China 
Africa 
Explore chapters and articles related to this topic
Decision-Making
Published in Nancy J. Stone, Chaparro Alex, Joseph R. Keebler, Barbara S. Chaparro, Daniel S. McConnell, Introduction to Human Factors, 2017
Nancy J. Stone, Chaparro Alex, Joseph R. Keebler, Barbara S. Chaparro, Daniel S. McConnell
What happens in the case of the availability heuristic is that we tend to estimate the likelihood of events that readily come to mind to be higher than those that do not. Some decisions we might make can include choosing to drive rather than to fly, deciding not to enter the water because of a fear of sharks, or standing outside to watch a lightning storm. In each case, it is likely that we have not made a good decision. It is well known that flying is considerably safer than driving and that you are more likely to die from a lightning strike than being bitten by a shark (“Death Odds,” September 24, 1990). Our decisions, though, were likely influenced by our recollections of what was safest. Perhaps recent news reports impacted this recollection, which influenced our decisions.
Determining Probabilistic Inputs for Decision Models
Published in Gerald W. Evans, Multiple Criteria Decision Analysis for Industrial Engineering, 2016
Tversky and Kahneman (1974) are the major researchers in this field. One of the heuristics they identified is the “availability heuristic.” This heuristic involves judging the probability of an event according to how easily these or similar events can be recalled. As an example, one of the important inputs for a simulation model of an emergency (911) call center was a probability distribution over the number of calls made for the same event. A recent event involved an outdoor shooting in a well-to-do neighborhood. Thirty-nine calls were made to the emergency call center as a result of the gunshots heard from the shooting. This salient event would have resulted in an overestimation of the number of calls associated with a single event if the data (relating to this and other events) had not been available for these situations.
Decision Making
Published in Christopher D. Wickens, Justin G. Hollands, Simon. Banbury, Raja. Parasuraman, Engineering Psychology and Human Performance, 2015
Christopher D. Wickens, Justin G. Hollands, Simon. Banbury, Raja. Parasuraman
The availability heuristic indicates that the perceived frequency of different negative consequences of unsafe behavior will be based not on their actual frequency (objective risk), but upon their salience in memory, if those consequences were either directly experienced or learned through description. When these do not correspond, risks can be seriously misestimated. The representativeness heuristic (and base rate ignoring) suggests that we may not be very sensitive to the probability of disastrous consequences at all; and indeed a study by Young, Wogalter, and Brelsford (1992) found that the perceived severity of a hazard has a greater impact on risk estimation than does the probability of the hazard. Finally, it is the case that both perceived severity and probability will be abstract experiences in making the choice, only possibly perceived in the future. As temporal discounting suggest (see 6.3.3), their expected costs may be diminished. In contrast, the cost of compliance imposes a direct tangible and present experience (e.g., the discomfort of wearing a safety device or the inconvenience of adhering to safety procedure), the experience is highly accessible (Kahneman & Frederick, 2002). This analysis suggests that risk mitigation efforts should be directed heavily to reducing the cost of compliance more than increasing the perceived negative risks of the accident.
Students’ intuitively-based (mis)conceptions in probability and teachers’ awareness of them: the case of heuristics
Published in International Journal of Mathematical Education in Science and Technology, 2022
Ayhan Kursat Erbas, Mehmet Fatih Ocal
People’s judgments are intensively affected by easily recalled events. The availability heuristic refers to people’s tendency to determine the probability of an event by the ease of recalling or imagining relevant instances of the event in their lives (Tversky & Kahneman, 1983). Thus, the availability heuristic occurs when people judge events according to the most frequent and relevant events that are easier to recall (Kennis, 2006; Shaughnessy, 1992). According to Schwarz and Vaughn (2002), ‘ … we may consider reliance on ease of recall a heuristic strategy, suggesting that variables known to determine the degree of heuristic processing should influence the extent to which individuals rely on this source of information’ (p. 115). From this point of view, if an event can be easily recalled, people judge it as more important or at least more valuable than those not easily recalled. From the students’ perspective, they intuitively answer probability questions by considering the events that are easier to come to mind, since they evaluate these events as more probable (or likely) than others. For example, Tversky and Kahneman (1983) found that when people were asked whether the first or third position in a word (in English) is more likely to be the letter ‘R’, about two-thirds of them answered that the first position would more likely to be the case since it is easier for people to remember the letter ‘R’ in the first position of any word.
Implementing an uncertainty-based risk conceptualisation in the context of environmental risk assessment, with emphasis on the bias of uncertain assumptions
Published in Civil Engineering and Environmental Systems, 2019
Next, the risk analyst assesses the uncertainties related to (RS′,A′,C′), representing the assessed uncertainty using some uncertainty measure, Q. The specification of (RS′,A′,C′) and Q is based on some background knowledge, K, which is also part of the risk description. The SRA glossary distinguishes between two types of knowledge (SRA 2018, 8): ‘know-how (skill) and know-that of propositional knowledge (justified beliefs)’. Both types are relevant in relation to performing a risk assessment. However, propositional knowledge is most relevant to consider in relation to the risk description per se, as the consequences specified in the risk description can essentially be seen as a set of propositions. As pointed out by one of the referees of this paper, also a third form, know-about, or acquaintance knowledge (cf. Lemos 2007), is relevant in relation to bias. For example, an expert assigning a probability may be influenced by availability heuristics, which essentially refers to the ease with which similar events can be retrieved from memory (e.g. Aven 2012a). The basis for forming propositional knowledge in the form of justified beliefs will typically be data and information. In a risk assessment, these beliefs are often expressed in the form of assumptions (Aven 2014). Uncertain assumptions and their bias will be considered in detail in Section 4.2.