to a class / processes by its similarity: insensitivity to prior probability insensitivity to sample size misconception of chance insensitivity to predictability illusion of validity misconception of regression Availability heuristic: events that can be more easily brought to mind or imagined are judged to be more likely than events that could not easily be imagined: biases due to retrievability of instances biases due to the effectiveness of research set biases of imaginability illusory correlation Anchoring and Adjustment heuristic: people will often start with one piece of known information and then adjust it to create an estimate of an unknown risk – but the adjustment will usually not be big enough: insufficient adjustment biases in the evaluation of conjunctive and disjunctive event (conjunction fallacy) anchoring in the assessment of subjective probability distributions Asymmetry between gains and losses: People are risk averse with respect to gains, preferring a sure thing over a gamble with a higher expected utility but which presents the possibility of getting nothing. On the other hand, people will be risk-seeking about losses, preferring to hope for the chance of losing nothing rather than taking a sure, but smaller, loss (e.g. insurance). Threshold effects: People prefer to move from uncertainty to certainty over making a similar gain in certainty that does not lead to full certainty. For example, most people would choose a vaccine that reduces the incidence of disease A from 10% to 0% over one that reduces the incidence of disease B from 20% to 10%. Another key finding was that the experts are not necessarily any better at estimating probabilities than lay people. Experts were often overconfident in the exactness of their estimates, and put too much stock in small samples of data.
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