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Previous studies of age-related macular degeneration (AMD) report impaired facial expression recognition even with enlarged face images. Here, we test potential benefits of caricaturing (exaggerating how the expression's shape differs from neutral) as an image enhancement procedure targeted at mid- to high-level cortical vision. Experiment 1 provides proof-of-concept using normal vision observers shown blurred images as a partial simulation of AMD. Caricaturing significantly improved expression recognition (happy, sad, anger, disgust, fear, surprise) by ∼4%-5% across young adults and older adults (mean age 73 years); two different severities of blur; high, medium, and low intensity of the original expression; and all intermediate accuracy levels (impaired but still above chance). Experiment 2 tested AMD patients, running 19 eyes monocularly (from 12 patients, 67-94 years) covering a wide range of vision loss (acuities 6/7.5 to poorer than 6/360). With faces pre-enlarged, recognition approached ceiling and was only slightly worse than matched controls for high- and medium-intensity expressions. For low-intensity expressions, recognition of veridical expressions remained impaired and was significantly improved with caricaturing across all levels of vision loss by 5.8%. Overall, caricaturing benefits emerged when improvement was most needed, that is, when initial recognition of uncaricatured expressions was impaired.

We argue that the way ambiguity has been operationalized throughout the literature on ambiguity effects has an important limitation, insofar as ambiguity in outcomes has been neglected. We report two studies where judges do encounter ambiguity in the sampled outcomes and find evidence that ambiguity aversion is not less than when judges are given a range of outcomes without reference to ambiguous outcomes themselves. This result holds regardless of whether people are presented with a sample all at once or sample outcomes sequentially. Our experiments also investigate the effects of conflicting information about outcomes, finding that conflict aversion also does not decrease. Moreover, ambiguity and conflict aversion do not seem to arise as a consequence of judges ignoring uncertain outcomes and thereby treating outcome sets as reduced samples of unambiguous (or unconflicting) information. Instead, ambiguity and conflict aversion are partly explained by more pessimistic outcome forecasts by judges. This pessimism, in turn, may be due to the judges' uncertainty about how the chance of a desirable outcome from an ambiguous or conflictive alternative compares with an equivalent risky alternative. Both studies used hypothetical scenarios, and no incentives were provided for participants' decisions.

Causal reasoning with ambiguous observations requires subjects to estimate and evaluate the ambiguous observations. Detecting how people process ambiguous observations can be complicated by individual differences in causal reasoning. This paper proposes a hierarchical model that accounts for the uncertainty in both the distribution of the functional form selection and the distribution of the ambiguity treatment selection. The model provides an alternative to self-report measures for identifying subjects' strategic choices in reasoning about causal relationships under ambiguity. The posterior distribution of the causal estimates is determined by both the functional form and the ambiguity processing strategy adopted by the reasoner. Our model is tested in a simulation study where it demonstrates its ability to recover the strategies and functional forms adopted by simulated subjects across a range of hypothetical conditions. In addition, the model is applied to the results of an experimental study. Copyright © 2016 John Wiley & Sons, Ltd.

Vested interests and political agents have long opposed political or regulatory action in response to climate change by appealing to scientific uncertainty. Here we examine the effect of such contrarian talking points on the scientific community itself. We show that although scientists are trained in dealing with uncertainty, there are several psychological reasons why scientists may nevertheless be susceptible to uncertainty-based argumentation, even when scientists recognize those arguments as false and are actively rebutting them. Specifically, we show that prolonged stereotype threat, pluralistic ignorance, and a form of projection (the third-person effect) may cause scientists to take positions that they would be less likely to take in the absence of outspoken public opposition. We illustrate the consequences of seepage from public debate into the scientific process with a case study involving the interpretation of temperature trends from the last 15 years. We offer ways in which the scientific community can detect and avoid such inadvertent seepage.

Many real-world planning and decision problems are far too uncertain, too variable, and too complicated to support realistic mathematical models. Nonetheless, we explain the usefulness, in these situations, of qualitative insights from mathematical decision theory. We demonstrate the integration of info-gap robustness in decision problems in which surprise and ignorance are predominant and where personal and collective psychological factors are critical. We present practical guidelines for employing adaptable-choice strategies as a proxy for robustness against uncertainty. These guidelines include being prepared for more surprises than we intuitively expect, retaining sufficiently many options to avoid premature closure and conflicts among preferences, and prioritizing outcomes that are steerable, whose consequences are observable, and that do not entail sunk costs, resource depletion, or high transition costs. We illustrate these concepts and guidelines with the example of the medical management of the 2003 SARS outbreak in Vietnam.

The relationship between trust and uncertainty has not been fully developed in current frameworks on trust, including trust in autonomous systems. This chapter presents an investigation of this relationship. It begins with a survey of trust and distrust in general, followed by a focus on human-robot interaction (HRI). Thereafter, the roles of uncertainty in trust and distrust are elucidated, and the impacts of different kinds and sources of uncertainty are elaborated.

One prominent approach in the exploration of the variations in project team performance has been to study two components of the aggregate personalities of the team members: conscientiousness and agreeableness. A second line of research, known as self-categorisation theory, argues that identifying as team members and the team's performance norms should substantially influence the team's performance. This paper explores the influence of both these perspectives in university software engineering project teams. Eighty students worked to complete a piece of software in small project teams during 2007 or 2008. To reduce limitations in statistical analysis, Monte Carlo simulation techniques were employed to extrapolate from the results of the original sample to a larger simulated sample (2043 cases, within 319 teams). The results emphasise the importance of taking into account personality (particularly conscientiousness), and both team identification and the team's norm of performance, in order to cultivate high...

Uncertainty forms an integral part of climate science, and it is often used to argue against mitigative action. This article presents an analysis of uncertainty in climate sensitivity that is robust to a range of assumptions. We show that increasing uncertainty is necessarily associated with greater expected damages from warming, provided the function relating warming to damages is convex. This constraint is unaffected by subjective or cultural risk-perception factors, it is unlikely to be overcome by the discount rate, and it is independent of the presumed magnitude of climate sensitivity. The analysis also extends to “second-order” uncertainty; that is, situations in which experts disagree. Greater disagreement among experts increases the likelihood that the risk of exceeding a global temperature threshold is greater. Likewise, increasing uncertainty requires increasingly greater protective measures against sea level rise. This constraint derives directly from the statistical properties of extreme values. We conclude that any appeal to uncertainty compels a stronger, rather than weaker, concern about unabated warming than in the absence of uncertainty.

In public debate surrounding climate change, scientific uncertainty is often cited in connection with arguments against mitigative action. This article examines the role of uncertainty about future climate change in determining the likely success or failure of mitigative action. We show by Monte Carlo simulation that greater uncertainty translates into a greater likelihood that mitigation efforts will fail to limit global warming to a target (e.g., 2 °C). The effect of uncertainty can be reduced by limiting greenhouse gas emissions. Taken together with the fact that greater uncertainty also increases the potential damages arising from unabated emissions (Lewandowsky et al. 2014), any appeal to uncertainty implies a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty.

Despite its importance, research has revealed limitations in jury decision-making and has sought to develop processes for its improvement. Using 92 individual mock jurors, this study examined the use of question trails (QTs) as decision aids, testing a cognitive load explanation of their benefits and investigating individual differences in need for cognition (NFC) and need for cognitive closure (NFCC). Only limited evidence was obtained suggesting significant improvements due to QTs, and results failed to find a mediating role for extrinsic references as cognitive load explanations propose. QTs did not appear to increase decision confidence or significantly reduce perceived difficulty, leading rather, for some offences, to lower confidence. Inconclusive evidence was obtained consistent with a moderating role for individual NFC and NFCC on QT effectiveness. Implications for future research, theoretical understandings of jury decision-making, and the use of QTs in the legal system are discussed.

While much has been written about the consequences of zero-sum (or fixed-pie) beliefs, their measurement has received almost no systematic attention. No researchers, to our awareness, have examined the question of whether the endorsement of a zero-sum-like proposition depends on how the proposition is formed. This paper focuses on this issue, which may also apply to the measurement of other attitudes. Zero-sum statements have a form such as “The more of resource X for consumer A, the less of resource Y for consumer B”. X and Y may be the same resource (such as time), but they can be different (e.g., “The more people commute by bicycle, the less revenue for the city from car parking payments”). These statements have four permutations, and a strict zero-sum believer should regard these four statements as equally valid and therefore should endorse them equally. We find, however, that three asymmetric patterns routinely occur in people’s endorsement levels, i.e., clear framing effects, whereby endorsement of one permutation substantially differs from endorsement of another. The patterns seem to arise from beliefs about asymmetric resource flows and power relations between rival consumers. We report three studies, with adult samples representative of populations in two Western and two non-Western cultures, demonstrating that most of the asymmetric belief patterns are consistent across these samples. We conclude with a discussion of the implications of this kind of “order effect” for attitude measurement.

On grounds of insufficient reason, a probability of 1/K is assigned to K mutually exclusive possible events when nothing is known about the likelihood of those events. Fox and Rottenstreich (2003) present evidence that subjective probability judgments are typically biased towards this ignorance prior, and therefore depend on the partition K. Results from two studies indicate that lower-upper (imprecise) probability judgments by naive judges also exhibit partition dependence, despite the potential that imprecise probabilities provide for avoiding it. However, beta regression reveals two kinds of priming effects, one of which is modeled by mixture distributions. Another novel finding suggests that when partition primes conflict with a normatively correct partition some judges widen their probability intervals to encompass both partitions. The results indicate that imprecise probability judgments may be better suited than precise probabilities for handling conflicting or ambiguous information about ...

HIGHLIGHTS We use a simple gambles design in an fMRI study to compare two conditions: ambiguity and conflict. Participants were more conflict averse than ambiguity averse. Ambiguity aversion did not correlate with conflict aversion. Activation in the medial prefrontal cortex correlated with ambiguity level and ambiguity aversion. Activation in the ventral striatum correlated with conflict level and conflict aversion. Studies of decision making under uncertainty generally focus on imprecise information about outcome probabilities (“ambiguity”). It is not clear, however, whether conflicting information about outcome probabilities affects decision making in the same manner as ambiguity does. Here we combine functional magnetic resonance imaging (fMRI) and a simple gamble design to study this question. In this design the levels of ambiguity and conflict are parametrically varied, and ambiguity and conflict gambles are matched on expected value. Behaviorally, participants avoided conflict more than ambiguity, and attitudes toward ambiguity and conflict did not correlate across participants. Neurally, regional brain activation was differentially modulated by ambiguity level and aversion to ambiguity and by conflict level and aversion to conflict. Activation in the medial prefrontal cortex was correlated with the level of ambiguity and with ambiguity aversion, whereas activation in the ventral striatum was correlated with the level of conflict and with conflict aversion. These novel results indicate that decision makers process imprecise and conflicting information differently, a finding that has important implications for basic and clinical research.

Whether conflict and ambiguity are distinct kinds of uncertainty remains an open question, as does their joint impact on judgments of overall uncertainty. This paper reviews recent advances in our understanding of human judgment and decision making when both ambiguity and conflict are present, and presents two types of testable models of judgments under conflict and ambiguity. The first type concerns estimate-pooling to arrive at “best” probability estimates. The second type is models of subjective assessments of conflict and ambiguity. These models are developed for dealing with both described and experienced information. A framework for testing these models in the described-information setting is presented, including a reanalysis of a multi-nation data-set to test best-estimate models, and a study of participants' assessments of conflict, ambiguity, and overall uncertainty reported by Smithson (2013). A framework for research in the experienced-information setting is then developed, that differs substantially from extant paradigms in the literature. This framework yields new models of “best” estimates and perceived conflict. The paper concludes with specific suggestions for future research on judgment and decision making under conflict and ambiguity.

Aims To conduct an empirical investigation of the efficacy of law enforcement in reducing heroin supply in Australia. Specifically, this paper addresses the question of whether heroin purity levels in the Australian Capital Territory (ACT) could be predicted by heroin seizures at the national level by the Australian Federal Police (AFP) in the preceding year. Design We considered two forms of evidence. First, a Bayesian Markov Chain Monte Carlo (MCMC) change-point model was used to discover (a) if there was a substantial increase in heroin seizures by the AFP, (b) when the increase began and (c) whether it occurred after increased funding to the Australian Federal Police for the purpose of drug law enforcement. Second, standard timeseries methods were used to ascertain whether fluctuations in heroin seizure weights or the frequency of large-scale seizures after the aforementioned changes in seizure levels predicted fluctuations in heroin purity levels in the ACT after autocorrelation had been removed from the purity series. Findings A Bayesian MCMC change-point model supported the hypothesis that heroin seizures rapidly increased about a year before the estimated decline in heroin purity and after the increased funding of AFP. The autoregression models suggested that 10‐20% of the variance in the residuals of the heroin purity series was predicted by appropriately lagged residuals of the seizurenumber and log-weight series, after autocorrelation had been removed. Conclusion The overall results are consistent with the hypothesis that largescale heroin seizures by the AFP reduce street-level heroin supply a year or so later, although the short-term dynamics suggest an ‘opponent’ response to residual fluctuations in seizures. To our knowledge, this is first time a connection has been identified between large-scale heroin seizures and street-level

This paper surveys results of a research program investigating human judgments of imprecise probabilities under sample-space ignorance (i.e., ignorance of what the possible outcomes are in a decision). The framework used for comparisons with human judgments is primarily due to Walley (1991, 1996). Five studies are reported which test four of Walley's prescriptions for judgment under sample-space ignorance, as well as assessing the impact of the number of observations and types of events on subjective lower and upper probability estimates. The paper concludes with a synopsis of future directions for empirical research on subjective imprecise probability judgments.

This research investigates preferences and judgments under ambiguous vs conflicting information. Three studies provided evidence for two major hypotheses: (1) Conflicting messages from two equally believable sources are dispreferred in general to two informatively equivalent, ambiguous, but agreeing messages from the same sources (i.e., conflict aversion); and (2) conflicting sources are perceived as less credible than ambiguous sources. Studies 2 and 3 yielded evidence for two framing effects. First, when the outcome was negative, subjects' preferences were nearly evenly split between conflict and ambiguity, whereas a positive outcome produced marked conflict aversion. Second, a high probability of a negative outcome or a low probability of a positive one induced conflict preference. However, no framing effects were found for source credibility judgments. Study 3 also investigated whether subject identification with a source might affect preferences or credibility judgments, but found no evi dence for such an effect. The findings suggest cognitive and moti vational explanations for conflict aversion as distinct from ambi guity aversion. The cognitive heuristic is that conflict raises suspicions about whether the sources are trustworthy or credi ble. The motivational explanation stems from that idea that if sources disagree, then the judge not only becomes uncertain but also must disagree with at least one of the sources, whereas if the sources agree then the judge may agree with them and only has to bear the uncertainty. Copyright 1999 Academic Press.

This paper attempts to establish that ignorance is a necessary condition for science to have a purpose, just as it is a precondition for any kind of intentional learning or discovety. The author argues that those who find intrinsic value in science must necessarily attribute positive value to ignorance, for the relationship between the two is symbiotic rather than combative. He presents a number of ways in which scientific ignorance may be positively valued by scientists and non-scientists and lays down a framework for discussing ignorance. He finds accumulating evidence of a shift in scientists' values towards more tolerance of ignorance. The paper concludes by saying that although some trends and currents suggest a swing towards an insistence on certainty, safety and security of political agendas, this does not indicate which way the rest of society is going. The author points out the possibility that at least some sectors of society may be moving towards a realization that many kinds of ignorance are irreducible, and with that, a recognition of the need for scientists to cultivate their particular type of purposive ignorance.

Given a linear relationship between two continuous random variables $X$ and $Y$ that may be moderated by a third, $Z$, the extent to which the correlation $rho$ is (un)moderated by $Z$ is equivalent to the extent to which the regression coefficients $beta_y$ and $beta_x$ are (un)moderated by $Z$ iff the variance ratio $sigma_y^2/sigma_x^2$ is constant over the range or states of $Z$. Otherwise, moderation of slopes and of correlations must diverge. Most of the literature on this issue focuses on tests for heterogeneity of variance in $Y$, and a test for this ratio has not been investigated. Given that regression coefficients are proportional to $rho$ via this ratio, accurate tests and estimations of it would have several uses. This paper presents such a test for both a discrete and continuous moderator and evaluates its Type I error rate and power under unequal sample sizes and departures from normality. It also provides a unified approach to modeling moderated slopes and correlations with categorical moderators via structural equations models.

Recent decades have seen a dramatic increase in creative work on scientifc ignorance and uncertainty, which can be traced in part to a realization that ignorance and uncertainty cannot always be reduced or banished from science, and that they are social and cultural products rather than merely "part of the phenomenon. " The fact that ignorance is negotiable and yet fundamental to scientific work poses several important dilemmas and prospects. We may be participating in a shift from the traditional research strategies of reducing or banishing ignorance toward a deeper understanding of and greater capacity to cope with ignorance.

In this study, we examined the neural basis of decision making under different types of uncertainty that involve missing information: ambiguity (vague probabilities) and sample space ignorance (SSI; unknown outcomes). fMRI revealed that these two different types of uncertainty recruit distinct neural substrates: Ambiguity recruits the left insula, whereas SSI recruits the anterior cingulate cortex, bilateral inferior parietal cortex, and the lateral orbitofrontal cortex. The finding of unique activations for different types of uncertainty may not necessarily be predicted within the reductive approach of modern theories of decision making under uncertainty, because these theories purport that humans reduce more complicated uncertain environments to subjectively formed less complicated ones (i.e., SSI to ambiguity). The predictions of the reductive view held only for ambiguity-averse individuals and not for ambiguity-tolerant individuals. Consequently, theories of decision making under uncertainty should include individual tolerance for missing information and how these individual differences modulate the neural systems engaged during decision making. Supplemental materials for this article may be downloaded from http://cabn.psychonomic-journals.org/content/supplemental.

This paper presents an investigation into marginalizing racism, a form of prejudice whereby ingroup members claim that specific individuals belong to their group, but also exclude them by not granting them all of the privileges of a full ingroup member. One manifestation of this is that perceived degree of outgroup membership will covary negatively with degree of ingroup membership. That is, group membership may be treated as a zero-sum quantity (e.g., one cannot be both Australian and Iraqi). Study 1 demonstrated that judges allocate more zero-sum membership assignments and lower combined membership in their country of origin and their adopted country to high-threat migrants than low-threat migrants. Study 2 identified a subtle type of zero-sum reasoning which holds that stronger degree of membership in one's original nationality constrains membership in a new nationality to a greater extent than stronger membership in the new nationality constrains membership in one's original nationality. This pattern is quite general, being replicated in large samples from four nations (USA, UK, India, and China). Taken together, these studies suggest that marginalizing racism is more than a belief that people retain a "stain" from membership in their original group. Marginalizing racism also manifests itself as conditional zero-sum beliefs about multiple group memberships.

Uncorrectable skew and heteroscedasticity are among the "lemons" of psychological data, yet many important variables naturally exhibit these properties. For scales with a lower and upper bound, a suitable candidate for models is the beta distribution, which is very flexible and models skew quite well. The authors present maximum-likelihood regression models assuming that the dependent variable is conditionally beta distributed rather than Gaussian. The approach models both means (location) and variances (dispersion) with their own distinct sets of predictors (continuous and/or categorical), thereby modeling heteroscedasticity. The location sub-model link function is the logit and thereby analogous to logistic regression, whereas the dispersion sub-model is log linear. Real examples show that these models handle the independent observations case readily. The article discusses comparisons between beta regression and alternative techniques, model selection and interpretation, practical estimation, and software.

In previous work we used a conceptualization of double standards to develop a theory explaining how similar performances may lead to different levels of attributed ability when performers belong to different status groups (e.g., gender, ethnicity). The theory predicts that the effect of double standards will be reduced when objective standards are presented explicitly. Using the dyadic influence paradigm from status characteristics research, we tested three hypotheses: (1) In the absence of performance information, people hold higher performance expectations for males than for females on a male task; (2) contradictory information on performance with explicit standards for ability cancels the effect of gender-based expectations; and (3) the size of the performance difference (magnitude of contradiction) is related to the magnitude of difference in expectations. We measured expectation advantage/disadvantage both directly and through its effect on behavior, namely rejection or acceptance of influence. The results support the hypotheses.

In task-oriented groups, people accept influence more from others whom they believe, on the basis of diffuse and specific status characteristics as well as prior performances, to have greater ability at the task. Past research has treated difference in ability as a binary variable (better or worse); it is not known whether magnitude of difference in performances translates into relative magnitude of inferred ability, and thence into degrees of differentiation in the status structure of decision-making groups. We conducted an experiment to examine the relative impact of three aspects of task performance on the inference of ability: absolute level of performance, relative performance (better/worse), and the degree of difference in performances by two group members. These variables then were used to predict acceptance of influence in a two-person decision task. Simple binary difference in ability explained 35 per cent of the variance in influence accepted. Degree of difference in ability significantly increased level of prediction, particularly for subjects in the lower range of performance scores, thus supporting the claim that relative and absolute levels of performance create additional differences in expectations. Finally, we propose that a concept of graded status characteristics should be incorporated into status characteristics theory.

Some extensions are proposed of the Taagepera-Rav generalized nth power index of concentration to the multivariate case. These are compared for their relative advantages in a variety of analytical contexts. The concepts and tools used in de veloping these eextensions are then employed in the derivation of indexes for pair- wise relative inequality between owners. Finally, the problem of the relationship between inequality and concentration is approached through the development of a concentration index which measures the degree of concentration relative to the level of inequality in the resource system. These various indexes are then put through their paces in an empirical example.

The sociology of knowledge and related work in social psychology have been biased towards overvaluing shared perspectives and the attainment of certainty. This paper moves to fill a theoretical gap created by relative inattention to the roles of nonshared perspectives and uncertainty by outlining a middle-range theory of the connections between human interests and uncertainty. It is proposed that individuals and groups find instrumental uses for uncertainty, just as they do for other states of mind, and that these uses arise from particular interests which characteristically belong to certain types of individuals or groups.

The objective of this article is to examine the nature of individual and social responses to the nuclear threat from psychological and sociological perspectives on ignorance. It is argued that a constructed and managed ignorance concerning the nuclear threat serves many functions, structuring an individual and social reality which is reassuring, meaningful, and both individually and collectively self-serving. A sociology of ignorance framework is employed to articulate the possible benefits of “not knowing about” and collaboratively “not dealing with” the nuclear threat, as well as to define the longer-term costs of ignoring this threat. The distinctive roles played by various kinds of ignorance regarding this important issue are investigated, and the conventional wisdom that knowledge of the consequences of a nuclear war is the only way to prevent its occurrence is challenged.

Summary The perception of appropriate self-disclosure was examined as a function of sex of the interactants, intimacy, and valence of self-disclosure. Dependent variables investigated in addition to perceived appropriateness were perceived friendliness, maturity, warmth, psychological adjustment, openness, emotional stability, and sensitivity of the discloser; and mutual liking of, and desire for future contact with, the discloser. Normative evaluations of male or female confederate disclosure where intimacy level and valence had been experimentally manipulated, were provided by 64 male and 64 female volunteer undergraduate Ss, who were randomly assigned to the 16 cells of a 2(sex of discloser/confederate) × 2(sex of recipient/subject) × 2(intimacy) × 2(valence) factorial design. Interpersonal evaluations of the discloser occurred in the context of a structured acquaintance session. Female recipients were more receptive to disclosure thus viewing it as appropriate. Positive disclosure in contrast to negat...

Mathematics (and therefore statistics) is a language, albeit less complex than natural languages. Its main functions in scientific research have been twofold: first, to enhance the capacity of the researcher to handle logical complexity; and second, as a means to represent explicitly and accurately, the phenomena under study. These two functions are often interlinked, with the second being a necessary but not always sufficient condition for the fulfilment of the first. This paper is concerned mainly with the problem of constructing formal models that correspond to the researcher's own hypotheses, which are often formulated in natural-language statements. This problem may be summarized by saying that traditional statistical models in the behavioural sciences have not properly operationalized commonly used modifiers, conjunctions and predicates. In the move from qualitative to quantitative language, conventional techniques frequently distort or entirely lose certain meanings. This problem is discussed in detail, after which approaches to its solution are outlined and illustrated with empirical examples. The first requirement is a conceptual basis for discussing translations between mathematical and natural languages. I have chosen a simple partsof-speech approach which links certain common mathematical operators with their natural-language counterparts. These links are, of course, only approximate, but sufficiently accurate for the purposes of this presentation. (1) Nouns and variables. In verbal hypotheses, variables are usually the major nouns in the phrases which make up such hypotheses. This paper does not deal with the problems of operationalizing such nouns: these issues already occupy a large and thoroughgoing literature. (2) Modifiers and variable transformations. Variable transformations modify variables, and thus occupy approximately the same position in mathematical languages that adjectives and adverbs do in natural languages. Any translation from a verbal hypothesis which includes ordinary modifiers should therefore incorporate mathematical versions of these modifiers. Con

Until recently, ignorance and uncertainty were neglected topics in the human sciences and even in philosophy. Even now, they do not share center stage with knowledge in those disciplines, but remain sideshow oddities for the most part. Instead, Western intellectual culture has been preoccupied with the pursuit of absolutely certain knowledge or, barring that, the nearest possible approximation to it. This preoccupation is worth investigating, since it appears to be responsible not only for the neglect of ignorance, but also the absence of a conceptual framework for seriously studying it. Accordingly, in this section I will briefly outline the orientations in Western science and philosophy which have motivated the neglect of ignorance. I will also take the position that these orientations have begun to change recently, and that one of the spinoffs has been an unprecedented interest in ignorance, uncertainty, and related topics.

Publisher Summary Psychologists have relied almost exclusively on statistical models and methods for the quantitative analysis of human behavior. Fuzzy logic and possibility theory offer an alternative framework which is compatible with psychological explanations that permit choice under partial and uncertain constraints. This chapter summarizes the framework and demonstrates its application to research and theory construction in psychology. The main components of the framework are “weak” prediction and entailment models provided by fuzzy logic, and the modeling of domains of choice via possibility theory. Possibility theory is proposed as a basis for an alternative to the conventional statistical paradigm employed by researchers in psychology. The mathematical legacy bequeathed to psychology is dominated by the Neyman–Pearson–Fisher (NPF) statistical framework, which bears recognizable traces of its origins in military strategic and decision analysis, industrial quality control, and agricultural experimentation. The NPF is designed to solve the problems posed in those fields, not the human sciences; however it has been adopted almost without modification by psychologists.

If there is any approach to ignorance that bears a creditable claim to generalizability and rationality simultaneously, it is probability. Virtually all modern accounts of uncertainty refer to the concept and theory of probability as a benchmark. However, it is crucial to realize that probability theory actually consists of a cluster of competing approaches, each of which claims either exclusive or universal status as the one true theory. The differences among these theories are considered fundamental by many probabilists, especially the division between the so-called ‘objective’ and ‘subjective’ approaches.

Ignorance has been a marginal and neglected topic in the social sciences, as is the case in cognate disciplines. Indeed, most of what mainstream social science says about ignorance is merely implicit in its outpourings about knowledge. As for direct statements about ignorance or even uncertainty themselves, at best, one could say there is a fragmentary literature that is loosely held together by common themes. This state of affairs might seem to justify ignoring altogether whatever contributions sociologists, social psychologists, and anthropologists may have made in this area. However, this literature has important redeeming features in that it discusses several aspects of ignorance that are not effectively covered in the perspectives we have reviewed so far. Moreover, at least some social scientists bring to their commentary philosophical perspectives that differ in crucial ways from those that inform either applied mathematicians or cognitive psychologists.

Without attempting a thoroughgoing survey of classical philosophy, some initial insights may be gained into Western intellectuals’ traditional orientations toward ignorance by briefly examining the varieties of ignorance referred to in classical philosophical literature. Every theory of knowledge draws a distinction between knowledge and ignorance, and most between ignorance in the sense of incomplete knowledge and ignorance in the sense of erroneous belief. The earliest forms of philosophy amount to various kinds of dogmatism or skepticism, both of which necessarily refer to and utilize ignorance. I will discuss dogmatism first.

The field of psychology is ostensibly concerned with explaining human thought, emotion, and behavior, and we may properly turn to it when seeking explanations of how individuals perceive and respond to ignorance. However, traditionally psychology also has provided clear normative messages concerning ignorance (and especially uncertainty), of sufficient consistency across theories and schools of thought that they qualify as a dominant ideology. Those messages, in turn, are derived from frameworks which have crucially influenced psychological research and theories on these topics, as have debates over the relationship that should obtain between normative and explanatory frameworks. This chapter is an attempt to construct an overview of the interactions between the normative and the explanatory in the psychological literature which addresses ignorance. Once again, the reader should not expect an exhaustive treatment; preference has been given to the modern mainstreams of theory and research and a comparative synthesis.

What are we to make of the recent intellectual ferment over ignorance? Clearly its explanation presents a fascinating problem in social psychological history, and the social psychological study of science in particular. While we are far from being able to obtain a complete account, I should like to suggest what such an account might look like. After all, this problem should hold more than the promise of intellectual interest; the more we know about why we are collectively and individually preoccupied with ignorance, the more able we will be to adopt mindful strategies and choices regarding ignorance.

The modern orientation towards ignorance contrasts starkly with traditional approaches. In the older view there is no room even for irreducible uncertainty, and all intractable forms of ignorance are banished from analysis. The modern view, on the other hand, in the words of Renee Fox (1980: 9) sees “errors and mistakes, as well as uncertainty and chance, as perennial parts of the… human condition.” Nor is this view consensual or stationary. At the very least, the past two decades have seen a dramatic increase in public awareness of uncertainty and fundamental challenges to traditional methods for coping with it. Concurrently, applied mathematicians, scientists, engineers, and philosophers have questioned the exalted position of probability theory as the dominant formalism for analyzing uncertainty.