Improving Communication of Uncertainty in the Reports of the Intergovernmental Panel on Climate Change

The effect of likelihood and impact information on public response to severe weather warnings

Meteorological services are increasingly moving away from issuing weather warnings based on the exceedance of meteorological thresholds (e.g., windspeed), toward risk-based (or "impact-based") approaches. The UK Met Office's National Severe Weather Warning Service has been a pioneer of this approach, issuing yellow, amber, and red warnings based on an integrated evaluation of information about the likelihood of occurrence and potential impact severity. However, although this approach is inherently probabilistic, probabilistic information does not currently accompany public weather warning communications. In this study, we explored whether providing information about the likelihood and impact severity of forecast weather affected subjective judgments of likelihood, severity, concern, trust in forecast, and intention to take protective action. In a mixed-factorial online experiment, 550 UK residents from 2 regions with different weather profiles were randomly assigned to 1 of 3 Warning Format conditions (Color-only, Text, Risk Matrix) and presented with 3 warnings: high-probability/moderate-impact (amber HPMI); low-probability/high-impact (amber); high-probability/high-impact (red). Amongst those presented with information about probability and impact severity, red high-likelihood/high-impact warnings elicited the strongest ratings on all dependent variables, followed by amber HPMI warnings. Amber low-likelihood/high-impact warnings elicited the lowest perceived likelihood, severity, concern, trust, and intention to take protective responses. Taken together, this indicates that UK residents are sensitive to probabilistic information for amber warnings, and that communicating that severe events are unlikely to occur reduces perceived risk, trust in the warning, and behavioral intention, even though potential impacts could be severe. We discuss the practical implications of this for weather warning communication.

Communicating uncertainty in national security intelligence: Expert and nonexpert interpretations of and preferences for verbal and numeric formats

Organizations in several domains including national security intelligence communicate judgments under uncertainty using verbal probabilities (e.g., likely) instead of numeric probabilities (e.g., 75% chance), despite research indicating that the former have variable meanings across individuals. In the intelligence domain, uncertainty is also communicated using terms such as low, moderate, or high to describe the analyst's confidence level. However, little research has examined how intelligence professionals interpret these terms and whether they prefer them to numeric uncertainty quantifiers. In two experiments (N = 481 and 624, respectively), uncertainty communication preferences of expert (n = 41 intelligence analysts in Experiment 1) and nonexpert intelligence consumers were elicited. We examined which format participants judged to be more informative and simpler to process. We further tested whether participants treated verbal probability and confidence terms as independent constructs and whether participants provided coherent numeric probability translations of verbal probabilities. Results showed that although most nonexperts favored the numeric format, experts were about equally split, and most participants in both samples regarded the numeric format as more informative. Experts and nonexperts consistently conflated probability and confidence. For instance, confidence intervals inferred from verbal confidence terms had a greater effect on the location of the estimate than the width of the estimate, contrary to normative expectation. Approximately one-fourth of experts and over one-half of nonexperts provided incoherent numeric probability translations for the terms likely and unlikely when the elicitation of best estimates and lower and upper bounds were briefly spaced by intervening tasks.

From reductive to generative crisis: businesspeople using polysemous justifications to make sense of COVID-19

Both lay understandings of crisis moments and influential psychological models of cognition in times of uncertainty emphasize how crises limit thinking. Conversely, scholars as diverse as Foucault, Swidler, Bourdieu, and Butler have elaborated generative conceptions of crisis, which specify crises as moments of change, transformation, and heightened cognition. The research presented here takes up the question of how crises become thinkable, as actors gradually make sense of a newly uncertain context. Against a backdrop of polarization on the topic, in-depth interviews with 60 businesspeople navigating the coronavirus pandemic show that they see public health and economic well-being as interrelated. This has important effects on how businesses interpret and implement government directives and public health guidelines, from choosing to close before being mandated to do so, to staying closed even when allowed to reopen. Taken together, these findings substantiate generative models of crisis while drawing attention to the polysemous justifications elaborated by actors as they navigate shifting cultural and social scaffoldings.

Rule-Governed Behavior and Climate Change: Why Climate Warnings Fail to Motivate Sufficient Action

Climate scientists warn of dire consequences for ecological systems and human well-being if significant steps to mitigate greenhouse gas emissions are not taken immediately. Despite these warnings, greenhouse gas concentrations continue to rise, indicating that current responses are inadequate. Climate warnings and reactions to them may be analyzed in terms of rules and rule-governed behavior. The literature on rule-governed behavior in behavior analysis has identified a variety of factors that can reduce rule following, including insufficient rule exposure, insufficient learning history and rule complexity, incomplete rules, instructed behavior not sufficiently learned, rules having weak function-altering effects, conflicting rules, lack of speaker credibility, rule plausibility and inconsistency with prior learning, and insufficient reinforcement for rule following. The present paper aims to analyze how these factors might impact responses to climate change, and possible solutions and strategies are discussed. Much of the theory and research on climate-change communication has come from outside of behavior analysis. Thus, the paper also aims to integrate findings from this literature with a behavior-analytic approach to rule control. Interpreting climate warnings and climate solutions in terms of rule-governed behavior may improve our understanding of why such rules are not more effective, and aid in the development of verbal and nonverbal strategies for changing behavior and reducing greenhouse gas emissions.

Developmental Trajectories in the Understanding of Everyday Uncertainty Terms

Dealing with uncertainty and different degrees of frequency and probability is critical in many everyday activities. However, relevant information does not always come in the form of numerical estimates or direct experiences, but is instead obtained through qualitative, rather vague verbal terms (e.g., "the virus often causes coughing" or "the train is likely to be delayed"). Investigating how people interpret and utilize different natural language expressions of frequency and probability is therefore crucial to understand reasoning and behavior in real-world situations. While there is considerable work exploring how adults understand everyday uncertainty phrases, very little is known about how children interpret them and how their understanding develops with age. We take a developmental and computational perspective to address this issue and examine how 4- to 14-year-old children and adults interpret different terms. Each participant provided numerical estimates for 14 expressions, comprising both frequency and probability phrases. In total we obtained 2856 quantitative judgments, including 2240 judgments from children. Our findings demonstrate that adult-like intuitions about the interpretation of everyday uncertainty terms emerge fairly early in development, with the quantitative estimates of children converging to those of adults from around 9 years on. We also demonstrate how the vagueness of verbal terms can be represented through probability distributions, which provides additional leverage for tracking developmental shifts through cognitive modeling techniques. Taken together, our findings provide key insights into the developmental trajectories underlying the understanding of everyday uncertainty terms, and open up novel methodological pathways to formally model the vagueness of probability and frequency phrases, which are abundant in our everyday life and activities.

Untangling Trustworthiness and Uncertainty in Science: Implications for Science Education

This article focuses on uncertainty-ways in which scientists recognize and analyze limits in their studies and conclusions. We distinguish uncertainty from (un)trustworthiness-ways in which scientific reports can be affected by conscious deception or unconscious bias. Scientific journal articles typically include analyses and quantifications of uncertainty in both quantitative forms (e.g., error bars, ranges of predictions, statistical tests) and qualitative forms (e.g., alternate hypotheses, limitations of studies, questions for future research). These analyses of uncertainty are often incorporated into reports from scientific organizations and responsible scientific journalism. We argue that a critical goal of science education should be to help students understand how science may be employed as an uncertain and limited, yet still useful tool for informing decisions about socioscientific problems. When members of the public are insufficiently prepared to understand analyses and quantifications of uncertainty, the consequences are manifest in public skepticism about science and inadequately informed decision-making about socioscientific issues. We describe current design work in science education that includes a worthwhile emphasis on helping students to recognize and leverage uncertainty in their own data and models. Additional important work can enable students to develop proficiency in seeking out and understanding analyses of continuing uncertainty in media accounts of scientific conclusions and predictions.

Capturing richer information: On establishing the validity of an interval-valued survey response mode

Obtaining quantitative survey responses that are both accurate and informative is crucial to a wide range of fields. Traditional and ubiquitous response formats such as Likert and visual analogue scales require condensation of responses into discrete or point values—but sometimes a range of options may better represent the correct answer. In this paper, we propose an efficient interval-valued response mode, whereby responses are made by marking an ellipse along a continuous scale. We discuss its potential to capture and quantify valuable information that would be lost using conventional approaches, while preserving a high degree of response efficiency. The information captured by the response interval may represent a possible response range—i.e., a conjunctive set, such as the real numbers between 3 and 6. Alternatively, it may reflect uncertainty in respect to a distinct response—i.e., a disjunctive set, such as a confidence interval. We then report a validation study, utilizing our recently introduced open-source software (DECSYS), to explore how interval-valued survey responses reflect experimental manipulations of several factors hypothesised to influence interval width, across multiple contexts. Results consistently indicate that respondents used interval widths effectively, and subjective participant feedback was also positive. We present this as initial empirical evidence for the efficacy and value of interval-valued response capture. Interestingly, our results also provide insight into respondents’ reasoning about the different aforementioned types of intervals—we replicate a tendency towards overconfidence for those representing epistemic uncertainty (i.e., disjunctive sets), but find intervals representing inherent range (i.e., conjunctive sets) to be well-calibrated.

Effects of communicating uncertainty descriptions in hazard identification, risk characterization, and risk protection

Uncertainty is a crucial issue for any risk assessment. Consequently, it also poses crucial challenges for risk communications. Many guidebooks advise reporting uncertainties in risk assessments, expecting that the audience will appreciate this disclosure. However, the empirical evidence about the effects of uncertainty reporting is sparse and inconclusive. Therefore, based on examples of potential health risks of electromagnetic fields (EMF), three experiments were conducted analysing the effects of communicating uncertainties separately for hazard identification, risk characterisation and risk protection. The setups aimed to explore how reporting and how explaining of uncertainty affects dependent variables such as risk perception, perceived competence of the risk assessors, and trust in risk management. Each of the three experiments used a 2x2 design with a first factor presenting uncertainty descriptions (as used in public controversies on EMF related health effects) or describing a certainty conditions; and a second factor explaining the causes of uncertainties (by pointing at knowledge gaps) or not explaining them. The study results indicate that qualitative uncertainty descriptions regarding hazard identification reduce the confidence in the professional competencies of the assessors. In contrast, a quantitative uncertainty description in risk characterisation-regarding the magnitude of the risk-does not affect any of the dependent variables. Concerning risk protection, trust in exposure limit values is not affected by qualitative uncertainty information. However, the qualitative description of uncertainty regarding the adequacy of protection amplifies fears. Furthermore, explaining this uncertainty results in lower text understandability.

Towards robust statistical inference for complex computer models

Ecologists increasingly rely on complex computer simulations to forecast ecological systems. To make such forecasts precise, uncertainties in model parameters and structure must be reduced and correctly propagated to model outputs. Naively using standard statistical techniques for this task, however, can lead to bias and underestimation of uncertainties in parameters and predictions. Here, we explain why these problems occur and propose a framework for robust inference with complex computer simulations. After having identified that model error is more consequential in complex computer simulations, due to their more pronounced nonlinearity and interconnectedness, we discuss as possible solutions data rebalancing and adding bias corrections on model outputs or processes during or after the calibration procedure. We illustrate the methods in a case study, using a dynamic vegetation model. We conclude that developing better methods for robust inference of complex computer simulations is vital for generating reliable predictions of ecosystem responses.

On measuring agreement with numerically bounded linguistic probability schemes: A re-analysis of data from Wintle, Fraser, Wills, Nicholson, and Fidler (2019)

Across a wide range of domains, experts make probabilistic judgments under conditions of uncertainty to support decision-making. These judgments are often conveyed using linguistic expressions (e.g., x is likely). Seeking to foster shared understanding of these expressions between senders and receivers, the US intelligence community implemented a communication standard that prescribes a set of probability terms and assigns each term an equivalent numerical probability range. In an earlier PLOS ONE article, [1] tested whether access to the standard improves shared understanding and also explored the efficacy of various enhanced presentation formats. Notably, they found that embedding numeric equivalents in text (e.g., x is likely [55–80%]) substantially outperformed the status-quo approach in terms of the percentage overlap between participants’ interpretations of linguistic probabilities (defined in terms of the numeric range equivalents they provided for each term) and the numeric ranges in the standard. These results have important prescriptive implications, yet Wintle et al.’s percentage overlap measure of agreement may be viewed as unfairly punitive because it penalizes individuals for being more precise than the stipulated guidelines even when the individuals’ interpretations fall perfectly within the stipulated ranges. Arguably, subjects’ within-range precision is a positive attribute and should not be penalized in scoring interpretive agreement. Accordingly, in the present article, we reanalyzed Wintle et al.’s data using an alternative measure of percentage overlap that does not penalize in-range precision. Using the alternative measure, we find that percentage overlap is substantially elevated across conditions. More importantly, however, the effects of presentation format and probability level are highly consistent with the original study. By removing the ambiguity caused by Wintle et al.’s unduly punitive measure of agreement, these findings buttress Wintle et al.’s original claim that the methods currently used by intelligence organizations are ineffective at coordinating the meaning of uncertainty expressions between intelligence producers and intelligence consumers. Future studies examining agreement between senders and receivers are also encouraged to reflect carefully on the most appropriate measures of agreement to employ in their experiments and to explicate the bases for their methodological choices.

Facilitating sender-receiver agreement in communicated probabilities: Is it best to use words, numbers or both?

Organizations tasked with communicating expert judgments couched in uncertainty often use numerically bounded linguistic probability schemes to standardize the meaning of verbal probabilities. An experiment (N = 1,202) was conducted to ascertain whether agreement with such a scheme was better when probabilities were presented verbally, numerically or in a combined “verbal + numeric” format. Across three agreement measures, the numeric and combined formats outperformed the verbal format and also yielded better discrimination between low and high probabilities and were less susceptible to the fifty-fifty blip phenomenon. The combined format did not confer any advantage over the purely numeric format. The findings indicate that numerically bounded linguistic probability schemes are an ineffective means of communicating information about probabilities to others and they call into question recommendations for use of the combined format for delivering such schemes.

Uncertain About Uncertainty: How Qualitative Expressions of Forecaster Confidence Impact Decision-Making With Uncertainty Visualizations

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction between direct quantitative uncertainty and indirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of knowledge concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how "good" a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants' decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty.

Environmentally Sustainable Food Consumption: A Review and Research Agenda From a Goal-Directed Perspective

The challenge of convincing people to change their eating habits toward more environmentally sustainable food consumption (ESFC) patterns is becoming increasingly pressing. Food preferences, choices and eating habits are notoriously hard to change as they are a central aspect of people's lifestyles and their socio-cultural environment. Many people already hold positive attitudes toward sustainable food, but the notable gap between favorable attitudes and actual purchase and consumption of more sustainable food products remains to be bridged. The current work aims to (1) present a comprehensive theoretical framework for future research on ESFC, and (2) highlight behavioral solutions for environmental challenges in the food domain from an interdisciplinary perspective. First, starting from the premise that food consumption is deliberately or unintentionally directed at attaining goals, a goal-directed framework for understanding and influencing ESFC is built. To engage in goal-directed behavior, people typically go through a series of sequential steps. The proposed theoretical framework makes explicit the sequential steps or hurdles that need to be taken for consumers to engage in ESFC. Consumers need to positively value the environment, discern a discrepancy between the desired versus the actual state of the environment, opt for action to reduce the experienced discrepancy, intend to engage in behavior that is expected to bring them closer to the desired end state, and act in accordance with their intention. Second, a critical review of the literature on mechanisms that underlie and explain ESFC (or the lack thereof) in high-income countries is presented and integrated into the goal-directed framework. This contribution thus combines a top-down conceptualization with a bottom-up literature review; it identifies and discusses factors that might hold people back from ESFC and interventions that might promote ESFC; and it reveals knowledge gaps as well as insights on how to encourage both short- and long-term ESFC by confronting extant literature with the theoretical framework. Altogether, the analysis yields a set of 33 future research questions in the interdisciplinary food domain that deserve to be addressed with the aim of fostering ESFC in the short and long term.

Impact of uncertainty and ambiguous outcome phrasing on moral decision-making

The literature has shown that different types of moral dilemmas elicit discrepant decision patterns. The present research investigated the role of uncertainty in contributing to these decision patterns. Two studies were conducted to examine participants' choices in commonly used dilemmas. Study 1 showed that participants' perceived outcome probabilities were significantly associated with their moral choices, and that these associations were independent from the dilemma type. Study 2 revealed that participants had significantly less preference for killing the individual when the outcome probabilities were stated using the modal verb 'will' than when they were stated using the numerical phrasing of '100%'. Our findings illustrate a discord between experimenter and participant in the interpretation of task instructions.

12,000-Year-old Aboriginal rock art from the Kimberley region, Western Australia

The Kimberley region in Western Australia hosts one of the world's most substantial bodies of indigenous rock art thought to extend in a series of stylistic or iconographic phases from the present day back into the Pleistocene. As with other rock art worldwide, the older styles have proven notoriously difficult to date quantitatively, requiring new scientific approaches. Here, we present the radiocarbon ages of 24 mud wasp nests that were either over or under pigment from 21 anthropomorphic motifs of the Gwion style (previously referred to as "Bradshaws") from the middle of the relative stylistic sequence. We demonstrate that while one date suggests a minimum age of c. 17 ka for one motif, most of the dates support a hypothesis that these Gwion paintings were produced in a relatively narrow period around 12,000 years ago.

Effect of Providing the Uncertainty Information About a Tornado Occurrence on the Weather Recipients' Cognition and Protective Action: Probabilistic Hazard Information Versus Deterministic Warnings

Currently, a binary alarm system is used in the United States to issue deterministic warning polygons in case of tornado events. To enhance the effectiveness of the weather information, a likelihood alarm system, which uses a tool called probabilistic hazard information (PHI), is being developed at National Severe Storms Laboratory to issue probabilistic information about the threat. This study aims to investigate the effects of providing the uncertainty information about a tornado occurrence through the PHI's graphical swath on laypeople's concern, fear, and protective action, as compared with providing the warning information with the deterministic polygon. The displays of color-coded swaths and deterministic polygons were shown to subjects. Some displays had a blue background denoting the probability of any tornado formation in the general area. Participants were asked to report their levels of concern, fear, and protective action at randomly chosen locations within each of seven designated levels on each display. Analysis of a three-stage nested design showed that providing the uncertainty information via the PHI would appropriately increase recipients' levels of concern, fear, and protective action in highly dangerous scenarios, with a more than 60% chance of being affected by the threat, as compared with deterministic polygons. The blue background and the color-coding type did not have a significant effect on the people's cognition of the threat and reaction to it. This study shows that using a likelihood alarm system leads to more conscious decision making by the weather information recipients and enhances the system safety.

Communicating uncertainty about facts, numbers and science

Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combining a statistical and psychological perspective. This informs a framework for uncertainty communication in which we identify three objects of uncertainty-facts, numbers and science-and two levels of uncertainty: direct and indirect. An examination of current practices provides a scale of nine expressions of direct uncertainty. We discuss attempts to codify indirect uncertainty in terms of quality of the underlying evidence. We review the limited literature about the effects of communicating epistemic uncertainty on cognition, affect, trust and decision-making. While there is some evidence that communicating epistemic uncertainty does not necessarily affect audiences negatively, impact can vary between individuals and communication formats. Case studies in economic statistics and climate change illustrate our framework in action. We conclude with advice to guide both communicators and future researchers in this important but so far rather neglected field.

Verbal probabilities: Very likely to be somewhat more confusing than numbers

People interpret verbal expressions of probabilities (e.g. 'very likely') in different ways, yet words are commonly preferred to numbers when communicating uncertainty. Simply providing numerical translations alongside reports or text containing verbal probabilities should encourage consistency, but these guidelines are often ignored. In an online experiment with 924 participants, we compared four different formats for presenting verbal probabilities with the numerical guidelines used in the US Intelligence Community Directive (ICD) 203 to see whether any could improve the correspondence between the intended meaning and participants' interpretation ('in-context'). This extends previous work in the domain of climate science. The four experimental conditions we tested were: 1. numerical guidelines bracketed in text, e.g. X is very unlikely (05-20%), 2. click to see the full guidelines table in a new window, 3. numerical guidelines appear in a mouse over tool tip, and 4. no guidelines provided (control). Results indicate that correspondence with the ICD 203 standard is substantially improved only when numerical guidelines are bracketed in text. For this condition, average correspondence was 66%, compared with 32% in the control. We also elicited 'context-free' numerical judgements from participants for each of the seven verbal probability expressions contained in ICD 203 (i.e., we asked participants what range of numbers they, personally, would assign to those expressions), and constructed 'evidence-based lexicons' based on two methods from similar research, 'membership functions' and 'peak values', that reflect our large sample's intuitive translations of the terms. Better aligning the intended and assumed meaning of fuzzy words like 'unlikely' can reduce communication problems between the reporter and receiver of probabilistic information. In turn, this can improve decision making under uncertainty.

Combining two national-scale datasets to map soil properties, the case of available magnesium in England and Wales

Given the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant-available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross-validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties.

HIGHLIGHTS

Differences in sample support mean that two datasets on a soil property cannot be combined simply.We showed how a multivariate geostatistical model can be used to elucidate the relationships between two such datasets.The same model allows soil properties to be mapped jointly from such data.This offers a general basis for combining soil datasets from diverse sources.

Communicating Geographical Risks in Crisis Management: The Need for Research

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.

Guidance on Communication of Uncertainty in Scientific Assessments

This document provides guidance for communicators on how to communicate the various expressions of uncertainty described in EFSA's document: 'Guidance on uncertainty analysis in scientific assessments'. It also contains specific guidance for assessors on how best to report the various expressions of uncertainty. The document provides a template for identifying expressions of uncertainty in scientific assessments and locating the specific guidance for each expression. The guidance is structured according to EFSA's three broadly defined categories of target audience: 'entry', 'informed' and 'technical' levels. Communicators should use the guidance for entry and informed audiences, while assessors should use the guidance for the technical level. The guidance was formulated using evidence from the scientific literature, grey literature and two EFSA research studies, or based on judgement and reasoning where evidence was incomplete or missing. The limitations of the evidence sources inform the recommendations for further research on uncertainty communication.

Climate change and intergroup relations: Psychological insights, synergies, and future prospects

Climate change is often conceived as a technical challenge, requiring smart policies and science-driven solutions. Yet, as revealed by each new round of international negotiations, and by growing (rather than receding) partisan divides on climate change in the United States, climate change is also profoundly social: How people understand and engage with the issue is powerfully influenced by the responses of others, including members of ingroups and outgroups. This special issue brings together research and theory that shed light on new and understudied group and intergroup dimensions of climate change. The featured articles showcase the breadth of social psychological processes (e.g., social identity and categorization processes, intergroup perceptions, normative influence, justice concerns, and group-based ideologies) relevant to the study of climate change while highlighting how the problem’s shared, global relevance poses unique questions and opportunities for the field. We explore the contributions of these articles to the social psychological study of climate change and highlight new challenges and pathways forward.

CDF-quantile distributions for modelling random variables on the unit interval

This paper introduces a two-parameter family of distributions for modelling random variables on the (0,1) interval by applying the cumulative distribution function of one 'parent' distribution to the quantile function of another. Family members have explicit probability density functions, cumulative distribution functions and quantiles in a location parameter and a dispersion parameter. They capture a wide variety of shapes that the beta and Kumaraswamy distributions cannot. They are amenable to likelihood inference, and enable a wide variety of quantile regression models, with predictors for both the location and dispersion parameters. We demonstrate their applicability to psychological research problems and their utility in modelling real data.

Social Climate Science: A New Vista for Psychological Science

The recent Paris Agreement to limit greenhouse gas emissions, adopted by 195 nations at the 2015 United Nations Climate Change Conference, signaled unprecedented commitment by world leaders to address the human social aspects of climate change. Indeed, climate change increasingly is recognized by scientists and policymakers as a social issue requiring social solutions. However, whereas psychological research on intrapersonal and some group-level processes (e.g., political polarization of climate beliefs) has flourished, research into other social processes-such as an understanding of how nonpartisan social identities, cultural ideologies, and group hierarchies shape public engagement on climate change-has received substantially less attention. In this article, we take stock of current psychological approaches to the study of climate change to explore what is "social" about climate change from the perspective of psychology. Drawing from current interdisciplinary perspectives and emerging empirical findings within psychology, we identify four distinct features of climate change and three sets of psychological processes evoked by these features that are fundamentally social and shape both individual and group responses to climate change. Finally, we consider how a more nuanced understanding of the social underpinnings of climate change can stimulate new questions and advance theory within psychology.

Informing Public Perceptions About Climate Change: A 'Mental Models' Approach

As the specter of climate change looms on the horizon, people will face complex decisions about whether to support climate change policies and how to cope with climate change impacts on their lives. Without some grasp of the relevant science, they may find it hard to make informed decisions. Climate experts therefore face the ethical need to effectively communicate to non-expert audiences. Unfortunately, climate experts may inadvertently violate the maxims of effective communication, which require sharing communications that are truthful, brief, relevant, clear, and tested for effectiveness. Here, we discuss the 'mental models' approach towards developing communications, which aims to help experts to meet the maxims of effective communications, and to better inform the judgments and decisions of non-expert audiences.

Communicative Functions of Directional Verbal Probabilities: Speaker's Choice, Listener's Inference, and Reference Points

Verbal probabilities have directional communicative functions, and most can be categorized as positive (e.g., “it is likely”) or negative (e.g., “it is doubtful”). We examined the communicative functions of verbal probabilities based on the reference point hypothesis According to this hypothesis, listeners are sensitive to and can infer a speaker's reference points based on the speaker's selected directionality. In four experiments (two of which examined speakers’ choice of directionality and two of which examined listeners’ inferences about a speaker's reference point), we found that listeners could make inferences about speakers’ reference points based on the stated directionality of verbal probability. Thus, the directionality of verbal probabilities serves the communicative function of conveying information about a speaker's reference point.

Communicating Conservation Status: How Different Statistical Assessment Criteria Affect Perceptions of Extinction Risk

Although alternative forms of statistical and verbal information are routinely used to convey species' extinction risk to policymakers and the public, little is known about their effects on audience information processing and risk perceptions. To address this gap in literature, we report on an experiment that was designed to explore how perceptions of extinction risk differ as a function of five different assessment benchmarks (Criteria A-E) used by scientists to classify species within IUCN Red List risk levels (e.g., Critically Endangered, Vulnerable), as well as the role of key individual differences in these effects (e.g., rational and experiential thinking styles, environmental concern). Despite their normative equivalence within the IUCN classification system, results revealed divergent effects of specific assessment criteria: on average, describing extinction risk in terms of proportional population decline over time (Criterion A) and number of remaining individuals (Criterion D) evoked the highest level of perceived risk, whereas the single-event probability of a species becoming extinct (Criterion E) engendered the least perceived risk. Furthermore, participants scoring high in rationality (analytic thinking) were less prone to exhibit these biases compared to those low in rationality. Our findings suggest that despite their equivalence in the eyes of scientific experts, IUCN criteria are indeed capable of engendering different levels of risk perception among lay audiences, effects that carry direct and important implications for those tasked with communicating about conservation status to diverse publics.

“I am uncertain” vs “It is uncertain”. How linguistic markers of the uncertainty source affect uncertainty communication

Two psychological sources of uncertainty bear implications for judgment and decision-making: external uncertainty is seen as stemming from properties of the world, whereas internal uncertainty is seen as stemming from lack of knowledge. The apparent source of uncertainty can be conveyed through linguistic markers, such as the pronoun of probability phrases (e.g., I am uncertain vs. It is uncertain). Here, we investigated whether and when speakers use different pronoun subjects as such linguistic markers (Exp. 1 and 2) and what hearers infer from them (Exp. 3 and 4). Speakers more often described higher probabilities and knowable outcomes with internal probability phrases. In dialogue, speakers mirrored the source of their conversational partner. Markers of the source had a main effect or interacted with the probability conveyed and speaker expertise to shape the judgments and decisions of hearers. For example, experts voicing an internal probability phrase were judged as more knowledgeable than experts using an external probability phrase whereas the result was the opposite for lay speakers. We discuss how these findings inform our understanding of subjective uncertainty and uncertainty communication theories.

Making better decisions in groups

We review the literature to identify common problems of decision-making in individuals and groups. We are guided by a Bayesian framework to explain the interplay between past experience and new evidence, and the problem of exploring the space of hypotheses about all the possible states that the world could be in and all the possible actions that one could take. There are strong biases, hidden from awareness, that enter into these psychological processes. While biases increase the efficiency of information processing, they often do not lead to the most appropriate action. We highlight the advantages of group decision-making in overcoming biases and searching the hypothesis space for good models of the world and good solutions to problems. Diversity of group members can facilitate these achievements, but diverse groups also face their own problems. We discuss means of managing these pitfalls and make some recommendations on how to make better group decisions.

Simulation of Optimal Decision-Making Under the Impacts of Climate Change

Climate change causes transformations to the conditions of existing agricultural practices appointing farmers to continuously evaluate their agricultural strategies, e.g., towards optimising revenue. In this light, this paper presents a framework for applying Bayesian updating to simulate decision-making, reaction patterns and updating of beliefs among farmers in a developing country, when faced with the complexity of adapting agricultural systems to climate change. We apply the approach to a case study from Ghana, where farmers seek to decide on the most profitable of three agricultural systems (dryland crops, irrigated crops and livestock) by a continuous updating of beliefs relative to realised trajectories of climate (change), represented by projections of temperature and precipitation. The climate data is based on combinations of output from three global/regional climate model combinations and two future scenarios (RCP4.5 and RCP8.5) representing moderate and unsubstantial greenhouse gas reduction policies, respectively. The results indicate that the climate scenario (input) holds a significant influence on the development of beliefs, net revenues and thereby optimal farming practices. Further, despite uncertainties in the underlying net revenue functions, the study shows that when the beliefs of the farmer (decision-maker) opposes the development of the realised climate, the Bayesian methodology allows for simulating an adjustment of such beliefs, when improved information becomes available. The framework can, therefore, help facilitating the optimal choice between agricultural systems considering the influence of climate change.

How certain is 'certain'? Exploring how the English-language media reported the use of calibrated language in the Intergovernmental Panel on Climate Change's Fifth Assessment Report

This article presents findings from an analysis of English-language media reports following the publication of the fifth Intergovernmental Panel on Climate Change Assessment Report in September 2013. Focusing on the way they reported the Intergovernmental Panel on Climate Change's use of 'calibrated' language, we find that of 1906 articles relating to the issuing of the report only 272 articles (14.27%) convey the use of a deliberate and systematic verbal scale. The Intergovernmental Panel on Climate Change's carefully calibrated language was rarely discussed or explicated, but in some instances scientists, political actors or journalists would attempt to contextualise or elaborate on the reported findings by using analogies to other scientific principles or examples of taking action despite uncertainty. We consider those analogies in terms of their efficacy in communicating (un)certainty.

Politeness and the communication of uncertainty

Ambiguity in language derives, in part, from the multiple motivations that underlie the choice to use any particular expression. The use of some lexical items, such as probability expressions and scalar terms, can be motivated by a desire to communicate uncertainty as well as a desire to be polite (i.e., manage face). Research has demonstrated that the interpretation of these items can be influenced by the existence of a potential politeness motive. In general, communications about negative events, relative to positive events, result in higher likelihood estimates whenever politeness can be discerned as a potential motive. With few exceptions, however, this research has focused only on the hearer. In the present research we focused on the dyad and examined whether speakers vary their messages as a function of politeness, and the effect that this has on subsequent judgments made by a recipient. In two experiments we presented participants with situations that varied in terms of face-threat and asked them how they would communicate potentially threatening information. Both experiments included a second set of participants who read these utterances and provided judgments as to the degree of uncertainty conveyed by the utterance. In both experiments, messages in the face-threatening condition conveyed greater uncertainty than messages in the non-face-threatening condition, and the probability estimates made by the second set of participants varied as a function of conveyed uncertainty. This research demonstrates that when examining speakers and hearers together, severe events may be judged less likely (rather than more likely), because speakers tend to hedge the certainty with which they communicate the information.

Approaches for describing and communicating overall uncertainty in toxicity characterizations: U.S. Environmental Protection Agency's Integrated Risk Information System (IRIS) as a case study

Single point estimates of human health hazard/toxicity values such as a reference dose (RfD) are generally used in chemical hazard and risk assessment programs for assessing potential risks associated with site- or use-specific exposures. The resulting point estimates are often used by risk managers for regulatory decision-making, including standard setting, determination of emission controls, and mitigation of exposures to chemical substances. Risk managers, as well as stakeholders (interested and affected parties), often have limited information regarding assumptions and uncertainty factors in numerical estimates of both hazards and risks. Further, the use of different approaches for addressing uncertainty, which vary in transparency, can lead to a lack of confidence in the scientific underpinning of regulatory decision-making. The overarching goal of this paper, which was developed from an invited participant workshop, is to offer five approaches for presenting toxicity values in a transparent manner in order to improve the understanding, consideration, and informed use of uncertainty by risk assessors, risk managers, and stakeholders. The five approaches for improving the presentation and communication of uncertainty are described using U.S. Environmental Protection Agency's (EPA's) Integrated Risk Information System (IRIS) as a case study. These approaches will ensure transparency in the documentation, development, and use of toxicity values at EPA, the Agency for Toxic Substances and Disease Registry (ATSDR), and other similar assessment programs in the public and private sector. Further empirical testing will help to inform the approaches that will work best for specific audiences and situations.

When, not if: the inescapability of an uncertain climate future

Climate change projections necessarily involve uncertainty. Analysis of the physics and mathematics of the climate system reveals that greater uncertainty about future temperature increases is nearly always associated with greater expected damages from climate change. In contrast to those normative constraints, uncertainty is frequently cited in public discourse as a reason to delay mitigative action. This failure to understand the actual implications of uncertainty may incur notable future costs. It is therefore important to communicate uncertainty in a way that improves people's understanding of climate change risks. We examined whether responses to projections were influenced by whether the projection emphasized uncertainty in the outcome or in its time of arrival. We presented participants with statements and graphs indicating projected increases in temperature, sea levels, ocean acidification and a decrease in arctic sea ice. In the uncertain-outcome condition, statements reported the upper and lower confidence bounds of the projected outcome at a fixed time point. In the uncertain time-of-arrival condition, statements reported the upper and lower confidence bounds of the projected time of arrival for a fixed outcome. Results suggested that people perceived the threat as more serious and were more likely to encourage mitigative action in the time-uncertain condition than in the outcome-uncertain condition. This finding has implications for effectively communicating the climate change risks to policy-makers and the general public.

Uncertainty as knowledge

This issue of Philosophical Transactions examines the relationship between scientific uncertainty about climate change and knowledge. Uncertainty is an inherent feature of the climate system. Considerable effort has therefore been devoted to understanding how to effectively respond to a changing, yet uncertain climate. Politicians and the public often appeal to uncertainty as an argument to delay mitigative action. We argue that the appropriate response to uncertainty is exactly the opposite: uncertainty provides an impetus to be concerned about climate change, because greater uncertainty increases the risks associated with climate change. We therefore suggest that uncertainty can be a source of actionable knowledge. We survey the papers in this issue, which address the relationship between uncertainty and knowledge from physical, economic and social perspectives. We also summarize the pervasive psychological effects of uncertainty, some of which may militate against a meaningful response to climate change, and we provide pointers to how those difficulties may be ameliorated.