Percep&on of Risk Topic 2 Content of the Lectures Topic 1: Risk concept Topic 2: Percep&on of risks Topic 3: Risk communica&ons Topic 4: Trust and credibility Topic 5: Labeling risks Topic 6:Par&cipatory decision making and dialogue Topic 7: Disclosure of uncertain&es Topic 8:Precau&onary measures and risk management Topic 9: Evidence characteriza&on Topic 10: Tips for risk communica&on 1
Risk percep&ons create reality. The risks that kill you are not necessarily the risks that anger and frighten you. A look back: Fatality percep&on Sarah, Lichtenstein; Paul, Slovic; Baruch, Fischhoff; Mark, Layman; Barbara, Combs. Judged frequency of lethal events Journal of Experimental Psychology: Human Learning and Memory. Vol 4(6), Nov 1978, 551-578 2
Important Roots of Risk Percep&on Research A. Tversky D. Kahneman P. Slovic Bounded Ra&onality Remarks hap://www.ted.com/talks/lang/eng/ dan_ariely_asks_are_we_in_control_of_our_o wn_decisions.html 3
Bounded Ra&onality H. Simons Concept of Bounded Ra&onality Humans have to struggle with three constraints: (1) only limited, ofen unreliable, informa&on is available regarding possible alterna&ves and their consequences, (2) human mind has only limited capacity to evaluate and process the informa&on that is available, and (3) only a limited amount of &me is available to make a decision. Humans aren't ra&onal decision makers Research into bounded ra&onality: Heuris&cs & biases, fallacies Tversky, A., Kahneman, D. (1974). Judgment under Uncertainty: Heuris&cs and Biases. Science, New Series, Vol. 185, No. 4157, pp. 1124-1131 Biases and Heuris&cs Bias and fallacy A cogni&ve bias is the human tendency to draw incorrect conclusions based on distorted informa&on processing Biases can be observed Heuris&c Rule of thumb, strategy to reduce cogni&ve efforts Heuris&c can not be observed 4
Biases and Heuris&cs Confirma&on bias: The tendency to search for or interpret informa&on in a way that confirms one's own beliefs Nega&vity bias: The tendency to pay more aaen&on to nega&ve and to put more weight on than posi&ve events Omission bias: The tendency to judge harmful ac&ons as worse, or less moral, than equally harmful omissions Op&mism bias: The tendency to be over- op&mis&c about the outcome of an risky ac&ons. Probability neglect: The tendency to ignore probability when making a risk related judgment. Zero- risk bias: The preference for reducing a small risk to zero over a greater reduc&on in a larger risk Kahneman D., Slovic P., and Tversky, A. (Eds.) (1982) Judgment Peter Under Wiedemann Uncertainty: Heuris&cs and Biases. New York: Cambridge University Press Example Conjunc&on Fallacy Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimina&on and social jus&ce, and also par&cipated in an&- nuclear demonstra&ons. The subjects were then asked which one of the two statements they thought was more probable. Statement A: Linda is a bank teller. Statement B: Linda is a bank teller who is ac&ve in the feminist movement. 5
Example Conjunc&on Fallacy 86% of the study par&cipants of Tversky& Kahneman said that statement B was more probable. However, this result does not fit with a model of probability that states the probability of A is always greater than the probability of A and B. Heuris&cs Anchoring: People use arbitrary yards&cks when making numerical es&mates under ignorance Availability heuris&c: People assess the frequency of an event, based on how easily an example can be brought to mind. Affect heuris&c: People rely on their affects to make decisions and judgements Representa&veness heuris&c: People tend to judge the probability of an event by finding a comparable known event and assuming that the probabili&es will be similar. Shah, A. K., & Oppenheimer, D. M. (2008). Heuris&cs made easy: An effort- reduc&on framework. Psychological Bulle&n, 134(2), 207-222. 6
Example: Availability Heuris&c Example: Representa&veness Heuris&c What is truly random? 1. ABABABAB 2. AAAABBBB 3. ABAABABB 7
Sarah, Lichtenstein; Paul, Slovic; Baruch, Fischhoff; Mark, Layman; Barbara, Combs. Judged frequency of lethal events Journal of Experimental Psychology: Human Learning and Memory. Vol 4(6), Nov 1978, 551-578 Sarah, Lichtenstein; Paul, Slovic; Baruch, Fischhoff; Mark, Layman; Barbara, Combs. Judged frequency of lethal events Journal of Experimental Psychology: Human Learning and Memory. Vol 4(6), Nov 1978, 551-578 8
Variables Voluntariness Controllability Dread Catastrophic poten&al Familiarity Newness/Known to science Risk vulnerable persons Fairness But not probability 9
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Independent variables Common Factor 2 Risk Percep&on Common Factor 1 11
A look back: The psychometric approach Factors Voluntariness Familiarity Controllability Fatality Dreadfulness Fischhoff et al. 1978, p. 146 12
Problems Highly aggregated data Cold judgment situa&on No gathering of probability es&mates in the surveys Cannot explain social amplifica&on of risk percep&ons Is not rooted in psychological theories on informa&on processing and judgment Intui&ve Toxicology How do lay people understand basic concepts of toxicology Dose- response sensi&vity, Trust in animal and bacterial studies, Attudes toward chemicals, Concep&ons of toxicity including the toxicity of natural vs. synthe&c substances Toxicity of prescrip&on drugs vs. chemicals in general, Cause- effect rela&onships between exposure to chemicals and human health 13
Intui&ve Toxicology Kraus, Malmfors & Slovic 1992, p. 217 Lessons learned There are different approaches for measuring risk percep&ons Key is the evalua&on of rela&onship between intui&ve vs. analy&cal thinking Risk percep&on are mostly descrip&on- based. Intui&ve risk evalua&ons lacks a proper considera&on of probabili&es as well as a proper understanding of risk assessment methodology Dynamics of risk percep&on is poorly understood. 14
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