What information belongs in a warning

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Psychology & Marketing Vol. 15(7):663–686 (October 1998)q 1998 John Wiley & Sons, Inc. CCC 0742-6046/98/070663-24

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What Information Belongsin a Warning?Baruch Fischhoff, Donna Riley, Daniel C. Kovacs, and MitchellSmallCarnegie Mellon University

ABSTRACT

A general approach is described for determining the informationcontent of warnings. It begins with a formal analysis of themagnitudes of the risks arising from misuse (or even from properuse) of a product. It proceeds with structured, open-endedinterviews intended to elicit consumers’ naive conceptualizations ofthe processes creating and controlling those risks. Communicationsare then focused on information filling the most consequential gapsin their knowledge. The implementation of those warnings willdepend on the extent of the knowledge gaps, and the opportunitiesfor closing them. This approach allows for an estimate of theresidual problems to be expected, if a warning program isimplemented. It can also help to focus the policy debate overwhether a product warning will achieve an acceptable level ofmisunderstanding. q 1998 John Wiley & Sons, Inc.

Imagine that you had to get people’s attention about a potential risk.What would you tell them? Presumably, things that they need to know,but currently do not. That means plugging the critical gaps in theirknowledge, while using their time efficiently. Such focus should increasethe chances of their getting the essential information before losing in-terest. It might even increase the credibility of whatever information ispresented—because the presenter has taken the trouble to use recipi-ents’ time well. Conceivably, it could increase the chances of drawingtheir attention in the first place, if the warning is seen as presentingnovel (even interesting) information.

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Identifying such information is conceptually straightforward. It in-volves the following steps:

1. Determine what information is most critical to understanding howa risk is created and controlled.

2. Assess consumers’ current beliefs regarding those facts.3. Design messages focused on the critical gaps between what con-

sumers know and what they need to know.4. Evaluate the effectiveness of those messages in closing the gaps,

in tests with consumers forced to consider them.5. Develop and evaluate a delivery mechanism capable of drawing

actual consumers’ attention.

For the past 20 years or so, our research group has been working onSteps 1–4 of this process, drawing upon (and perhaps expanding) thetools of behavioral decision theory (Plous, 1993; Yates, 1990). This is aninterdisciplinary field, whose common thread is characterizing humanbehavior in terms that can be contrasted with normatively defined stan-dards of optimality. Like other fields, it has found that easy-to-identifyresearch tasks can take some doing to execute. In this case, Steps 1 and2 have proven to be surprisingly problematic. That is, it is particularlyhard to determine the normative standard (what is worth knowing) andthe current state of beliefs (where are people coming from). Althoughwarning design has not been a focal topic of this research, its approachesmay complement those described elsewhere in this special issue and thewarning literature more generally (Laughery, Wogalter, & Young,1995). It seems compatible with some emerging trends in the warningliterature itself (Edworthy & Adams, 1996; McCarthy, Ayres, & Wood,1995, Papastavrou & Lehto, 1996).

The following two sections provide a brief history of behavioral deci-sion theory research on risk communication, including points of accessto the scientific literature. The first of these sections looks at commu-nicating about the magnitude of risks, essential to deciding whether toundertake a risky action. The second looks at communicating the pro-cesses producing and controlling a risk, essential to deciding how tobehave, if one does take the action. Two case studies follow. The firstdeals with the risks of household chemicals, especially methylene chlo-ride used for stripping paint. Although still incomplete, that exampleshows the role that a combination of formal modeling and behavioralresearch can play in determining the content of consumer warnings. Thesecond study focuses on another chlorinated solvent, perchloroethylene,which is used for dry cleaning. Although it was motivated by the desirefor a warning program, its results suggested an alternative risk-com-munication strategy. The final section uses the general perspective to

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Figure 1 Comparison of the best-fit quadratic curve for geometric mean estimates ofsubjects told either that roughly 50,000 people die each year in traffic accidents in theUnited States or that roughly 1,000 people die each year in the United States fromaccidental electrocutions. Based on data from Lichtenstein et al. (1978).

frame the question of the adequacy of warnings as a risk-managementstrategy.

UNDERSTANDING RISK ESTIMATES

Behavioral decision-making researchers were initially drawn into therisk business by technology managers alarmed at declining public trustin their technologies (such as drugs, energy sources, and waste-disposalpractices). The presenting symptoms in the technologists’ descriptionswere that the public greatly overestimates risks known to be small, oreven trivial. As a result, the public creates needless problems for tech-nologies that could bring great benefit to the public (and to shareholders,managers, etc.). A natural first research step was to determine whetherthese claims were sufficiently valid to cast doubt on the legitimacy ofthe public’s worries. Claims about public competence have implicationsfor the allocation of political power in a society. If citizens cannot un-derstand risks, then decisions might be better left to those who can.

Eliciting Risk Estimates

As a result, the first decade or so of risk-perception research focused onthe accuracy of lay people’s quantitative risk estimates. Figure 1 shows



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an early, and typical, result. Citizens (here, students and members ofthe League of Women Voters) estimated the number of people dyingannually from 40 causes of death in the United States. Because pre-tests showed great variability in the ranges of numbers used by dif-ferent subjects, respondents were provided public health statistics forone additional cause of death, either motor vehicle accidents (about50,000 at that time) or electrocution (1000).1 That value was intendedto provide a feeling for the kinds of numbers to use for respondents whomight be unfamiliar with how many people live (or die) in the UnitedStates.

In both groups, there was a strong correlation between the deathrates estimated by subjects and by public health researchers. However,considering the wide range in statistically estimated risk levels, it wouldbe extraordinary if people had not picked up on some of the differencesbetween large and small risks. One less predictable result is that esti-mates in the two groups were highly correlated with one another, andwith estimates elicited with other response modes (e.g., asking subjectsto identify the riskier of two causes of death and the ratio of their re-spective death tolls). Thus, citizens might have an ordinal (even inter-val) subjective ordering of risks in their heads, which emerges regard-less of how questions are posed. However, the two groups produced quitedifferent absolute risk estimates. Specifically, the motor-vehicle-acci-dent group produced estimates that were higher by a factor of 2–5 thanthose produced by the electrocution group. Such an anchoring effect isconsistent with general psychophysical principles (Poulton, 1989). Itsmagnitude here suggests just how unfamiliar subjects were with thisspecific response mode in this specific context—hence, how sensitivethey were to the elicitation procedure. Although both number anddeaths are common terms, it is unusual for lay people to think abouttheir combination in a national context. (One also might ask just what“50,000 deaths” really means to anyone.)

Estimates for some causes of death had consistently positive or neg-ative residuals, relative to the best-fit curves in the figure. Further stud-ies found that, by and large, the overestimated risks involved causes ofdeath that were disproportionately available to people (e.g., in terms ofpersonal familiarity and newspaper reporting). A simple account ofthese results (which is generally consistent with basic research into fre-quency perception) is that people do a pretty good job of tracking in-stances that come directly to their attention (Hasher & Zacks, 1984;Tversky & Kahneman, 1973). However, they have difficulty correctingfor systematic biases in those exposure processes (or even, perhaps,thinking spontaneously about the possibility of such biases). In addition,

1The study was at a time when the death penalty was suspended, so that all deaths were accidental.



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their quantitative estimates of frequency depend on the specific re-sponse mode being used. As mentioned, the degree of dependence onelicitation procedure might suggest how good a gut feeling people havefor the numbers in question.

By analogy, citizens should have a relatively accurate intuitive feel-ing for the frequency of risky outcomes that are reported in proportionto their occurrence. If this hypothesis is evaluated by direct questioning,multiple methods might be used, lest a single method artifactually de-pict citizens as overestimating or underestimating risk (Fischhoff &MacGregor, 1983). Warnings that provided risk levels would circumventthe need to extract estimates from occurrences.

Sensitivity

Whereas it is troubling to see risk estimates vary with meaninglessvariations in response mode, other studies have shown an encouragingsensitivity to more meaningful variations. For example, people haveshown sensitivity to just how risk is defined (e.g., is it just annual fa-talities or does it include other dimensions of risk, such as their cata-strophic potential and how equitably they are distributed?) (Fischhoff,Watson, & Hope, 1984). How risk is defined can have important impli-cations for how hazards are managed in society (i.e., when riskinessvaries significantly as a function of definition). Indeed, the policy com-munity increasingly recognizes the need to negotiate definitions beforeconducting risk analyses (Davies, 1996; EPA, 1993; National ResearchCouncil, 1996). These studies have, however, focused on people’s per-ceptions of societal risks, rather than on the features of personally ex-perienced risks that might (or might not) grab people’s attention, inducecaution, and create a desire for warnings.

Another test of sensitivity arises when people are asked to thinkaloud as they answer questions about risk. If they understand the issueswell, then they will raise concerns that technical specialists also con-sider germane. We have asked teens to perform this task for deliberatelyambiguous questions regarding risks that they might face, such as,“What is the probability that someone who drinks and drives will getinto an accident?” That question has no real answer without knowing(or making some assumption about) the amount of drinking, the numberof trips, and the severity of the accidents involved (among other things).We have found that teens, drawn from both high- and low-risk popu-lations, do a pretty good job of recognizing the deficiencies in these ques-tions (Fischhoff, 1994; Quadrel, 1990). The most notable exception isthat they are not interested in the amount of exposure for two sex-re-lated questions (getting AIDS, getting pregnant). This confirms otherresults suggesting that people (and not just teens) have difficulty ap-preciating how risks accumulate through repeated sexual activity (Lin-



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ville, Fischer, & Fischhoff, 1993; Luker, 1975; Shaklee & Fischhoff,1990).

By implication, when designing warnings, it is important to focus onthe kinds of risk that matter to people, whether those be mortality aloneor various forms of morbidity. Where the nature of risk is left unspeci-fied, it cannot be assumed that people will guess correctly at its defini-tion. Nor need they make the extrapolation from one expression to an-other (e.g., from single to multiple exposure, from safety to residual risk)(Kahneman & Tversky, 1984).

Modeling Risky Decisions

Although it is natural to ask about people’s ability to estimate the mag-nitudes of risks, not all errors are meaningful. The importance of anydeviation must be interpreted in the context of specific decisions. Anactivity may have so much (or so little) expected benefit that it will bedone (or avoided) even if its risks are substantially misestimated. Thisrealization has prompted attention to the sensitivity of decisions to er-rors in estimates (von Winterfeldt & Edwards, 1986). Warning aboutthings that do not matter can waste people’s time, undermine one’s cred-ibility, and contribute to the clutter that obscures which risks reallymatter (Papastavrou & Lehto, 1996). Sometimes, the sensitivity (or in-sensitivity) of decisions to the precision of estimates is obvious. Some-times, though, more deliberate analysis is needed. A risk that is com-pletely unacceptable on a vacation might be a small price to pay for anotherwise beneficial surgical procedure—depending on the full packageof risk and benefits.

Merz, Fischhoff, Mazur, & Fischbeck (1993) offer one general proce-dure for performing this balancing act, focusing on the need to warnpatients about the risks of carotid endarterechtomy. Scraping out themain artery to the brain can reduce the risk of stroke for individualswith arteriosclerosis there. Were there no side effects (and were moneyno object), every candidate would undergo the procedure. Unfortu-nately, many problems are possible, including iatrogenic strokes. Onewarning strategy is to tell candidates about every possibility. Anotheris to tell them primarily about the things that should matter the most(while hiding nothing).

“Matter the most” approximates the materiality standard for in-formed consent maintained by roughly half the states (i.e., tell patientswhatever is material to their decisions—rather than telling them whatother physicians are saying, which is the standard in the remainingstates). Merz et al. operationalized that standard by creating a hypo-thetical population of patients with different physical states (e.g., howsick, how vulnerable to side effects) and values (e.g., desired trade-offsbetween short-term and long-term outcomes). In effect, a decision tree



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was drawn for each of these hypothetical patients, created to resemblethe actual population of patients. Assuming that each patient was (orat least would want to be) a rational decision maker for this fatefulchoice, Merz et al. asked whether learning about each side effect wouldlead to declining the surgery. The modeling showed that only a few ofthe potential problems mattered for very many patients. As mentioned,all candidates would take the surgery, were it risk (and cost) free. Forabout 15% of these hypothetical patients, the benefits would be out-weighed by the risks of dying from surgery; another 5% or so shoulddecline surgery upon learning about the risks of stroke and of neurolog-ical deficit. If those—relatively likely and relatively severe—side ef-fects are not enough to tip the balance, then the other ones would not.Arguably, physicians seeking informed consent should focus on ensur-ing that patients facing surgery understand those potentially criticalrisks.

The same logic would apply to prioritizing the risks worth mentioningon warning labels. It becomes more tenuous as the population of usersbecomes more diverse in ways that give them different priorities re-garding the risks for which warnings matter. Understanding a riskmeans grasping both the probabilities and the meanings of possible ad-verse consequences. In the example, the meaning of death and strokeshould be relatively clear to individuals considering surgery in order toavoid just those fates. If so, then just the probabilities need to be madeclear. It might take a special effort to explain what life is like for peoplewith the specific neurological problems that this surgery might cause.It is an empirical question whether a physician (or label) can createunderstanding good enough that it will lead to the same choices as com-plete understanding.

UNDERSTANDING RISK PROCESSES

All these studies assume that it is the numbers that matter. However,often that is not the case. People are not just facing a specific decision,waiting for a missing number—upon receipt of which they will run thecalculations needed to identify the best course of action. Rather, theymight just be trying to get their bearings, in anticipation of possiblefuture decisions. With a consumer product, they might be trying to feelcompetent enough to use it safely, to anticipate possible problems, tofigure out what to ask a merchant (or a more knowledgeable friend), toassess the feasibility of following the instructions. Or, even if they dojust need a number, they may not be able to absorb it in numerical form.They might get a better feeling for riskiness from an understanding ofhow risks are created and controlled. Such process information may alsobe essential for safe use, so that consumers understand the rationale



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Figure 2 “Mental models” strategy for risk-communication development. Final stagesare repeated until adequate improvement is achieved.

for precautions—both for motivational reasons and for devising strat-egies to cope with mishaps.

For the last 10 years, we have been concerned with how to identifyand provide such process information, focusing on people’s greatestneeds (Bostrom, Fischhoff, & Morgan, 1992; Fischhoff, Bostrom, &Quadrel, 1997; Morgan, Fischhoff, Bostrom, Lave, & Atman, 1992). Tothis end, we have developed a variant on the mental models (or mentalmaps or cognitive models) approaches that have proven useful in otherareas of basic and applied psychology (Kearney & Kaplan, in press;Rouse & Morris, 1986). Figure 2 provides a schematic summary of themethod. This section will focus on that method. Although not the onlyway to develop such communications, it does, we believe, provide a taskanalysis of the issues facing any method.

Step 1: Develop Expert Model. The procedure begins by creating aformal model of the underlying processes, a model that could, in prin-ciple, be used to calculate the magnitude of the risks in any given in-stantiation. Actually performing those calculations requires masteringthe relevant technical literature and summarizing it in quantitativeterms. A more modest aspiration is simply identifying the major causalfactors and the relations among them (Fischhoff & Downs, 1997). Doingso can define the domain of discourse more deliberately than seems tohave been the case with many health and safety communications (whererecipients might, justifiably, ask “why are they telling me that—andjust that?”). Our preferred representation is the influence diagram—aform of directed network, in which two nodes are connected if the valueof the variable at the head of an arrow depends on the value of thevariable at its tail. These diagrams have the advantages of being di-rectly related to decision trees, while being easily scanned and reviewed



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by those involved in their compilation (Burns & Clemen, 1993; Howard,1989; Morgan & Henrion, 1990).

Steps 2 and 3: Open-Ended Interviews. Once the risky processeshave been circumscribed, we interview people potentially exposed tothem, hoping to elicit their comparable perceptions. In order to allowmaximum freedom for expressing naive conceptualizations, the inter-views begin with the most general questions, such as “tell me aboutradon/AIDS/climate change.” Interviewees are asked to expand uponeverything that they offer (“tell me more about...”). Often, this probingfinds that respondents speak the language of risk without really under-standing its terms. For example, our first study concerned radon. Mostrespondents knew that it was a colorless, odorless, radioactive gas thatcaused lung cancer. However, when pressed, they typically revealed in-appropriate notions of radioactivity. Specifically, people often believedthat anything radioactive would permanently contaminate their homes.Indeed, some told us that they would not test for radon, because therewas nothing that they could do if they did find a problem (Bostrom etal., 1992). In studies with adolescents regarding the risks of HIV/AIDS,we have found other forms of false fluency, in the sense of teens usingterms, such as “safe sex” and “clean needles,” without fully understand-ing them. It is not hard to imagine how such unwitting ignorance cancreate problems (Fischhoff, Downs, & Bruine de Bruin, 1998).

Once these spontaneous expressions have been exhausted, the inter-view becomes more directive. In order to ensure that significant topicshave not been neglected, as a result of following the interviewee’s trainof thought, the discussion is guided gently around the major areas ofthe model. Pertinent questions might be “Is there anything that you cando to prevent the risk?,” “How exactly does it hurt you?,” “How can youlimit the damage?,” and “Are some people particularly vulnerable?”Each of these is a generic aspect of risky processes, the mention of whichshould not unduly put ideas in participants’ heads. At times, we haveconcluded the interview with more directive questioning, asking inter-viewees to discuss the relevance of a list of factors, chosen to vary intheir relevance. These prompts provide another stimulus for revealinglay theories. For example, the final section of our radon protocol hadrespondents sort a set of photos according to relevance, explaining eachchoice. One photo depicted the produce section of a local supermarket.Although we had intended it as an obviously irrelevant filler (includedso that interviewees would not feel compelled to find some way to makeevery photo relevant), it prompted some people to discuss the effect ofradon on houseplants. This seemed like one reflection of a moderatelycommon belief that sickly plants would give warning of bad air (perhapslike the canaries in a mine). This belief could, obviously, create a false



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sense of confidence or concern (depending on the quality of one’s indoorgardening).

Steps 4 and 5: Structured Interviews. The results of these open-ended interviews are used to create a structured questionnaire, coveringthe critical topics (as defined by the formal analysis) and the criticalmisconceptions (as identified in the interviews). This questionnaire pro-vides both a more efficient method of data collection and an opportunityto cross-validate the beliefs elicited in the open-ended interviews. Be-cause the questions can be identified with portions of the expert model,one can create subtests for separate areas of knowledge. These subtests(or even individual questions) could be weighted to reflect the impor-tance of that knowledge in understanding the risk as a whole. Suchstrategies offer an alternative to the common practice of producing testscores by equally weighting answers to questions often chosen withoutan obvious plan from an imprecisely defined domain.

Steps 6 and 7: Communication. The final stage is to create commu-nications focused on the same critical information—things that peopleneed to know but do not. In structuring the critical information, we usewhat we can of developments in document design (Schriver, 1989). How-ever, there is inevitably some element of opinion in the process. Forexample, we typically try to start with the big picture regarding a risk,showing how the pieces fit together. This strategy seems especially help-ful when people’s knowledge is quite fragmentary. By contrast, our teeninterviewees seemed awash in information about HIV/AIDS, so muchso that they felt on top of the topic. Nonetheless, there were a few sig-nificant holes in their knowledge (such as the misunderstanding ofterms). As a result, that communication focuses on “the few last thingsthat you need to know about HIV/AIDS.” It is currently in testing; how-ever, at least anecdotally, teens seem to like it—or at least do not dis-miss it out of hand, as more of the same on a topic raised often in schooland out.2

Worked Examples. We have followed (some or all of) this process witha variety of risks, including electromagnetic fields, climate change, nu-clear energy sources in space, Lyme disease, mammography, and sex-ually transmitted diseases other than HIV/AIDS. Each application hassome element of repetition, in the sense of relying on previously ac-quired experiences. However, each case also raises new problems, re-

2Because the same perspective guides creation of both the communication and the evaluation ques-tionnaire, the former trains for the criterion defined by the latter. This complicates comparisonswith communications derived from other theoretical perspectives. In those relatively rare caseswhere other communications have some evaluation procedure, we would typically not want tobe bound to it, as a result of rejecting its concept of what is worth knowing.



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garding the characterization of expert knowledge, the design of the in-terview protocol, the analysis of responses, or the design and evaluationof communications. For example, in the work on adolescent sexuality,we have had to create formal models summarizing complex behavioralpatterns and to reduce complex processes to language suited to a low-literacy audience (Fischhoff et al., in press). For the work on electro-magnetic fields, we needed to present the unintuitive ways in whichfields fall off with distance and are affected (or unaffected) by variousforms of shielding. We also needed a way to deal with deep splits in thescientific community. To that end, one strategy was to present distinctviews, each of which had been vetted by its advocates (“yes, that’s whatI believe, although I wish that you wouldn’t present the other side aswell”). A second was offering strategies for “prudent avoidance,” low-cost ways to reduce field exposures, even if one doubted that there wereany associated health effects (e.g., sleeping with one’s head away fromthe wall, in order to distance oneself from the electrical wires embeddedin it) (Morgan, 1989).

OPPORTUNITIES FOR RISK REDUCTION

In this country, there is currently a vigorous discussion of the possibil-ities for regulatory reform. Although some protagonists simply hope torepeal onerous regulations, others are looking for more cooperative andflexible ways to manage risks. In the jargon (Fischhoff, 1984), they hopeto replace technical standards, specifying how hazardous technologiesshould be managed, with performance standards, specifying what safetylevels must be achieved, while allowing the risk managers (in govern-ment or industry) to seek the most efficient way of doing so. These al-ternatives figure prominently in Vice-President Gore’s (1993) attemptsto reinvent government, as well as regulatory reform proposals in the104th and 105th Congresses.

One obvious option for risk reduction is changes in user/operator be-havior for hazardous technologies. Following Three Mile Island, therewere concerted efforts not only to reduce operator-attributed error, butalso to incorporate human factors in the probabilistic risk analyses usedto estimate system safety. Those analyses helped to identify the rolesplayed by operators (and, to a lesser extent, maintenance personnel,administrators, and designers) in determining the relative safety of al-ternative designs. However, the absolute risk estimates associated withsuch analyses have been viewed skeptically by those who doubted thepossibility of estimating the fallibility of such diverse and complex cog-nitive, social, and psychomotor tasks. If one cannot scale up from simplehuman factors analyses to plant-level analyses, then demonstratingcompliance with performance standards is problematic.

A more realistic goal might be estimating the risk-reduction oppor-



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tunities with simple consumer products, considering the role that be-havior plays in them and the realistic chances for shaping that behaviorby warning users. Recently, we have applied our mental-models meth-odology to this objective with two chlorinated solvents, each designatedas a probable carcinogen by the International Agency for Research onCancer (IARC), each without adequate substitutes (at least for the im-mediate future). One is methylene chloride, used as paint stripper. Theother is perchloroethylene, used as dry-cleaning fluid. We briefly de-scribe these two projects in turn. Because the work is still in progress,they are best viewed as illustrations of the general approach and thekinds of recommendations that it can produce, regarding the contentand implementation of warning campaigns.

Methylene Chloride, Used as Paint Stripper

The Problem. Many people undertake home-improvement projects, us-ing volatile organic compounds (VOCs) that are worrisome enough tobe a focus of workplace regulation. Characterizing the risks of home-improvement projects requires an understanding of the time-activityand product-use patterns of those in the home, as well as quantitativeexposure modeling based on those estimates. With such a model, onecan prioritize actions according to their impact on risk—and, hence, forcommunication (as in the carotid endarterechtomy study). One can eval-uate the adequacy of do-it-yourselfers’ understanding of the process (asin the mental-models studies). First, we consider the magnitudes ofthese risks.

Many studies of personal exposure to VOCs have looked at buildingmaterials, such as treated wood (Jayjock, Doshi, Nungasser, & Shade,1995) and carpeting (Sollinger, Levsen, & Wunsch, 1993; Weschler,Hodgson, & Wooley, 1992); home-improvement activities, such as woodfinishing (Chang & Guo, 1992); and everyday tasks, such as showeringand washing clothes, using a water supply with elevated VOC concen-trations (Wilkes and co-workers, 1992, 1996). Wallace et al. (1989) ex-amined indoor air concentrations for 20 common household compounds,finding that some were at levels associated with acute reactions in peo-ple who have sick building syndrome. For example, paint and paintremover use have been found to elevate 8-hour averages of some aro-matic and aliphatic compounds by two orders of magnitude. Thompsonand Thompson (1990) considered personal exposures resulting from theuse of arts-and-crafts materials, such as oil paints, degreasers, rubbercement, and paint stripper. Levels of xylene from both marking-pen inkand methylene chloride from high-tack adhesive were found to producea significant potential for high short-term exposure.

As in other areas, one line of previous studies has estimated chemicalexposures from home-improvement projects, such as paint stripping(Girman & Hodgson, 1986; Girman, Hodgson, & Wind, 1987; Hodgson



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& Girman, 1987), whereas another line has considered the effectivenessof risk communication materials in terms of information retention andbehavioral changes in product use (Pollack-Nelson, 1995). We are tryingto integrate these two approaches, so that information relevant to riskcommunication can inform the exposure analysis, which, in turn, canprovide guidance for improving risk communication.

The Formal Model. Our model combines indoor air-quality modelingwith physiologically-based pharmacokinetic (PBPK) modeling, whichtracks chemical movement, accumulation, and removal processes withinthe human body, and considers dermal as well as inhalation exposurepathways (Dankovic & Bailer, 1994).3 It draws upon van Veen’s (1996)general model for exposure and uptake from consumer products throughmultiple exposure pathways. This model is especially suited for complexexposure patterns because it incorporates both a contact function andspatial differences in concentration. It also includes generalized uptakefunctions that utilize PBPK modeling and can be customized for specificchemicals. Our model also reflects the Girman and Hodgson (1986) find-ing that breathing-zone concentrations of methylene chloride werehigher than the chamber concentrations predicted by their model, at thetime that workers were applying the product or scraping it off. As aresult, our model divides the work area into two compartments, in orderto account for intimate exposure. It assumes a small virtual chamber(4.3 m3) around the work site, within a larger room (25 m3), with airreplacement rates of 12 changes/h, in the smaller (virtual) room and 0.5changes/h for the overall room.

Figure 3 illustrates the model, with a typical scenario in which a userapplies paint stripper to a quarter of the surface, takes a break whilethe stripper cures, and returns both to scrape the paint off the firstquarter and to add paint stripper to the second quarter. It shows themuch greater uptake for a user who stays in the immediate area whilethe paint stripper is curing (solid line) than for a user who takes breaksin a space where the concentration is effectively zero (e.g., outside)(dashed line). An additional analysis showed that exposure can be re-duced further if the user scrapes the cured section first, then appliesstripper to the next section (rather than the other way around).

Figure 4 shows the combined effects on uptake of two variations inworking conditions: how well ventilated the room is (closed windows,open windows, or open windows plus fan) and where breaks are taken

3This model can be generalized for use with other chemicals and can be expanded to include anoral route of exposure and uptake. Expanding the paint-stripping example to consider the largerproject of furniture refinishing demonstrates the potential complexity of home improvement proj-ects: Refinishing may involve multiple chemical exposures to products including paint, varnish,paint thinner, trisodium phosphate, mineral spirits, and wood stain. Multiple tasks would alsobe considered, including paint stripping, cleaning, sanding (a potential source of particulateexposure), staining, painting, and varnishing.



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Figure 3 Uptake patterns for paint-stripper users leaving/not leaving the area whilestripper is curing.

Figure 4 Methylene chloride uptake for users of paint stripper as a function of ven-tilation and break patterns.



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while the stripper is curing (in the work area, in a corner of the room,or outside). It suggests that someone working continuously in a properlyventilated room has much less (respiratory) uptake than someone whotakes regular breaks outside a closed room. This seems to be a best buyin risk reduction, in the sense of a work practice that provides the great-est protection at the least cost (in added expense, job interruption, etc.).If that is, in fact, the case (which would ideally be confirmed with directexposure measurement), then there is one critical message to convey toconsumers: Ventilate the room properly. If that message could getthrough, then consumer exposures could be significantly reduced, sup-porting the feasibility of this kind of voluntary regulatory reform.

The Mental Models. For that to happen, several (obvious) behavioralconditions must be met: (a) there must be enough of a perceived threatfor consumers to attend to warnings, (b) the warnings must make sensein terms of consumers’ mental models of the risky processes involved,and (c) it must be possible to execute the recommendations faithfully,while still getting the job done. In order to assess the general likelihoodof this happening, we conducted mental-models interviewers with 20individuals, recruited at a home improvement center in a Pittsburghsuburb, who said that they had used paint stripper before. Participantswere asked questions about their use of paint stripper, both in quali-tative and quantitative terms (as needed for inputs to a formal model).They were also asked for any concerns about paint stripper and anyactions that had been prompted by those concerns.

Their responses suggested some reasons for optimism regarding acommunication focused on ventilation. There was a high level of aware-ness regarding possible risks of paint stripper. The potential health ef-fects that participants described were, in fact, as serious as those de-scribed in the literature as occurring at much higher exposure levelsthan the typical home project. All 20 subjects reporting taking someprecautions. Moreover, their specific precautions suggested some intu-itive feel for the nature of the risks and the steps needed for their con-trol. All but two reported taking breaks, about half of whom waited longenough to enjoy the benefits shown in Figure 3, only a few of whomstayed in the work area. Half the sample mentioned improving venti-lation. Three quarters reported using gloves while they worked, eventhough most commercially available gloves have breakdown rates of lessthan 20 minutes with this potent solvent (Vahdat, 1987). All 20 inter-viewees said that they read the label on the can. When asked what theywanted a label to tell them, 11 mentioned directions for safe use, 9 ofwhom wanted to know the rationale for the precautionary steps.

The Programmatic Implications. These individuals reported wor-rying about paint stripper, doing some reading, and recognizing theneed for ventilation. Thus, there should be a reasonable chance of their



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seeking and heeding a warning to ventilate, apparently the single mosteffective thing that they could do [Figure 3(b)]. One focus of future in-terviews will be the details of consumers’ beliefs about ventilation strat-egies (e.g., patterns of opening windows and fan placement). Given thecomplexity of air dynamics, it may take some effort to devise compre-hensible heuristics for achieving the potential of the ostensibly simpleventilation strategy. A second focus will be the beliefs leading to the useof gloves, seemingly a needless expense—given the small dermal up-take and the limited effectiveness of available gloves. A third will bebeliefs about goggles, which few users mentioned wearing, even thougheye injury is the most frequent reason for emergency-room admittancerelated to home paint-stripper use (R. Brown, Dow Chemical Company,personal communication, February 1996). Our study suggests that con-sumers are close enough to understanding the problem (and sufficientlyinterested in doing so) that a simple, well-designed warning could sub-stantially reduce exposure, at a small price in task performance (seealso Pollack-Nelson, 1995).

Perchloroethylene, Used as Dry Cleaning Fluid

The Problem. Perchloroethylene (PCE) is used by over 85% of drycleaners in the US. It is classified as a hazardous substance and a prob-able human carcinogen by EPA. It has recently come under increasedscrutiny, especially regarding possible health effects on dry cleaningworkers (International Agency for Research on Cancer [IARC], 1995),individuals living above dry cleaners in New York City (Wallace, Groth,Kirrane, Warren, & Halloran, 1995) and even the wearers of dry-cleanedclothes (Wallace & Groth, 1996). Although alternatives to PCE are beingdeveloped, some have their own risks (e.g., flammability) and none arecurrently ready to assume its place in the market (Black, 1995; EPA,1993). Publications such as Consumer Reports and the New York Timeshave featured the problem, suggesting consumers’ need and desire forwarnings about risks in the clothes they wear.

The Formal Model. Studies have found that emission rates of PCE arelow (Kawauchi & Nishiyama, 1989; Moscandreas & O’Dea, 1995;Thomas, Pellizzari, Perritt, & Nelson, 1991; Tichenor et al., 1990) Asa result, the risks to consumers from dry cleaning are likely to bevery small, except for a worst-case scenario: picking up a significantquantity of improperly dried clothing, immediately after it has beencleaned, then hanging it in a tightly sealed room, where one spends agood deal of time. (Wearing such clothing could also create exposures,but seems too unpleasant to contemplate.) Industrial practices, regu-latory enforcement, and commercial pressures keep residues from beingvery large very often. Even if they were, volatilization rates are suffi-ciently fast for residues to disappear fairly quickly. The period that most



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people leave their clothes at the cleaner (perhaps hoping that their sig-nificant other will pick them up and pay the tab) should reduce the risksto a negligible level. The time that they spend at the dry cleaning plantis typically minimal.

The Mental Models. The fact that risks are small does not, of course,mean that they will seem so. We conducted interviews with a diversegroup of 30 consumers. They showed a wide variation in knowledgelevels. A small number had a good understanding of the dry-cleaningprocess, but most had at best a fragmentary understanding, often cou-pled with incorrect beliefs. A common mental model is that dry cleaninginvolves a spraying or steaming processes, perhaps using chemicals.4Only a small fraction of these consumers had heard about any specificconcerns, or had considered altering their dry-cleaning habits. However,once the issue was raised, several previously unconcerned subjectsmade the inference that there might be a problem, simply because achemical is used. When asked who should be most concerned about thepossible impacts of dry cleaning, subjects typically (and appropriately)identified employees and sensitive individuals, such as children, the el-derly, or those with allergies. They mentioned less frequently those whowear dry-cleaned clothes often and those who live or work near dry-cleaning stores. Few expressed any interest in reading about risks, say-ing that they would ask their dry cleaner if they had any concerns.

The Programmatic Implications. There seems to be little interest indry-cleaning warning labels, or need for them—in terms of likely overallrisks or remedial actions worth communicating. Thus, there seems tobe little value to an investment in warnings. Should interest in the risksarise, there is the somewhat unusual situation in which consumers haveeasy access to (reasonably) trusted professionals whom they would askdirectly to explain these issues. If dry cleaners can fulfill this role, theymight help consumers achieve their personal comfort level with theserisks. Doing so might also motivate good industrial practices: By andlarge, actions that dry cleaners can take to protect consumers wouldalso protect workers.

In order to anticipate the success of such interactions, we interviewed20 dry cleaners regarding their own mental models. Most of these drycleaners thought that concerns about PCE risks were either totally in-appropriate or only rarely relevant (e.g., irresponsible cleaners, improb-able accidents). The reasons for this confidence were, however, not al-ways sound. The most frequent explanation was anecdotal evidence ofnot seeing any deaths or illness due to PCE exposure, among familymembers or others in the dry-cleaning industry. Although this argu-

4It actually involves something like a regular washing machine, using a viscous liquid (PCE) witha low boiling point and little absorption by fabrics.



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ment has a certain logic to it, and might have some appeal to somecustomers, it ignores the difficulty of establishing causal relationshipsfor health effects. As a result, it might alienate customers who realizethe inconclusiveness of such anecdotal evidence.

Many of the dry cleaners also had an incomplete understanding ofthe fate and transport processes that can produce exposures. For ex-ample, many stated that advances in dry-cleaning technology have al-lowed them to reduce significantly the amount of PCE that they used.While compelling as a statement of improvement, this argument failsto acknowledge that a significant part of this savings comes from recov-ering vapors previously vented outside the store. This does not neces-sarily translate to reduced fugitive emissions within the store or to re-duced clothing residuals—which are the result of incomplete drying.Some cleaners argued further that consumer exposures from PCE re-sidual in clothes are impossible because, even if some residual existsinitially, it quickly evaporates. This statement neglects the fact thatPCE vapor is heavier than air and will not simply disappear as it evap-orates. Instead, in areas with little ventilation (such as the home) it willtend to accumulate.

Moreover, a small loss of liquid PCE can result in relatively highvapor concentrations, when diluted in a small volume of air (e.g., insidethe store, on clothing). Some cleaners argued further that PCE expo-sures are impossible because hazardous waste shipments nearly equalthe amounts of virgin PCE that they purchase. However, the wasteshipped from dry cleaners is not pure PCE (otherwise it could still beused), but contains “contaminants” such as water.

Thus, although the dry cleaners have a favored position as commu-nicators, their incomplete knowledge might confuse or alienate consum-ers who sought their counsel. Matters might get worse if cleaners ex-pressed a dismissive attitude toward anyone who expressed concerns.In this situation, rather than warning consumers, it might be better toeducate dry cleaners. If they handle queries clumsily, they might createthe perception of a problem where none need exist (and, hence, mightimperil their livelihood) (Fischhoff, 1995b; Fischhoff & Merz, 1994).Having an acceptable response means having good operating practicesto report, such as using modern equipment and allowing a full dryingcycle.

THE ACCEPTABLE LEVEL OF MISUNDERSTANDING

Some professionals may have relatively long periods of time to help cli-ents understand the risks and benefits of some focal actions. However,as physicians and genetic counselors can attest, that is often a dauntingchallenge, even under favorable conditions. It is often hard to convey



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either element of risk, the severity or the likelihood of the possible con-sequences.

How much greater are the challenges to those whose warnings arerestricted to a fragment of a label. The behavioral decision theory ap-proach presented here attempts to simplify this task by focusing it onthe information that matters most to recipients. It does that with acombination of analytical and empirical procedures, assessing whatneeds to be known in principle (considering the nature of the decision)and what needs to be communicated in practice (considering what isknown already). Although these methods were not designed with warn-ing labels in mind, those tasks share common elements with other de-cisions involving risk. Pursuing these elements might be useful for bothfields.

The decision-making perspective also provides criteria for evaluatingthe adequacy of communications. Namely, do they allow people to makethe choices that they would if they understood the decision fully? Thiscriterion can put an awkward light on communications that cannot workfor all recipients.

This is the challenge facing the Food and Drug Administration, as itattempts to develop standardized labels for over-the-counter drugs,partly motivated by the recent movement of several prominent prescrip-tion drugs to over-the-counter status (Fischhoff, 1995a; Wogalter, 1995).Communications once left to imperfect patient–physician interactionsare now left to imperfect patient–label interactions (perhaps with thebackup of a pharmacist or an 800 number). Some imperfect understand-ing is inevitable. At times, this will lead to consumers needlessly for-going the benefits of a product whose risks are overestimated or whosebenefits are underestimated. At times, it will mean accepting risks thatare not justified by the associated benefits.

In such situations, any warning label reflects a policy regarding theacceptability of the associated level of misunderstanding—and of theunacceptability of the large risks or small benefits that the action pro-duces. Some people will not be getting what they expect or what theycould from those products. At times, those people will be just randomindividuals, whose idiosyncratic misunderstandings lead them to makethe wrong choice. At times, they will be readily identifiable, in the senseof belonging to groups expected to have higher levels of misunderstand-ing. The welfare of different groups may even be pitted against oneanother, unless there are ways to target them separately. For example,other things being equal, using two languages will provide greater pro-tection to those not literate in English (in this country), and less pro-tection to those with limited visual acuity (who must contend with thesmaller type). Thus, protecting some Spanish-speaking consumersmight mean exposing some older ones. If men and women differ in theirvalues, behavior patterns, and preconceptions, then there might be dif-



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ferent priorities for warnings directed at each gender. That is, whenlabel space and consumer attention are limited, disabusing men of theirkey confusions might leave women less well protected, and vice versa.

These issues are, of course, well known to those who regularly con-tend with labeling issues. What modeling can do is to cast these choicesinto sharper relief (Fischhoff, 1998). That can be an uncomfortable pro-cess. It would force the courts or legislators or designers to admit thatlabeling practices embody social philosophies, making trade-offs be-tween different people’s lives or between risks and benefits. They mightprefer to leave those philosophies unstated, rather than assume respon-sibility for them. However, even if these risk managers do not run thenumbers, or reveal those that they do run, there is no way to stop othersfrom doing so—and revealing the implicit social philosophies of stan-dard practices.

The debates over tort and regulatory reform are full of numbers, fo-cused on the economic or casualty burdens of (current and proposed)litigation and regulation practices (e.g., Breyer, 1993; Viscusi & Zeck-hauser, 1996). Those numbers have widely varying (and disputed) qual-ity, even within the disciplines that produce them. Few are even lightlyinformed by any research-based understanding of human behavior.Some partisans might just as soon not be bound by such research, pre-ferring to rely on their political weight, backed by some tortured statis-tics. However, to the extent that facts matter, those who study people’sresponses to hazards might constrain the debate and increase thechances of behaviorally realistic strategies being adopted. Formal mod-els provide a way to introduce diverse behavioral results into policyarenas (Kaplan & Brandeau, 1994).

This article has offered some general methods to this end, illustratedby a few specific results, contributing to the great lode of research rep-resented in the other articles in this Special Issue. Our perspective as-sumes reasonable behavior on the part of consumers, in terms of howthey determine and act on their beliefs about products. No one expectsthat to be a universally valid assumption. Moreover, the producers ofproducts are not expected to protect people against themselves, onlyagainst reasonable uses of products. Good research might expand theenvelope of such behavior, by helping people to understand more aboutproducts and to feel competent to do so—by raising their expectationthat warnings will be worth investigating. It can also provide betterestimates of the bounds of reasonable behavior. In hindsight, it is ofteneasy to hold both producers and consumers responsible for accidents,insofar as it now looks relatively obvious how things could have gonewrong (Fischhoff, 1975, 1977). One protection against such case-basedreasoning is systematic study of what things many people do and do notmanage to figure out on their own—when they even realize that thereis a hazard worth thinking and learning about.



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REFERENCES

Black, H. (1995). Prototype CO2 dry-cleaning process replaces toxic solventsource. Environmental Science and Technology, 29, 497A.

Bostrom, A., Fischhoff, B., & Morgan, G.M. (1992). Characterizing mental mod-els of hazardous processes: A methodology with an application to radon. Jour-nal of Social Issues, 48, 85–100.

Breyer, S. (1993). Breaking the vicious circle. Cambridge, MA: Harvard Uni-versity Press.

Burns, W. J., & Clemen, R. T. (1993). Covariance structure models and influ-ence diagrams. Management Science, 39, 816–834.

Chang, J. C. S., & Guo, Z. (1992). Modeling of the fast organic emissions froma wood-finishing product—floor wax. Atmospheric Environment, 26A, 2365–2370.

Dankovic, D. A., & Bailer, J. (1994). The impact of exercise and intersubjectvariability on dose estimates for dichloromethane derived from a physiolog-ically based pharmacokinetic model. Fundamental and Applied Toxicology,22, 20–25.

Davies, J. C. (Ed.) (1996). Comparing environmental risks. Washington, DC:Resources for the Future.

Edworthy, J., & Adams, A. (1996). Warning design: A research prospective. Lon-don: Taylor and Francis.

EPA. (1993). Multiprocess wet cleaning: Cost and performance comparison ofconventional dry cleaning and an alternative process. Washington, DC: U. S.EPA Office of Pollution Prevention and Toxics.

Fischhoff, B. (1975). Hindsight Þ foresight: The effect of outcome knowledgeon judgment under uncertainty. Journal of Experimental Psychology: HumanPerception and Performance, 104, 288–299.

Fischhoff, B. (1977). Cognitive liabilities and product liability. Journal of Prod-ucts Liability, 1, 207–220.

Fischhoff, B. (1984). Setting standards: A systematic approach to managingpublic health and safety risks. Management Science, 30, 823–843.

Fischhoff, B. (1994). What forecasts (seem to) mean. International Journal ofForecasting, 10, 387–403.

Fischhoff, B. (1995a). Conditions for a successful labeling program. Testimonyat FDA hearing on over-the-counter drug labeling (Sept. 29). Rockville, MD.

Fischhoff, B. (1995b). Risk perception and communication unplugged: Twentyyears of process. Risk Analysis, 15, 137–145.

Fischhoff, B. (1998). Communicate unto others... Reliability Engineering andSystem Safety, 59, 63–72.

Fischhoff, B., Bostrom, A., & Quadrel, M. J. (1997). Risk perception and com-munication. In R. Details, J. McEwen, & G. Omenn (Eds.), Oxford textbookof public health (pp. 987–1002). London: Oxford University Press.

Fischhoff, B., & Downs, J. (1997). Accentuate the relevant. Psychological Sci-ence, 8, 1–5.

Fischhoff, B., Downs, J., & Bruine de Bruin, W. (1998). Adolescent vulnerabil-ity. Applied and Preventive Psychology, 7, 77–94.

Fischhoff, B., & MacGregor, D. (1983). Judged lethality: How much people seemto know depends upon how they are asked. Risk Analysis, 3, 229–236.



shortstandard


Base of RF

Fischhoff, B., & Merz, J. F. (1994). The inconvenient public: Behavioral re-search approaches to reducing product liability risks. In J. R. Hunker & T.O. Jones (Eds.), Product liability and innovation (pp. 159–189). Washington,DC: National Academy Press.

Fischhoff, B., Watson, S., & Hope, C. (1984). Defining risk. Policy Sciences, 17,123–139.

Girman, J. R., & Hodgson, A. T. (1986). Source characterization and personalexposure to methylene chloride from consumer products. Prepared for the 79thAnnual Meeting of the Air Pollution Control Association.

Girman, J. R., Hodgson, A. T., & Wind, M. L. (1987). Considerations in evalu-ating emissions from consumer products. Atmospheric Environment, 21,315–320.

Gore, A. (1993). Reinventing government. Washington, DC: Government Pub-lishing Office.

Hasher, L., & Zacks, R. T. (1984). Automatic and effortful processes in memory.Journal of Experiment Psychology: General, 108, 356–388.

Hodgson, A. T., & Girman, J. R. (1987). Exposure to methylene chloride fromcontrolled use of a paint remover in residences. Presented at the 80th AnnualMeeting of the Air Pollution Control Association.

Howard, R. A. (1989). Knowledge maps. Management Science, 35, 903–922.International Agency for Research on Cancer (IARC). (1995). Dry cleaning,

some chlorinated solvents and other industrial chemicals. Monographs on theevaluation of carcinogenic risks to humans, 63. Geneva: World Health Or-ganization.

Jayjock, M. A., Doshi, D. R., Nungasser, E. H., & Shade. W. D. (1995). Devel-opment and evaluation of a source/sink model of indoor air concentrationsfrom isothiazolone treated wood used indoors. American Industrial HygieneAssociation Journal, 56, 546–557.

Kahneman, D., & Tversky, A. (1984). Choices, values, and frames. AmericanPsychologist, 39, 341–350.

Kaplan, E., & Brandeau, M. (Eds.) (1994). Modeling the AIDS epidemic. NewYork: Raven Press.

Kawauchi, T., & Nishiyama, K. (1989). Residual tetrachloroethylene in dry-cleaned clothes. Environmental Research, 48, 296–301.

Kearney, A. R., & Kaplan, S. (in press). Toward a methodology for the mea-surement of the knowledge structures of ordinary people. Environment &Behavior.

Laughery, K. R., Wogalter, M. S., & Young, S. L. (Eds.) (1995). Human factorsperspectives on warnings. Santa Monica, CA: Human Factors and Ergon-omics Society.

Lichtenstein, S., Slovic, P., Fischhoff, B., Layman, M., & Combs, B. (1978).Judged frequency of lethal events. Journal of Experimental Psychology: Hu-man Learning and Memory, 4, 551–578.

Linville, P. W., Fischer, G. W., & Fischhoff, B. (1993). AIDS risk perceptionsand decision biases. In J. B. Pryor & G. D. Reeder (Eds.), The social psy-chology of HIV infection (pp. 5–38). Hillsdale, NJ: Erlbaum.

Luker, K. (1975). Taking chances: Abortion and the decision not to contracept.Berkeley: University of California Press.



shortstandard


Base of RF

McCarthy, R. L., Ayres, T. J., & Wood, C. T. (1995). Risk and effectivenesscriteria for using on-product warnings. Ergonomics, 38, 2164–2175.

Merz, J., Fischhoff, B., Mazur, D. J., & Fischbeck, P. S. (1993). Decision-ana-lytic approach to developing standards of disclosure for medical informedconsent. Journal of Toxics and Liability, 15, 191–215.

Morgan, M. G. (1989). Electric and magnetic fields from 60 Hz electric power:What do we know about possible health risks. Pittsburgh: Carnegie MellonUniversity, Department of Engineering and Public Policy.

Morgan, M. G., Fischhoff, B., Bostrom, A., Lave, L., & Atman, C. J. (1992).Communicating risk to the public. Environmental Science and Technology,26, 2048–2056.

Morgan, M. G., & Henrion, M. (1990). Uncertainty. New York: Cambridge Uni-versity Press.

Moscandreas, D. J., & O’Dea, D. S. (1995). Measurement of perchloroethyleneindoor air levels caused by fugitive emissions from unvented dry-to-dry drycleaning units. Journal of Air and Waste Management Association, 45, 111–115.

National Research Council. (1996). Understanding risk: Informing decisions ina democratic society. Washington, DC: Author.

Papastavrou, J., & Lehto, M. (1996). Improving the effectiveness of warningsby increasing the appropriateness of their information content: Some hy-potheses about human compliance. Safety Science, 21, 175–189.

Plous, S. (1993). The psychology of judgment and decision making. New York:McGraw-Hill.

Pollack-Nelson, C. (1995). Analysis of methylene chloride product labeling. Er-gonomics, 38, 2176–2187.

Poulton, E. C., (1989). Bias in quantifying judgment. Hillsdale, NJ: LawrenceErlbaum Associates.

Quadrel, M. J. (1990). Adolescent risk perceptions: Quantitative and qualitativedimensions. Unpublished dissertation, Carnegie Mellon University, Pitts-burgh.

Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: Prospectsand limits in the search for mental models. Psychological Bulletin, 100, 349–363.

Schriver, K. A. (1989). Evaluating text quality: The continuum from text-fo-cused to reader-focused methods. IEEE Transactions on Professional Com-munication, PC-32, 238–255.

Shaklee, H., & Fischhoff, B. (1990). The psychology of contraceptive surprises:Judging the cumulative risk of contraceptive failure. Journal of Applied Psy-chology, 20, 385–403.

Sollinger, S., Levsen, K., & Wunsch, G. (1993). Indoor air pollution by organicemissions from textile floor coverings: Climate chamber studies under dy-namic conditions, Atmospheric Environment, 27B, 183–192.

Tichenor, B. A., Sparks, L. E., Jackson, M. D., Guo, Z., Mason, M. A., Plunkey,C. M., & Rasor, S. A. (1990). Emissions of perchloroethylene from dry cleanedfabrics. Atmospheric Environment, 24A, 1219–1229.

Thomas, K. W., Pellizzari, E. D., Perritt, R. L., & Nelson, W. C. (1991). Effectof dry-cleaned clothes on tetrachloroethylene levels in indoor air, personal



shortstandard


Base of RF

air, and breath for residents of several New Jersey homes. Journal of Ex-posure Analysis and Environmental Epidemiology, 1, 475–490.

Thompson, F. M. & Thompson, P. G. (1990). Arts and crafts. In L. V. Cralley,L. J. Cralley, & W. D. Cooper (Eds.), Health and safety beyond the workplace(pp. 9–32). New York: John Wiley & Sons.

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging fre-quency and probability. Cognitive Psychology, 4, 207–232.

Vahdat, N. (1987). Permeation of protective clothing materials by methylenechloride and perchloroethylene. American Industrial Hygiene AssociationJournal, 48, 646–651.

van Veen, M. P. (1996). A general model for exposure and uptake from con-sumer products. Risk Analysis, 16, 331–338.

Viscusi, W. K., & Zeckhauser, R. (1996). Hazard communication: Warning andrisk. Annals of the American Academy of Political and Social Science, 545,106–115.

von Winterfeldt, D., & Edwards, W. (1986). Decision analysis and behavioralresearch. New York: Cambridge University Press.

Wallace, L. A., Pellizzari, E. D., Hartwell, T. D., Davis, V., Michael, L. C. &Whitmore, R. W. (1989). The influence of personal activities on exposure tovolatile organic compounds. Environmental Research, 50, 37–59.

Wallace, D., Groth, E., Kirrane, E., Warren, B., & Halloran, J. (1995). Upstairs,downstairs: Perchloroethylene in the air in apartments above New York Citydry cleaners, Consumers Union: Yonkers, NY.

Wallace, D., & Groth, E. (1996). Perchloroethylene exposure from dry-cleanedclothes. Consumers Union: Yonkers, NY.

Weschler, C. J., Hodgson, A. T., & Wooley, J. D. (1992). Indoor chemistry:Ozone, volatile organic compounds, and carpets. Environmental Science andTechnology, 26, 2371–2377.

Wilkes, C. R., Small, M. J., Andelman, J. B. Giardino, N. J., & Marshall, J.(1992). Inhalation exposure model for volatile chemicals from indoor uses ofwater. Atmospheric Environment, 26A, 2227–2236.

Wilkes, C. R., Small, M. J., Davidson, C. I. & Andelman, J. B. (1996). Modelingthe effects of water usage and cobehavior on inhalation exposures to contam-inants volatilized from household water. Journal of Exposure Analysis andEnvironmental Epidemiology, 6, 393–412.

Wogalter, M. (1995). Testimony at FDA hearing on over-the-counter drug la-beling (Sept. 29). Rockville, MD.

Yates, J. F., (1990). Judgment and decision making. New York: John Wiley &Sons.

Support for the preparation of this manuscript and the research reported herecame from the National Science Foundation, the Environmental ProtectionAgency, the Center for Emission Controls, the National Institute of AlcoholAbuse and Alcoholism, and the National Institute of Allergies and InfectiousDisease. The contributions of the reviewers and editors are gratefully appre-ciated. The views expressed are those of the authors.

Correspondence regarding this article should be sent to: Baruch Fischhoff, De-partment of Engineering and Public Policy, Carnegie Mellon University, Pitts-burgh, PA 15213-3890 ([email protected]).

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