Newspaper Coverage of Causes of Death

Combs, Barbara Harris; Slovic, Paul

doi:10.1177/107769907905600420

Cited by 440 publications

(154 citation statements)

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“…The present experiments corroborate the basic finding from many other sampling approaches that decision makers -whether lay people of experts -take sample information for granted, uncritically and naively, even when it is obvious that samples are severely biased Kareev et al, 2002). Maybe one of the most prominent goals of research on rationality and intellectual emancipation is to sensitize decision makers to major sampling biases in the environment (Denrell, 2005;Taylor, 1991) -due to media coverage (Combs & Slovic, 1979), restricted information access (Fiedler, in press;Fiedler & Walther, 2003), selective memory (Tesser, 1978), unequal communicability (Kashima, 2000), or restricted designs (Wells & Windschitl, 1999) -to educate people in what samples are good for and to engage in corrections of biased samples wherever this is possible. In those remaining cases where corrections of biased samples are not available, the most prominent goal is to understand that ignoring a sample may be better, and more rational, than accurately utilizing a sample tailored for the wrong purpose.…”

Section: Ultimate Sampling Dilemmasupporting

confidence: 83%

The ultimate sampling dilemma in experience-based decision making.

Fiedler

2008

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Computer simulations and two experiments are reported to delineate the ultimate sampling dilemma, which constitutes a serious obstacle to inductive inferences in a probabilistic world. Participants were asked to take the role of a manager who is to make purchasing decisions based on positive versus negative feedback about three providers in two different product domains. When information sampling (from a computerized data base) was over, they had to make inferences about actual differences in the data base from which the sample was drawn (e.g., about the actual superiority of different providers, or about the most likely origins of negatively valenced products). The ultimate sampling dilemma consists in a forced choice between two search strategies that both have their advantages and their drawbacks: natural sampling and deliberate sampling of information relevant to the inference task. Both strategies leave the sample unbiased for specific inferences but create errors or biases for other inferences. Many everyday decision problems rely on direct environmental-learning experience.Teachers' grading decisions are informed by observations of students' performance in different disciplines. Personnel selection relies on applicants' reactions to various tasks and interview topics. Or, consumer choices reflect the information acquired about brands or providers in different product domains. There appears to be a simple and straightforward way of optimizing such experience-based decisions: If only the learning process relies on a sufficiently large sample of observations, it must be possible to discern the optimal decision through optimal data selection (Oaksford & Chater, 2003).The learning task seems to have a clear-cut structure. For a consumer to make an optimal choice between alternative providers, it is only necessary to compare the quality feedback that is available for different providers in specific product domains. Granting that the feedback is reliable and accurately reflects the contingency between providers and product quality, figuring out the best provider, with the highest rate of positive evaluations, should be straightforward. The consumer's task should be easy to solve if only the differences between providers are strong enough and sufficient observations are available.The aim of the present investigation is to contest this seemingly plausible sketch of simple experience-based decision making. In fact, finding a generally correct solution to such clearly structured problems is fraught with huge difficulties. It is actually impossible, because every sample of observations about mundane decision problems entails the potential to be misleading under certain conditions. I refer to the "ultimate sampling dilemma" to highlight the fact that any reasonable sampling strategy, which serves to optimize one decision, produces a sampling bias with regard to other decisions informed by the same data. Illustration of the Ultimate Sampling DilemmaThat judgments and decisions depend crucially on the samples of...

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Section: Ultimate Sampling Dilemmasupporting

confidence: 83%

The ultimate sampling dilemma in experience-based decision making.

Fiedler

2008

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…For example, participants correctly reported that desktop computers consume more energy than laptop computers, but they greatly underestimated the magnitude of this difference (a perceived ratio of 1.2 rather than 2.9). This compression bias (22) is consistent with participants using the 100-Wh reference point as an anchor from which they adjusted insufficiently (15,16).…”

Section: Resultssupporting

confidence: 63%

“…The availability heuristic (13-15), a judgment process in which the frequency of an event is estimated according to the ease with which specific instances come to mind, provides one explanation for this result. Judging by availability can result in estimates that are generally accurate but with systematic overestimates for frequencies of vivid low-probability events (13,15). A second explanation is provided by the anchoring-andadjustment heuristic (14,16), in which a person generates a numerical judgment by first adopting a salient reference point as a starting value and then adjusting his or her judgment in the desired direction.…”

mentioning

confidence: 99%

Public perceptions of energy consumption and savings

Attari

DeKay

Davidson³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

558

386

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In a national online survey, 505 participants reported their perceptions of energy consumption and savings for a variety of household, transportation, and recycling activities. When asked for the most effective strategy they could implement to conserve energy, most participants mentioned curtailment (e.g., turning off lights, driving less) rather than efficiency improvements (e.g., installing more efficient light bulbs and appliances), in contrast to experts' recommendations. For a sample of 15 activities, participants underestimated energy use and savings by a factor of 2.8 on average, with small overestimates for low-energy activities and large underestimates for high-energy activities. Additional estimation and ranking tasks also yielded relatively flat functions for perceived energy use and savings. Across several tasks, participants with higher numeracy scores and stronger proenvironmental attitudes had more accurate perceptions. The serious deficiencies highlighted by these results suggest that well-designed efforts to improve the public's understanding of energy use and savings could pay large dividends.climate change | decision making | judgment | environmental behavior | anchoring A nthropogenic CO 2 emissions are contributing to global climate change (1) and could negatively impact our way of life if serious action is further delayed. The United States produces 21% of the world's CO 2 emissions, with 98% of US emissions attributed to energy consumption (2).According to Pacala and Socolow (3), increasing energy efficiency and curtailing activities that consume energy may be our cheapest options for stabilizing atmospheric CO 2 concentrations below a doubling of preindustrial concentrations. Following the analogy of stabilization wedges (3), Dietz et al. (4) devised a potential behavioral wedge, recommending specific behavioral changes, such as weatherization investments, to be adopted by US households to decrease their emissions. Vandenbergh et al. (5) identified seven actions, such as reducing automobile idling and substituting compact fluorescent light bulbs (CFLs) for incandescent bulbs, that have the potential to achieve large emission reductions at a low cost to the government and with a net savings for individuals. In related work, Gardner and Stern (6) identified a short list of the most effective actions US households could take to decrease their contributions to climate change. They argued that by changing the selection and use of household and motor vehicle technologies, households could reduce their energy consumption by nearly 30%-without waiting for new technologies, making major economic sacrifices, or losing a sense of well-being. If households effectively implemented all of Gardner and Stern's recommended changes, US energy consumption would be reduced by approximately 11%. Similarly, Dietz et al. (4) estimated that behavioral interventions could reasonably achieve a 20% reduction in CO 2 emissions from household energy use (a 7.4% reduction in total US emissions) within 10 y.Gardner and...

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“…Young et al 's (Young et al, 2008) findings suggest that the increased likelihood to seek care for newly medicalized disorders presented with medical language labels in the present study is not due to an individual thinking that, for example, "gastroesophageal reflux disease" is more common than "chronic heartburn. " Therefore, the increased urgency with which individuals report they would seek care is unlikely to be explained by participants relying on base-rate probabilities (Jemmott et al, 1986), or their personal perceptions of risk (Coombs and Slovic, 1979).…”

Section: Discussionmentioning

confidence: 99%

The Impact of Medical Terminology in Self-Triage Decision-Making

D'Angelo

Humphreys

et al. 2017

Front. Commun.

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Objective: The present study examined whether medical terminology impacts self-triage decisions (deciding if and when to seek medical treatment) compared to lay terminology.Methods: Undergraduate psychology student participants read 32 hypothetical health scenarios and reported how urgently they would seek care ["In this situation, when would you seek medical attention?" presented along with a seven-point scale, with anchors at either extreme: "Never (At the next scheduled appointment, if at all)" and "As soon as possible (Within the hour)," reflecting the options coded as 1 and 7, respectively]. Data were collected in person via a paper-based questionnaire. Scenarios included a description of symptoms, which were labeled as consistent with a particular medical disorder using either a lay disease label (e.g. "Heart Attack") or medical terminology (e.g. "Myocardial Infarction"). The 32 health scenarios represented medical disorders that purposefully reflected a spectrum of severity, commonality, and how recently the medical terminology had entered common use. results: For disorders for which medical terminology has been more recently established, participants reported that they would seek care more urgently when the disorder was presented using a medical label [mean (SE) = 4.08 (0.23)] than when they were described using the lay disease labels [3.32 (0.23), t(35) = 5.36, p < 0.001, e.g., "seborrheic dermatitis" versus "chronic dandruff"]. However, this differential response to medical or lay disease labels was not observed for disorders for which medical terminology has been more well established [medical labels = 5.16 (0.18); lay labels = 4.89 (0.18), t(35) = −1.67, p = 0.104, e.g., "heart attack" and "myocardial infarction"].conclusion: These results indicate that self-triage decision-making can be readily influenced by the terminology used to identify a disorder; however, this phenomenon appears to be particularly relevant for disorders in which public opinion may still be in flux.

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Newspaper Coverage of Causes of Death

Cited by 440 publications

References 0 publications

The ultimate sampling dilemma in experience-based decision making.

The ultimate sampling dilemma in experience-based decision making.

Public perceptions of energy consumption and savings

The Impact of Medical Terminology in Self-Triage Decision-Making

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