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Over the years, policy learning has emerged as a theoretical lens to provide an alternative understanding of powering-based approaches to the policy process and policy outcomes. This has been a success story, with policy learning gaining presence among theoretical frameworks on the policy process. However, the puzzling-powering dichotomy is reductive. In real-world policy processes, we find both puzzling and powering. Also, we cannot reduce the value of learning to what is not explained by power alone. This Introduction shows how puzzling and powering interact and with what consequences on policy processes, moving beyond the dichotomy. We present how the four contributions of this Special Issue explore this territory and suggest how to approach the design of governance architectures for learning. [ABSTRACT FROM AUTHOR]

Physicians' empathy and ambiguity tolerance have recently become a focus of medical education. However, the association between the two concepts remains unclear. This study examined the association between empathy and ambiguity tolerance in the clinical context among medical trainees. We conducted a multicenter cross-sectional study in 12 institutions: 2 universities for medical students and 10 hospitals for residents. We assessed ambiguity tolerance using the Japanese version of the Tolerance of Ambiguity in Medical Students and Doctors scale. The outcome variable was empathy, measured using the Japanese translation of the Jefferson Scales of Empathy (JSE). Data from 100 medical students and 135 residents were analyzed. After adjustment for possible confounders, the factor scores of 'tolerance for things that are not black or white in medicine' showed a dose-dependent association with the JSE. There was no clear trend in the association between the total scores or other factor scores and empathy. This nationwide multicenter study showed that the factor scores of 'tolerance for things that are not black or white in medicine' were associated with empathy among medical trainees. Our findings may be helpful for developing interventions in the field of medical education to nurture empathy. [ABSTRACT FROM AUTHOR]

When it comes to cybersecurity incidents – public opinion matters. But how do voters form opinions in the aftermath of cyberattacks that are shrouded in ambiguity? How do people account for the uncertainty inherent in cyberspace to forge preferences following attacks? This article seeks to answer these questions by introducing an uncertainty threshold mechanism predicting the level of attributional certainty required for the public to support economic, diplomatic or military responses following cyberattacks. Using a discrete-choice experimental design with 2025 US respondents, we find lower attributional certainty is associated with less support for retaliation, yet this mechanism is contingent on the suspected identity of the attacker and partisan identity. Diplomatic allies possess a reservoir of good will that amplifies the effect of uncertainty, while rivals are less often given the benefit of the doubt. We demonstrate that uncertainty encourages the use of cognitive schemas to overcome ambiguity, and that people fall back upon pre-existing and politically guided views about the suspected country behind an attack. If the ambiguity surrounding cyberattacks has typically been discussed as an operational and strategic concern, this article shifts the focus of attention to the human level and positions the mass public as a forgotten yet important party during cyber conflict. [ABSTRACT FROM AUTHOR]

Language is closely connected to the concepts of uncertainty and indeterminacy, as it functions as a fundamental tool for the expression and communication of information. Linguistic formulations possess inherent qualities of ambiguity, imprecision, and vagueness. The comprehension of language frequently hinges upon contextual factors, individual interpretation, and subjective viewpoints, resulting in ambiguities in comprehension. Neutrosophic-linguistic valued hypersoft sets (N-LVHS) play a pivotal role in decision-making by effectively managing linguistic uncertainty, modeling real-world complexity, and accommodating multidimensional information. In the realm of medical diagnosis and treatment, several limitations tied to language and indeterminacy persist. Patients often use vague or imprecise language to describe their symptoms, complicating the accurate identification of ailments. Moreover, diagnostic criteria are subjectively defined, leading to inconsistencies in diagnoses. Disease progression, characterized by its complexity and unpredictability, adds further indeterminacy in treatment planning. The variability in patient responses to treatments introduces uncertainties in outcome prediction. Inconclusive test results and limited clinical data may compound these challenges, underscoring the need for innovative approaches like N-LVHS to address these linguistic and indeterminacy-related limitations and improve the precision and efficacy of medical decision-making and treatment procedures. In constructing an N-LVHS framework for medical diagnosis and treatment, relevant factors, and linguistic terms characterizing medical conditions and treatments are identified. For example, disease severity could be described using terms such as "mild," "moderate," and "severe," while treatment effectiveness may be categorized as "low," "moderate," and "high." Each factor is then assigned neutrosophic values based on their measured impacts. This approach provides a more precise representation of the complex medical diagnostic and treatment landscape. The findings of this study have the potential to assist medical practitioners, researchers, and policymakers in optimizing medical diagnosis and treatment strategies, enhancing patient outcomes, and improving healthcare practices. [ABSTRACT FROM AUTHOR]

Our symposium was not a stage for success narratives but for contradictions and illusions, obstacles and fears associated with sustainable action. We addressed them in a multi-perspective, discursive and interactive way. Everyone could feel that inter- and transdisciplinary experiences are borderline experiences, in finding roles and ways of working, communication and language, and in implementing ideas and insights. This is associated with uncertainty, ambiguity and disagreement, which require a holistic and equally sensitive approach to transitions and differentiation processes. With our symposium, we ventured into these border crossings. Everyone was a visible seeker, there were handshakes across disciplinary boundaries and shared stuttering. Making the blind spots visible also revealed that sustainability is not an abstract objective or method so much as a process for personal and existential maturation. [ABSTRACT FROM AUTHOR]

Although uncertainty and ambiguity have been used interchangeably within the anxiety literature, no studies have directly compared the psychometric properties and predictive utility of two commonly used scales to assess these constructs, the Intolerance of Uncertainty Scale (IUS) and the Tolerance of Ambiguity Scale (TOA). Furthermore, previous findings of the individual scales have been inconsistent regarding their factor structure and potential clinical utility. Therefore, the current study aims to independently evaluate the factor structure of the IUS and TOA utilizing exploratory and confirmatory factor analysis. Additionally, predictive utility of the scales was evaluated with correlation and regression analyses. Data were collected across 3 studies. Results suggest two-factor solutions provide the best-fitting models for both scales; however, these findings differ from previous studies. The IUS and its subscales provide greater predictive utility for anxiety and related areas of psychopathology compared to the TOA, suggesting that the IUS and uncertainty may be more closely related to anxiety and related symptoms. Future studies should continue to examine the interconnected role of uncertainty and ambiguity within anxiety and other forms of psychopathology. [ABSTRACT FROM AUTHOR]

A major concern with Bayesian decision making under uncertainty is the use of a single probability measure to quantify all relevant uncertainty. This paper studies prior robustness as a form of continuity of the value of a decision problem. I show that this notion of robustness is characterized by a form of stable choice over a sequence of perturbed decision problems, in which the available acts are perturbed in a precise fashion. I then introduce a choice-based measure of prior robustness and apply it to models of climate mitigation and portfolio choice. • Belief Robustness and Continuity: Beliefs are "robust" if minor changes in beliefs lead to minor changes in choices. This notion can be tested by evaluating perturbations of feasible bets. • Measure of Robustness: A comparative measure of belief robustness can quantify the sensitivity of optimal choices to parametric assumptions. • Impact on Key Economic Models: The measure shows that heavy-tailed distributions yield more robust choices in portfolio and economy-climate models, highlighting its practical importance. [ABSTRACT FROM AUTHOR]

Complexity of the problem of choosing among uncertain acts is a salient feature of many of the environments in which departures from expected utility theory are observed. I study a class of Generalized Simple Bounds preferences in which acts that are complex from the perspective of the decision maker are bracketed by "simple" acts to which they are related by statewise dominance. I then study a refinement of the model in which the size of the partition with respect to which an act is measurable arises endogenously as a measure of subjective complexity. Finally, I consider choice behavior characterized by a "cautious completion" of Simple Bounds preferences, and discuss the relationship between this model and models of ambiguity aversion. I develop general comparative statics results, and explore applications to portfolio choice and insurance choice. • Problems of choice problems under uncertainty are complex. • From behavioral axioms, I characterize the set of acts that are well-understood. • Decision makers use acts they understand well to bracket those they do not. • The model is tractable, and admits rich comparative statics. • The model helps explain observed choice patterns in applications. [ABSTRACT FROM AUTHOR]

Because schools as educational institutions have the task of empowering today’s students (as future citizens) to lead their lives responsibly, current social changes (such as digitalisation) suggest that uncertainty may be a particularly important dimension of educational activities. In this manuscript, a systematic review of 43 references dealing with the topic of uncertainty in the field of physical education (PE) is presented. As a result, one can identify three dimensions of the phenomenon of uncertainty: (1) uncertainty as an opportunity to foster processes of deep learning (Bildung) among students in the PE classroom, (2) uncertainty as a challenge for PE teachers and preservice teachers and (3) uncertainty as a characteristic of movement, games and sports. The paper discusses the consequences of these findings for teaching and teacher education in PE. In distinguishing these three dimensions of the phenomenon of uncertainty in the field of one subject (here PE), the article suggests a structure for analysing uncertainty in other subjects, as well as with respect to wider fields of research such as research on professionalism or school development. [ABSTRACT FROM AUTHOR]

Abstract Forest-based communities are embedded in complex and uncertain global processes. We aim to understand the repercussions of uncertainty on forest-based livelihoods through a focus on implications in terms of access to resources for inhabitants of forest concessions. Uncertainty is conceptualized as unpredictability (not knowing), limited knowledge (knowing too little), and ambiguity (knowing too much, too differently). This research is based on document analysis and a case study in Zambezia Province, Mozambique. In-depth interviews and observations were conducted in local communities and triangulated with interviews with NGOs, researchers, and government officials. The findings indicate that multiple actors draw on formal and informal institutions to mediate access to forest-derived resources, which exacerbates uncertainties. Unpredictability, limited knowledge, and ambiguity undermine access to such resources for local communities. This paper contributes to an understanding of uncertainty and suggests a collaborative approach to reduce and cope with different uncertainties in the quest toward sustainable forest-based livelihoods. [ABSTRACT FROM AUTHOR]

In order to deploy robots that could be adapted by non-expert users, interactive imitation learning (IIL) methods must be flexible regarding the interaction preferences of the teacher and avoid assumptions of perfect teachers (oracles), while considering they make mistakes influenced by diverse human factors. In this work, we propose an IIL method that improves the human–robot interaction for non-expert and imperfect teachers in two directions. First, uncertainty estimation is included to endow the agents with a lack of knowledge awareness (epistemic uncertainty) and demonstration ambiguity awareness (aleatoric uncertainty), such that the robot can request human input when it is deemed more necessary. Second, the proposed method enables the teachers to train with the flexibility of using corrective demonstrations, evaluative reinforcements, and implicit positive feedback. The experimental results show an improvement in learning convergence with respect to other learning methods when the agent learns from highly ambiguous teachers. Additionally, in a user study, it was found that the components of the proposed method improve the teaching experience and the data efficiency of the learning process. [ABSTRACT FROM AUTHOR]

Uncertainty is prevalent in various health contexts. It is imperative to understand how health-related uncertainty can impact individuals' healthcare experiences and health decision making. The purpose of the present paper is to provide five overarching recommendations from an interdisciplinary team of experts to address gaps in the literature on health-related uncertainty. We present a case study of health-related uncertainty within the specific context of alcohol use to demonstrate these gaps and provide context for the recommendations. The five recommendations concerning health-related uncertainty include: (1) use common, consistent terminology to discuss uncertainty, (2) clarify measures of individual differences in response to uncertainty, (3) increase research on uncertainty and affect, (4) investigate the impact of the channel through which uncertainty is communicated, and (5) develop theory-driven interventions to improve uncertainty management. We conclude by reviewing health contexts in which health-related uncertainty exists and note how our recommendations complement existing reviews and data. [ABSTRACT FROM AUTHOR]

Background: The interval between adolescence and adulthood, 'emerging adulthood' (EA), lays the foundation for lifelong health and well‐being. To date, there exist little empirical data – particularly in the neurobiological domain – to establish markers of risk and resilience during the transition to adulthood. This gap in the literature is concerning given the numerous forms of psychiatric illness that emerge or worsen during this period. Methods: In this review, we focus on two strands of research with distinct importance for EA: reward sensitivity, and tolerance of ambiguity. We begin by placing these domains in a framework that considers the unique developmental goals of EA and then synthesize emerging neurobiological research on how these domains develop during EA. We then consider their role in common mental health problems that occur during this interval as well as how social support may moderate outcomes. Finally, we offer recommendations for advancing research to understand developmental process and outcomes in EA. Findings and conclusions: Few longitudinal studies specifically address emerging adult development and the milestones that characterize this interval. Data on neurobiological development are similarly sparse. Understanding neurobiological development during this window and its links to key adjustment outcomes is crucial for optimizing outcomes. [ABSTRACT FROM AUTHOR]

Risk and uncertainty are central concepts of decision neuroscience. However, a comprehensive review of the literature shows that most studies define risk and uncertainty in an unclear fashion or use both terms interchangeably, which hinders the integration of the existing findings. We suggest uncertainty as an umbrella term that comprises scenarios characterized by outcome variance where relevant information about the type and likelihood of outcomes may be somewhat unavailable (ambiguity) and scenarios where the likelihood of outcomes is known (risk). These conceptual issues are problematic for studies on the temporal neurodynamics of decision-making under risk and ambiguity, because they lead to heterogeneity in task design and the interpretation of the results. To assess this problem, we conducted a state-of-the-art review of ERP studies on risk and ambiguity in decision-making. By employing the above definitions to 16 reviewed studies, our results suggest that: (a) research has focused more on risk than ambiguity processing; (b) studies assessing decision-making under risk often implemented descriptive-based paradigms, whereas studies assessing ambiguity processing equally implemented descriptive- and experience-based tasks; (c) descriptive-based studies link risk processing to increased frontal negativities (e.g., N2, N400) and both risk and ambiguity to reduced parietal positivities (e.g., P2, P3); (d) experience-based studies link risk to increased P3 amplitudes and ambiguity to increased frontal negativities and the LPC component; (e) both risk and ambiguity processing seem to be related with cognitive control, conflict monitoring, and increased cognitive demand; (f) further research and improved tasks are needed to dissociate risk and ambiguity processing. [ABSTRACT FROM AUTHOR]

Humans globally are reaping the benefits of longer lives. Yet, longer life spans also require engaging with consequential but often uncertain decisions well into old age. Previous research has yielded mixed findings with regards to life span differences in how individuals make decisions under uncertainty. One factor contributing to the heterogeneity of findings is the diversity of paradigms that cover different aspects of uncertainty and tap into different cognitive and affective mechanisms. In this study, 175 participants (53.14% females, mean age = 44.9 years, SD = 19.0, age range = 16 to 81) completed functional neuroimaging versions of two prominent paradigms in this area, the Balloon Analogue Risk Task and the Delay Discounting Task. Guided by neurobiological accounts of age-related changes in decision-making under uncertainty, we examined age effects on neural activation differences in decision-relevant brain structures, and compared these across multiple contrasts for the two paradigms using specification curve analysis. In line with theoretical predictions, we find age differences in nucleus accumbens, anterior insula, and medial prefrontal cortex, but the results vary across paradigm and contrasts. Our results are in line with existing theories of age differences in decision making and their neural substrates, yet also suggest the need for a broader research agenda that considers how both individual and task characteristics determine the way humans deal with uncertainty. [ABSTRACT FROM AUTHOR]

Uncertainty permeates decisions from the trivial to the profound. Integrating brain and behavioral evidence, we discuss how probabilistic (varied outcomes) and temporal (delayed outcomes) uncertainty differ across age and individuals; how critical tests adjudicate between theories of uncertainty (prospect theory and fuzzy-trace theory); and how these mechanisms might be represented in the brain. The same categorical gist representations of gains and losses account for choices and eye-tracking data in both value-allocation (add money to gambles) and risky-choice tasks, disconfirming prospect theory and confirming predictions of fuzzy-trace theory. The analysis is extended to delay discounting and disambiguated choices, explaining hidden-zero effects that similarly turn on categorical distinctions between some gain and no gain, certain gain and uncertain gain, gain and loss, and now and later. Bold activation implicates dorsolateral prefrontal and posterior parietal cortices in gist strategies that are not just one tool in a grab-bag of cognitive options but rather are general strategies that systematically predict behaviors across many different tasks involving probabilistic and temporal uncertainty. High valuation (e.g., ventral striatum; ventromedial prefrontal cortex) and low executive control (e.g., lateral prefrontal cortex) contribute to risky and impatient choices, especially in youth. However, valuation in ventral striatum supports reward-maximizing and gist strategies in adulthood. Indeed, processing becomes less "rational" in the sense of maximizing gains and more noncompensatory (eye movements indicate fewer tradeoffs) as development progresses from adolescence to adulthood, as predicted. Implications for theoretically predicted "public-health paradoxes" are discussed, including gist versus verbatim thinking in drug experimentation and addiction. [ABSTRACT FROM AUTHOR]

Decision-makers often are faced with uncertain situations in which they have incomplete information. While risky decisions include the probabilities of the possible outcomes, ambiguous decisions involve both unknown probabilities and unknown outcomes. Prior research has suggested that there are differences in how men and women evaluate risk, but evidence related to gender and ambiguity is mixed. The present work approaches this problem from a novel angle, focusing on the use of information that is present rather than the impact of information that is absent. It examines how individuals assign value in uncertain decisions based on the partial information they do have. While a main effect of gender on value is not observed, there is an enhanced "optimism bias" in how both favorable and unfavorable information influences the subjective value of ambiguous financial prospects for male compared to female participants. Unpacking these effects suggests multiple mechanisms, including a significant contribution of risk processing. Specifically, favorable and unfavorable information are over- and underweighted respectively in male participants' estimated likelihood of a winning outcome, and unfavorable information is underweighted in estimating certainty. There also is an interaction of gender and risk preferences, such that value increases more for male participants as the subjectively estimated likelihood of winning increases. A second experiment demonstrates this risk interaction effect is also observed for objective probabilities of winning, suggesting that the relationship between value and risk uses similar mechanisms across layers of uncertainty. [ABSTRACT FROM AUTHOR]

Adolescence is a period of development in which youth have new opportunities for decision-making, often in situations where they may have little information or experience to guide their choices. Thus, learning to make decisions under uncertainty is a key challenge during adolescence. To date, researchers have applied economics formalisms to understand the processes that support adolescents in making decisions under two distinct forms of uncertainty: economic risk and economic ambiguity. Economic risk is when the probabilities of outcomes are known. Economic ambiguity is when the probabilities of outcomes are unknown or unknowable. This research has led to foundational knowledge about the basic processes involved in adolescent decision-making, but many experimental paradigms that dissociate economic risk and ambiguity rely on monetary or point-based choices. Given that adolescence is a period of development characterized by a changing social environment, it remains unclear whether the processes that adolescents engage during decision-making on monetary or point-based experimental tasks generalize to their day-to-day experiences in the real world. In this brief piece, we explore how developmental research applying economics formalisms can be bolstered by research on youth's social environments to advance our understanding of decision-making in adolescence. First, we review developmental research by using economic uncertainty paradigms. Next, we highlight research on adolescents' social environments to provide examples of the day-to-day choices that adolescents face among their peers and in their broader communities. Finally, we propose directions for future research integrating these separate approaches to create a more nuanced, ecologically informed understanding of adolescent decision-making. [ABSTRACT FROM AUTHOR]

Non‐linear inversion of controlled source electromagnetic data is non‐unique. Inversion ambiguity and uncertainty grow with model complexity and limitations in sensitivity, for example when imaging deep targets. Uncertainties should be estimated. The best way to do that is by statistical inversion techniques. Because of the large number of parameters of 3D models, these methods are too costly for 3D applications. But neglecting or underestimating uncertainties often leads to erroneous interpretation and poor exploration decisions. The number of inversion parameters can be dramatically reduced while still being able to describe a complex 3D subsurface. To obtain that, I represent prior information by an input ensemble of geologically reasonable prior models. The model space is defined by the principal directions of this ensemble, where the inversion parameters are the coefficients of the different components. In this model space, inversion becomes much more efficient and can produce an updated ensemble of posterior models at the same computational cost as a controlled‐source electromagnetic inversion project using conventional non‐linear inversion techniques. I present examples where unique Gauss–Newton inversions lead to difficult interpretations and poor conclusions about hydrocarbon presence and saturation. The new approach provides a quantitative estimation of resistivity ambiguities and uncertainties leading to better interpretation and safer decisions. The examples use 2D synthetic controlled‐source electromagnetic data and 3D controlled‐source electromagnetic field data. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the effects of Information Theory measures on the processing of polysemous nouns and reveal that the sensitivity to multiple related senses can be learned from the linguistic context. We collected large-scale data and applied a correlation design to show that an increase in sense uncertainty (or sense diversity) is followed by a faster visual lexical decision. The facilitatory effect of sense uncertainty was revealed by the predictive power of entropy, followed by the additional analysis, which revealed that both the number of senses and the balance of sense probabilities affected processing. For the first time, the balance of sense probabilities was described via redundancy to demonstrate the effect of the numerical description of the balance of sense probabilities. Finally, we crossed distribution semantics and discrimination learning to show that polysemy effects can arise as a consequence of the principles of error-driven learning. [ABSTRACT FROM AUTHOR]

The COVID-19 crisis has exposed the public to considerable scientific uncertainty, which may promote vaccine hesitancy among individuals with lower tolerance of uncertainty. In a national sample of US adults in May–June 2020, we examined how both perceptions of uncertainty about COVID-19 and trait-level differences in tolerance of uncertainty arising from various sources (risk, ambiguity, and complexity) are related to vaccine hesitancy-related outcomes, including trust in COVID-19 information, COVID-19 vaccine intentions, and beliefs that COVID-19 vaccines should undergo a longer testing period before being released to the public. Overall, perceptions of COVID-19 uncertainty were not associated with trust in information, vaccine intentions, or beliefs about vaccine testing. However, higher tolerance of risk was associated with lower intentions to get vaccinated, and lower tolerance of ambiguity was associated with lower intentions to get vaccinated and preferring a longer period of vaccine testing. Critically, perceptions of COVID-19 uncertainty and trait-level tolerance for uncertainty also interacted as predicted, such that greater perceived COVID-19 uncertainty was more negatively associated with trust in COVID-19 information among individuals with lower tolerance for risk and ambiguity. Thus, although perceptions of uncertainty regarding COVID-19 may not reduce trust and vaccine hesitancy for all individuals, trait-level tolerance of uncertainty arising from various sources may have both direct and moderating effects on these outcomes. These findings can inform public health communication or other interventions to increase COVID-19 vaccination uptake. [ABSTRACT FROM AUTHOR]

In the article "What are neural correlates neural correlates of?" published in the journal BioSocieties, Gabriel Abend points out that neuroscientists cannot avoid philosophical questions concerning the conceptualization and operationalization of social-psychological phenomena they deal with at the physiological level. In this article, we build on Abend's thesis and, through a systematic literature review of decision neuroscience studies, test it with the example of the social-psychological phenomenon of uncertainty in decision making. In this paper, we provide an overview of studies that appropriately attempt to conceptualise uncertainty, and then use these studies to analyse papers looking for neural correlates of uncertainty. Based on a systematic review of studies, we investigate what types of uncertainty authors in the field of decision neuroscience address and define, what criteria they use to distinguish between these types, what problems are associated with their conceptualization, and whether the neural correlates of different types of uncertainty can be accurately identified. The paper concludes that, particularly in the economic context, a collaboration between the natural and social sciences works well, and neuroscience studies use economic conceptualizations of uncertainty that are further developed by sophisticated decision tasks. However, the paper also highlights problematic aspects that obscure the understanding of the phenomena under study. These include the lack of criteria for distinguishing between different types of phenomena, the unclear use of the general concept of uncertainty, and the confusion of phenomena or their erroneous synonymous use. [ABSTRACT FROM AUTHOR]

Conspiracy beliefs are commonly seen during times of uncertainty. This study examined whether a chatbot offering counter-conspiracy information can mitigate conspiracy beliefs and the role of chatbot empathy on its effectiveness. We conducted an online experiment in two different contexts (climate change vs. Covid-19) (N = 189). The results showed that as for Covid-19, participants who interacted with the chatbot with less empathetic expressions showed fewer changes in conspiracy beliefs than those who read the scientific news article. Regarding climate change, a chatbot with more empathetic expressions was more effective in changing conspiracy beliefs than an article, but only for people who can tolerate ambiguity. [ABSTRACT FROM AUTHOR]

Background & Aims: Among psychological issues, anxiety is a "futuristic state of mind" in which a person is ready or preparing for the negative events of the future, indicating that there is a distinction between present and future dangers that alienate fear. Separates from anxiety (2). In fact, normal reaction anxiety is considered to be stress that may help a person cope with difficult situations. When anxiety is excessive, it may be so great that it is placed in the categories associated with anxiety disorders (2). Another variable that can be different in different situations between individuals and students is the individual self-efficacy of students. Self-efficacy refers to a person's personality strength in dealing with problems in achieving his goals and success, and more than it is influenced by the student's intelligence and learning ability, it is influenced by personality traits such as self-confidence, diligence and submission, self-esteem, Is self-regulation and self-direction. In the age of information explosion, cultivating selfsufficient people, people who can overcome their own anxiety and learn and solve problems independently is much more important than transferring a huge amount of information (3). Humans, on the other hand, are constantly solving problems throughout their lives. Problem solving helps people deal effectively with life's problems and challenges. When an issue is not clearly defined, ambiguity tolerance is used to construct and reconstruct solutions and interpretations. When an issue is not clearly defined, ambiguity tolerance is used to construct and reconstruct solutions and interpretations (4). Due to the above and the impact of anxiety on students' lives on the one hand, the identification of important factors and factors related to anxiety on the other hand and considering that research in the field and research was not found in the field The researcher seeks to answer the questions of whether there is a relationship between students' tolerance of ambiguity, anxiety and self-efficacy? Can tolerance of ambiguity and anxiety be able to predict anxiety in students? Methods: To conduct the present descriptive research, which is a type of basic and applied research, among male and female students of Farhangian University (former teacher training), Tehran in the academic year 1394-2014, based on multi-stage sampling of 150 people with a minimum of Undergraduate education was selected as a sample and then the questionnaires of McDonald (1970), Beck Anxiety (1998) and the self-efficacy of Scherer et al. (1982) were distributed and collected among the sample. After removing incomplete and invalid questionnaires, a total of 142 questionnaires were collected. Finally, descriptive statistics and Pearson correlation and linear regression tests were used simultaneously with the help of SPSS software for statistical analysis of data. Results: The results showed that the sum of the variables used (self-efficacy and ambiguity tolerance) predicted 18% of the variance of students' anxiety. Given that F calculated at the level less than 0.01 is significant, so the linear regression model is significant. The results of regression analysis show that self-efficacy at the level (= = 0.01) is able to significantly predict students' anxiety and t-test is significant for the significance of regression coefficients at the level less than 0.01. In other words, with increasing self-efficacy score, students' anxiety score decreases and vice versa. Given that β is the standardized coefficient of regression, it is clear that self-efficacy with an coefficient of explanation (0.393) is more effective in predicting student anxiety. Conclusion: The results showed that the variables of self-efficacy and ambiguity tolerance predict 18% of the variance of anxiety in students. Also, the results of regression analysis showed that self-efficacy is able to significantly predict students 'anxiety. In other words, with increasing self-efficacy score, students' anxiety score decreases and vice versa. It was also found that self-efficacy was more effective in predicting student anxiety. Explaining this finding, it can be said that tolerating the ambiguity of a personality variable is perceptual-emotional. In the clinical literature, ambiguity tolerance is a cognitive characteristic that can predict the risk of anxiety disorder. Tolerance of ambiguity is associated with negative anxiety and expectation about the future and is therefore always considered in research on anxiety disorders (11).Self-efficacy also refers to a person's perceived ability to perform a desired action. This means that evaluating one's abilities in a situation influences one's decision to act or avoid that situation, and because fear of judgment and negative evaluation prevents anxious people from displaying their abilities, The person is drawn to avoidance, passivity, and ultimately the inability to perform functional activities. Therefore, it is likely that a common factor in the structure of anxiety and low self-efficacy is the inability to understand individual competencies and adequacy and the misconception about the failure of activities (13). Beliefs about strong self-efficacy have been suggested as a powerful personal resource in dealing with stressful situations. Much evidence has been reported on the effect of selfefficacy beliefs on blood pressure with heart rate in heart patients. According to Bandura, self-efficacy related to one's judgments can affect one's actions in future situations. When a person is examining behavior or performing a task, he or she also makes judgments about his or her ability to perform the task and its circumstances. These judgments about self-efficacy affect a person's thinking about the ability or inability to do work and the individual's excitement and performance (14). The main focus of social cognitive theory is that self-efficacy beliefs strongly influence human performance. According to Graham and Wiener, self-efficacy is the most consistent predictor of behavioral outcomes relative to any other motivational structure (15). In predicting student anxiety. [ABSTRACT FROM AUTHOR]

Pérez-López (2018) identificó tres parámetros para evaluar la calidad de una organización: eficacia, atractividad y unidad (Ariño, 2005; Ferreiro, Alcázar, 2019). La aplicación de estos parámetros se ha llevado a cabo mayoritariamente en entornos estables, puesto que eran los entornos habituales cuando Pérez-López formuló su teoría. Las últimas décadas han estado marcadas por grandes turbulencias sociales y empresariales (Millar, Groth, Mahon, 2018). Este artículo repasa las tres dimensiones de Pérez-López para diagnosticar la calidad de una organización y sugiere pautas para su aplicación en entornos turbulentos. Pérez-López (2018) identified three parameters to evaluate the quality of an organization: efficacy, attractivity and unity (Ariño, 2005; Ferreiro, Alcázar, 2019). The practical application of these parameters has taken place mostly in stable environments, since this was the state of the world when Pérez-López developed his theories. The last decades have been characterized by big social and organizational changes and high degree of uncertainty (Millar, Groth, Mahon, 2018). This article reviews the three dimensions of Pérez-López to assess que quality of an organization and suggests some guidelines for its implementation in volatile and uncertain environments. [ABSTRACT FROM AUTHOR]

The article examines uncertainty as a category of economic science and as a characteristic of the business environment. Approaches to understanding uncertainty in the history of economic science are presented. Uncertainty as an economic category is considered a characteristic of an economic situation in which it is impossible to get an absolute idea about the internal and external conditions of the functioning of the socioeconomic system due to the ambiguity of its parameters, incompleteness, inaccuracy or contradiction of information and the influence of subjective factors, which causes the nondeterminism of economic processes, instability of economic development and economic risk. The components of the VUCA world and the BANI world are indicated, as well as the types of reactions to them in the business environment. Uncertainty is considered a norm of business functioning. [ABSTRACT FROM AUTHOR]

The memory effects of semantic attributes (e.g., concreteness, familiarity, valence) have long been studied by manipulating their average perceived intensities, as quantified in word rating norms. The semantic ambiguity hypothesis specifies that the uncertainty as well as the intensity of semantic attributes is processed when words are encoded. Testing that hypothesis requires a normed measure of ambiguity, so that ambiguity and intensity can be manipulated independently. The standard deviation (SD) of intensity ratings has been used for that purpose, which has produced three characteristic ambiguity effects. Owing to the recency of such research, fundamental questions remain about the validity of this method of measuring ambiguity and about its process-level effects on memory. In a validity experiment, we found that the rating SDs of six semantic attributes (arousal, concreteness, familiarity, meaningfulness, negative valence, positive valence) passed tests of concurrent and predictive validity. In three memory experiments, we found that manipulating rating SDs had a specific effect on retrieval: It influenced subjects' ability to use reconstructive retrieval to recall words. That pattern was predicted by the current theoretical explanation of how ambiguity benefits memory. [ABSTRACT FROM AUTHOR]

Increasing environmental crises due to climate change calls for bridging the research and operational logics of spatial planning and humanitarian response. This article explores how long-term spatial planning and short-term humanitarian responses relate to three facets of uncertainty that are particularly relevant in developmental contexts, namely epistemic uncertainty, ontic uncertainty, and ambiguity. The authors explore these facets through a case study of uncertainty, that of unexpected monsoon floods in 2018 and 2019 in Wayanad, a peri-urban hill district in Kerala, India. Through the case, they show that compounded uncertainty leads to ambiguity in action, but that this ambiguity can be ameliorated by a contextualised contingency planning approach. The authors conclude the article by outlining the approach in spatial planning that prioritises flexible and adaptable decision-making to enhance iterative organisational learning and action, as well as cross-sectoral dialogue to deal with uncertainty. [ABSTRACT FROM AUTHOR]

It can be argued that there is an ethical requirement to classify correctly what is known and what is unknown in decision situations, especially in the context of biomedicine when risks and benefits have to be assessed. This is because other methods for assessing potential harms and benefits, decision logics and/or ethical principles may apply depending on the kind or degree of uncertainty. However, it is necessary to identify and describe the various epistemic states of uncertainty relevant to such estimates in the first place. Therefore, this paper aims to develop a category system of different epistemic states of uncertainty which, although not exclusively, is primarily intended to be applied to early clinical trials. It is formed on the basis—and various combinations—of three dimensions of uncertainty that represent certain parts of incomplete knowledge: outcome (type of event), probability (of outcome) and evaluation (assessment of outcome). Furthermore, it is argued that uncertainty can arise from three different sources (the structure of the object of research, the state of the evidence, or individual handling of the research and already existing knowledge). The categories developed are applied to actual examples from gene therapy and genome editing to illustrate that they can be helpful for a more precise definition of the respective uncertainties, especially in the context of risk–benefit assessment. The categories allow a differentiated perspective of decision-making situations from the point of view of incomplete knowledge in general, but particularly, for example, in early clinical research, and may thereby support a more acceptable ethical assessment of potential harms and benefits. [ABSTRACT FROM AUTHOR]

To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model. It applies to both transmission and distribution systems and can achieve better robustness than the recent data-driven models. The key idea is to probabilistically constrain the worst-case errors through distributionally robust chance-constrained programming. It also allows guaranteeing the linearization accuracy for a chosen operating point. Comparison results with three recent LPF models demonstrate that the worst-case error of the RD-LPF model is significantly reduced over 2- to 70-fold while reducing the average error. A compromise between computational efficiency and accuracy can be achieved through different ambiguity sets and conversion methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an ambiguousness-aware state evolution (AASE) method which represents the uncertainty of the input sequence and evolves the subsequent skeletons to generate a reasonable full-length sequence for action prediction. Unlike most existing methods that enforce partial sequences with the labels of full-length videos and ignore the semantic information of the subsequent action, we develop an evolution method by predicting the instructional actions and generating the reasonable candidate subsequent actions, so that the ambiguity of the full sequence’s label supervising for the partial actions can be effectively alleviated. Our method generates the rational subsequent actions under the instructional action class to complement the partially observed action sequence. We design two criteria for a rational generation: 1) the instruction of subsequent action keeps the semantic consistency with the observed sequence; 2) the generation sequence is satisfied with the distribution of the sequence of real data. Moreover, we design an uncertainty module to decide the instructional action class for the generation network. AASE predicts instructional actions with uncertainty learning and evolves different instructional actions by generating the subsequent skeletons, which find the most probable action to represent the partially observed action by learning the way of perceiving the tendency of the ongoing action. We conduct experiments on seven widely used action datasets: NTU-60, NTU-120, UCF101, UT-Interaction, BIT, PKU-MMD and HMDB51, and our experimental results clearly demonstrate that our method achieves very competitive performance with state-of-the-art. [ABSTRACT FROM AUTHOR]

In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life applications concerning 3D data. While existing works strive to find a single solution to the pose estimation problem, we make peace with the ambiguities causing high uncertainty around which solutions to identify as the best. Instead, we report a family of poses which capture the nature of the solution space. DBN extends the state of the art direct pose regression networks by (i) a multi-hypotheses prediction head which can yield different distribution modes; and (ii) novel loss functions that benefit from Bingham distributions on rotations. This way, DBN can work both in unambiguous cases providing uncertainty information, and in ambiguous scenes where an uncertainty per mode is desired. On a technical front, our network regresses continuous Bingham mixture models and is applicable to both 2D data such as images and to 3D data such as point clouds. We proposed new training strategies so as to avoid mode or posterior collapse during training and to improve numerical stability. Our methods are thoroughly tested on two different applications exploiting two different modalities: (i) 6D camera relocalization from images; and (ii) object pose estimation from 3D point clouds, demonstrating decent advantages over the state of the art. For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify. For the latter we achieve the top results especially for symmetric objects of ModelNet dataset (Wu et al., in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1912–1920, 2015). The code and dataset accompanying this paper is provided under https://multimodal3dvision.github.io. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Decisions about one's health are often accompanied by uncertain outcomes, which may be either positively or negatively valenced. The presence of this uncertainty, which can range along a continuum from risk to ambiguity (i.e., decisions in which the outcome probabilities are known or unknown), can be perceived as threatening, and individuals tend to be averse to uncertain outcomes, attempting to avoid uncertainty when possible. We proposed that one way to reduce uncertainty aversion could be to provide opportunities to affirm one's core values, or "self‐affirmation." Prior research has suggested that self‐affirmation promotes health behavior by providing a buffer against potential threats to the self. However, the degree to which self‐affirmation affects decision making is still unclear. Across two studies, we tested the effects of a self‐affirmation manipulation on risk (Study 1) and ambiguity (Study 2) preferences for both potential gains and losses. In both studies, we found that, compared to the non‐affirmed group, affirmed individuals were more accepting of uncertainty when the decision involved potential gains, but not for potential losses. Furthermore, for risky decisions, the increased acceptance of uncertainty came at the expense of making choices consistent with the expected value, such that self‐affirmed individuals made more disadvantageous choices than non‐affirmed individuals. Our results suggest both benefits and costs of self‐affirmation in the context of risky choice, an important finding has given the many applications of self‐affirmation in behavioral decision‐making contexts. [ABSTRACT FROM AUTHOR]

Perceiving ambiguity in health information—that is, uncertainty elicited from believing information lacks credibility, reliability, or adequacy—is typically associated with pessimistic appraisals (e.g., high perceived disease risk) and behavioral avoidance. We examined the effect of ambiguous health information about COVID-19 on health cognitions and vaccination intentions, and tested a "normalized-uncertainty" intervention. Two studies with identical methodology (online adult sample: n = 299, undergraduate sample: n = 150) were conducted in March to April 2020. Participants were randomly assigned to read one of three health messages about COVID-19 that emphasized what was currently unknown (ambiguity condition), what was currently unknown but that scientific uncertainty is expected (intervention condition), or what was currently known (control condition). The ambiguity condition led to greater perceived ambiguity than the control condition and perceived ambiguity in the intervention condition was comparable to the ambiguity condition. There were few differences in health cognitions, and no differences in vaccination intentions, when examining pairwise comparisons across the three conditions. Correlational analyses collapsing across condition indicated evidence of pessimistic appraisal but not behavioral avoidance among individuals who perceived greater ambiguity. Future research should examine longer, more detailed normalized-uncertainty interventions. [ABSTRACT FROM AUTHOR]

During exploration, young children often show an intuitive sensitivity to uncertainty, despite their strong tendency towards overconfidence in their explicit judgments. Here, we examine the development of children's explicit and implicit recognition of uncertainty using the same stimuli. We presented 4‐ and 5‐year‐olds with objects that varied in their amount of perceptual occlusion, and assessed their ability to distinguish among them using two types of measures. Experiment 1 used a traditional 3‐point confidence scale to examine children's explicit uncertainty judgments. We compared these confidence judgments before and after they observed disconfirming evidence, to assess the impact of this experience on their acknowledgement of uncertainty in later trials. Experiment 2 examined children's exploration preference as a measure of implicit sensitivity to uncertainty. Our results indicate that children intuitively recognize gaps in their knowledge, and that this implicit recognition may be leveraged to support their explicit judgments. Specifically, we found that children's baseline confidence judgments improved significantly following the presentation of disconfirming evidence. Furthermore, when asked to make exploration decisions about the same set of objects, children showed a spontaneous sensitivity to uncertainty, prior to any evidence. Taken together, these results suggest that children's exploration behavior may be used as an early developing measure of uncertainty control and raise the intriguing possibility that the experience of unexpected outcomes may play a role in the development of metacognition. [ABSTRACT FROM AUTHOR]

The uncertainty in Facial expression recognition (FER) data is caused by factors such as ambiguous facial expressions, low-resolution facial images, the subjectivity of the annotator or subject of expression, and compound expression. FER has been steadily evolving with the advent of deep learning, but the label inconsistency problem due to the uncertainty in large FER datasets is one of the challenges in FER. Noisy labels in the label inconsistency problem adversely affect emotion recognition results. In this paper, we propose a Face-Specific Label Distribution Learning (FSLDL) method, which encourages deep networks to predict the actual emotion distribution of the input itself, rather than the noisy single label. Under the assumption that the emotion distribution of a specific sample is similar to the emotion distribution of the augmented sample from a different viewpoint, we generate a new target label distribution using facial expression-specific augmented samples. In order to generate a sophisticated target label distribution, the importance weights of the augmented samples are extracted and multiplied by each predicted emotion distribution. In addition, we compensate for the lack of information in the target label distribution by calculating the uncertainty of the provided label, and use it for model training. Finally, we make a more robust model by adding a rank regularization loss function for the importance weights and a discriminative loss function for the feature vectors. Representative experiments demonstrated that FSLDL outperforms the state-of-the-art on FER datasets such as RAF-DB, AffectNet, and SFEW. In addition, we demonstrated the effectiveness of the proposed method through various noisy label injection experiments. [Display omitted] • Noisy labels in the FER dataset affect facial expression recognition performance. • It is helpful to use distributions for facial expressions rather than single labels. • New target distribution created with faces from multi-perspectives using augmentation. • New target distribution reflecting original labels with uncertainty for each sample. • Proving the effectiveness of our method through noisy label injection experiments. [ABSTRACT FROM AUTHOR]

Theoretical accounts have linked anxiety to intolerance of ambiguity. However, this relationship has not been well operationalized empirically. Here, we used computational and neuro-imaging methods to characterize anxiety-related differences in aversive decision-making under ambiguity and associated patterns of cortical activity. Adult human participants chose between two urns on each trial. The ratio of tokens ('O's and 'X's) in each urn determined probability of electrical stimulation receipt. A number above each urn indicated the magnitude of stimulation that would be received if a shock was delivered. On ambiguous trials, one of the two urns had tokens occluded. By varying the number of tokens occluded, we manipulated the extent of missing information. At higher levels of missing information, there is greater second order uncertainty, i.e., more uncertainty as to the probability of pulling a given type of token from the urn. Adult human participants demonstrated avoidance of ambiguous options which increased with level of missing information. Extent of 'information-level dependent' ambiguity aversion was significantly positively correlated with trait anxiety. Activity in both the dorsal anterior cingulate cortex and inferior frontal sulcus during the decision-making period increased as a function of missing information. Greater engagement of these regions, on high missing information trials, was observed when participants went on to select the ambiguous option; this was especially apparent in high trait anxious individuals. These findings are consistent with individuals vulnerable to anxiety requiring greater activation of frontal regions supporting rational decision-making to overcome a predisposition to engage in ambiguity avoidance at high levels of missing information. [ABSTRACT FROM AUTHOR]

In a multidimensional environment, parties may have compelling incentives to obscure their preferences on select issues. This study contributes to a growing literature on position blurring by demonstrating how party leaders purposively create uncertainty about where their party stands on the issue of European integration. By doing so, it theoretically and empirically disentangles the cause of position blurring—parties' strategic behavior—from its intended political outcome. The analysis of survey and manifesto data across 14 Western European countries (1999–2019) confirms that three distinct strategies—avoidance, ambiguity, and alternation—all increase expert uncertainty about a party's position. This finding is then unpacked by examining for whom avoidance is particularly effective. This study has important implications for our understanding of party strategy, democratic representation, and political accountability. [ABSTRACT FROM AUTHOR]

The dynamic and changing healthcare environment requires educators to prepare nurse practitioner students with more than knowledge and skills. Healthcare providers need to be prepared with enhanced skills related to critical thinking and decision making. Metacognition training has been used by many disciplines within and outside of healthcare to prepare individuals to work in an environment that has volatility, uncertainty, complexity, ambiguity, and delayed feedback (VUCAD). This retrospective exploratory study uses computerized strategic management simulation (SMS) data to address the question: What parameters of metacognition do master's level nurse practitioner (NP) students excel versus underperform? Elements of Presentation: Presentation will include SMS parameters linked with the National Organization of Nurse Practitioner Faculties (NONPF) Core Competencies. In doing this, faculty can better assess students' individualized learning needs and tailor their academic education to address specific needs. Fifty‐nine master's level students completed SMS, measuring five unique parameters of metacognition (Crisis, Task, Strategy, People & Information Management). Students' mean and median scores were observed to be high range for crisis management; and moderate range for task management, strategy management, and people management. Conversely, mean and median scores were in the low range for information management. Knowledge gained from this study can be used for curriculum development and planning to ensure that the parameters of metacognition are demonstrated throughout the curriculum's content delivery, training exercises, and evaluation methods. Knowledge related to areas in which NP students score in the low range can be added to courses to assist students better. [ABSTRACT FROM AUTHOR]

To guide behavior, perceptual systems must operate on intrinsically ambiguous sensory input. Observers are usually able to acknowledge the uncertainty of their perception, but in some cases, they critically fail to do so. Here, we show that a physiological correlate of ambiguity can be found in pupil dilation even when the observer is not aware of such ambiguity. We used a well-known auditory ambiguous stimulus, known as the tritone paradox, which can induce the perception of an upward or downward pitch shift within the same individual. In two experiments, behavioral responses showed that listeners could not explicitly access the ambiguity in this stimulus, even though their responses varied from trial to trial. However, pupil dilation was larger for the more ambiguous cases. The ambiguity of the stimulus for each listener was indexed by the entropy of behavioral responses, and this entropy was also a significant predictor of pupil size. In particular, entropy explained additional variation in pupil size independent of the explicit judgment of confidence in the specific situation that we investigated, in which the two measures were decoupled. Our data thus suggest that stimulus ambiguity is implicitly represented in the brain even without explicit awareness of this ambiguity [ABSTRACT FROM AUTHOR]

Influential theories posit that bodily responses are important for decision‐making under uncertainty. However, the evidence of the role of our ability to perceive subtle bodily changes (interoception) in decision‐making under uncertainty is mixed. These differences may arise from the fact that uncertainty, a part of daily decision‐making, can be fractionated into risk (known probabilities) and ambiguity (unknown probabilities). Here we examine the role of arousal and interoception in shaping risky and ambiguous decisions. We measured skin conductance responses and heart rate changes while participants (N = 40) made gambling decisions in the context of risky and ambiguous lotteries. Results reveal that the anticipation phase produced the largest arousal responses, suggesting that the anticipation is a major contributor to arousal during gambling behavior, regardless of the uncertainty type. Moreover, physiological responses were higher following positive outcomes than negative outcomes. We did not find any direct relation between interoceptive dimensions and the attitudes toward risk and ambiguity. However, in those with higher interoceptive accuracy, skin conductance responses differentiated between risk and ambiguity as well as between the gamble phases (decision, anticipation, and outcome). Together, our findings demonstrate that decision‐making under uncertainty is to some extent associated with individual differences in the ability both to generate and to perceive accurately subtle changes in bodily arousal during the decision‐making process. However, these changes seem to be moderately related to the type of uncertainty (risk or ambiguity). We demonstrate that decision‐making under uncertainty is associated with individual differences in the ability both to generate and perceive accurately subtle changes in bodily arousal during the decision‐making process. However, we did not find any direct relationship between interoceptive dimensions and attitudes (seeking/avoidance) toward risk and ambiguity. We also complement previous evidence that the largest arousal responses occurred during the anticipation phases, compared with the responses produced during the decision and the outcome phase. [ABSTRACT FROM AUTHOR]

To interact with humans in collaborative environments, machines need to be able to predict (i.e., anticipate) future events, and execute actions in a timely manner. However, the observation of the human limb movements may not be sufficient to anticipate their actions unambiguously. In this work, we consider two additional sources of information (i.e., context) over time, gaze, movement and object information, and study how these additional contextual cues improve the action anticipation performance. We address action anticipation as a classification task, where the model takes the available information as the input and predicts the most likely action. We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation. Uncertainty is modeled as a stochastic process applied to a time-based neural network architecture, which improves the conventional class-likelihood (i.e., deterministic) criterion. The main contributions of this paper are fourfold: (i) We propose a novel and effective decision-making criterion that can be used to anticipate actions even in situations of high ambiguity; (ii) we propose a deep architecture that outperforms previous results in the action anticipation task when using the Acticipate collaborative dataset; (iii) we show that contextual information is important to disambiguate the interpretation of similar actions; and (iv) we also provide a formal description of three existing performance metrics that can be easily used to evaluate action anticipation models. Our results on the Acticipate dataset showed the importance of contextual information and the uncertainty criterion for action anticipation. We achieve an average accuracy of 98.75 % in the anticipation task using only an average of 25 % of observations. Also, considering that a good anticipation model should perform well in the action recognition task, we achieve an average accuracy of 100 % in action recognition on the Acticipate dataset, when the entire observation set is used. [ABSTRACT FROM AUTHOR]

Pricing practices of firms are an important yet the least studied aspect of the price phenomenon in sociology. This paper answers the question: why do firms, even in the same market, tend to use different pricing practices – value-informed, competition-informed, or cost-informed pricing – to set prices? To that end, this study constructs a formal dynamic flocking model to investigate the inter-dynamics between market uncertainties and the viability of the three pricing practices. The model is a substantial revision and extension of Harrison White's static W(y) market model by reformulating the latter into a dynamic one and by explicitly incorporating different market uncertainties into the model as variables. The study shows that each kind of pricing practice is only viable under certain distributions of market uncertainties. The theory is then used to explain the distribution of pricing practices among firms in the Burgundy wine market. Theoretical and methodological innovations and the implications for firms and for sociological research on markets and uncertainties are also discussed. [ABSTRACT FROM AUTHOR]

John Maynard Keynes's (1936) concept of "animal spirits" or "spontaneous optimism" as a major driving force in business fluctuations was motivated in part by his and his contemporaries' observations of human reactions to ambiguous situations where probabilities couldn't be quantified. We can add that in such ambiguity there is evidence that people let contagious popular narratives and the emotions they generate influence their economic decisions. These narratives are typically remote from factual bases, just contagious. Macroeconomic dynamic models must have a theory that is related to models of the transmission of disease in epidemiology. We need to take the contagion of narratives seriously in economic modeling if we are to improve our understanding of animal spirits and their impact on the economy. [ABSTRACT FROM AUTHOR]

This paper studies the comparative statics of an optimal portfolio choice problem for an investor with both expected return and volatility ambiguity about the financial market. The optimal holding of the risky asset depends on risk preference, expected return and volatility ambiguity, yielding a general comparative statistics analysis for all investors with linearly growing absolute risk tolerance. [ABSTRACT FROM AUTHOR]

Recent advances in decision making have incorporated both risk and ambiguity in decision theory and optimization methods. These methods implement a variety of uncertainty representations from probabilistic and non-probabilistic foundations, including traditional probability theory, sets of probability measures, uncertainty sets, ambiguity sets, possibility theory, evidence theory, fuzzy measures, and imprecise probability. The choice of uncertainty representation impacts the expressiveness and tractability of the decision models. We survey recent approaches for representing uncertainty in both decision making and optimization to clarify the trade-offs among the alternative representations. Robust and distributionally robust optimization are surveyed, with particular attention to standard form ambiguity sets. Applications of uncertainty and decision models are also reviewed, with a focus on recent optimization applications. These applications highlight common practices and potential research gaps. The intersection of behavioral decision making and robust optimization is a promising area for future research and there is also opportunity for further advances in distributionally robust optimization in sequential and multi-agent settings. [ABSTRACT FROM AUTHOR]

Human–robot interactions are increasingly taking place between a robot agent and a human observer who, unable to witness all aspects of the robot's behaviour, is uncertain as to how the robot will behave. This uncertainty has serious consequences when it concerns the normative aspects of machine behaviour: that is, whether the robot's actions are morally permissible. Bringing together distinct threads from robotic design and machine ethics, we demonstrate the importance of conveying ethical understanding and commitment in order to reduce moral ambiguity and how this can demand a behavioural demonstration. We provide a framework that structures these considerations in the form of a broad constraint on robot behaviour: roughly, to avoid behaviour, even if it is permissible, if that behaviour could appear impermissible. Thus, we formalise a model of communicative-behavioural ethics in human–robot interactions. We apply this constraint to a series of example cases demonstrating how it can be modified to incorporate different sources of information including preferences and probabilities. This reveals the complexity of less idealised cases and highlights how the constraint can be fine-tuned along a number of dimensions to take into account, amongst other thing, risk attitudes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]