Perceptions Of Uncertainty Research Articles

The Perception of Policy Uncertainty and the Labor Income Share of Firms: Empirical Research Based on Double Machine Learning

ABSTRACTBased on the double machine learning model, this study examines the impact of perception of policy uncertainty (PPU) on labor income share (LIS). We find that PPU negatively influences LIS. Mechanism test indicates that PPU not only results in a reduction in fixed asset investment, distorting human capital structure, but leads to a financing dilemma for firms, ultimately exerting a detrimental effect on LIS. Additionally, PPU primarily diminishes LIS of ordinary employees rather than executives. Furthermore, the inhibitory effect of PPU on LIS is significantly more pronounced in firms characterized by smaller union scales and those operating within monopolistic industries.

Adaptation biases the parallel perception of subitized numerosities

Numerosity adaptation is a phenomenon in which prolonged exposure to a stimulus of greater numerosity makes subsequent stimuli appear less numerous, and vice versa. It has been confined to moderated numerosities outside the subitizing range (> 4). This study investigated whether the estimation of small numerosities (1–4), which is performed rapidly and accurately due to the mechanism of subitizing, is susceptible to adaptation. After adapting to a 50-dot stimulus, participants were presented with stimuli consisting of 1–5 color sets. In some trials, participants were informed of the target color-set before the presentation of stimuli, while in others, they were instructed afterwards. When estimating 1–4 dots in the single-color set or superset (the total dots), no adaptation effect was observed. The coefficient of variation (CV) was below 0.05, indicating the effective function of subitizing. However, when enumerating subsets in parallel, adaptation biased the estimation. The CV in estimating subitized numerosities was comparable to and correlated with that of estimating moderate numerosities (5–12), suggesting that subitizing was superseded by numerosity estimation. Greater effects arise when the targets were probed afterwards, with elevated CV. The prior adaptor may be more weighted to optimize detection of number deviations, especially under higher perceptual uncertainty.

Real world comparison of filgrastim to filgrastim-sndz in patients with chemotherapy-induced neutropenia.

There exists some apprehension by prescribers, healthcare providers, and other stakeholders regarding the real-world safety and effectiveness of biosimilars. While some of the apprehension is likely due to clinician knowledge gaps in the biosimilarity exercise, additional data (including those generated from the real-world) regarding safety and efficacy could reduce a clinician's perception of biosimilar uncertainty and can potentially increase biosimilar acceptance and uptake. The published literature is lacking regarding the real-world impact on healthcare costs and clinical outcomes when a single healthcare institution converts from filgrastim to filgrastim-sndz as its short-acting Granulocyte Colony Stimulating Factor (GCSF), especially within diverse populations and indications not explicitly studied through the registration trials. Specifically, both filgrastim and filgrastim-sndz possess FDA-approved indications within patients with hematologic malignancies receiving high-dose chemotherapy and in those undergoing bone marrow transplantation. As a biosimilar to filgrastim, the FDA did not require prospective, randomized controlled trials to obtain these indications for filgrastim-sndz. The purpose of this study is to describe real-world healthcare resource costs, utilization patterns, and clinical outcomes in patients with hematologic malignancies who received filgrastim-sndz or filgrastim to support neutrophil recovery following chemotherapy (i.e., induction or consolidation) or a bone marrow transplant (BMT) at Yale New Haven Hospital (YNHH). A total of 148 patients were identified and met the following criteria: at least 18 years old, Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, absence of fever or infection, newly diagnosed acute myeloid leukemia or newly post-bone marrow transplant and received filgrastim-sndz (Zarxio®) or reference filgrastim (Neupogen®). Healthcare resource utilization outcomes were compared between biosimilar and reference filgrastim using descriptive statistics. There were no major differences between either cohort, including duration of hospitalization, the number of overall emergency room visits and additional hospital admissions with a primary diagnosis of febrile neutropenia or neutropenia within 30 days post-discharge from initial hospital admission, time to neutrophil recovery following GCSF support, and the incidence and duration of both fever and febrile neutropenia. The total cost based on treatment of all patients in each arm differed significantly with filgrastim-sndz being the most cost-effective choice. filgrastim-sndz significantly reduced healthcare utilization costs compared to reference filgrastim with similar effect on absolute neutrophil count, incidence of fever, and febrile neutropenia.

Computational joint action: Dynamical models to understand the development of joint coordination.

Coordinating with others is part of our everyday experience. Previous studies using sensorimotor coordination games suggest that human dyads develop coordination strategies that can be interpreted as Nash equilibria. However, if the players are uncertain about what their partner is doing, they develop coordination strategies which are robust to the actual partner's actions. This has suggested that humans select their actions based on an explicit prediction of what the partner will be doing-a partner model-which is probabilistic by nature. However, the mechanisms underlying the development of a joint coordination over repeated trials remain unknown. Very much like sensorimotor adaptation of individuals to external perturbations (eg force fields or visual rotations), dynamical models may help to understand how joint coordination develops over repeated trials. Here we present a general computational model-based on game theory and Bayesian estimation-designed to understand the mechanisms underlying the development of a joint coordination over repeated trials. Joint tasks are modeled as quadratic games, where each participant's task is expressed as a quadratic cost function. Each participant predicts their partner's next move (partner model) by optimally combining predictions and sensory observations, and selects their actions through a stochastic optimization of its expected cost, given the partner model. The model parameters include perceptual uncertainty (sensory noise), partner representation (retention rate and internale noise), uncertainty in action selection and its rate of decay (which can be interpreted as the action's learning rate). The model can be used in two ways: (i) to simulate interactive behaviors, thus helping to make specific predictions in the context of a given joint action scenario; and (ii) to analyze the action time series in actual experiments, thus providing quantitative metrics that describe individual behaviors during an actual joint action. We demonstrate the model in a variety of joint action scenarios. In a sensorimotor version of the Stag Hunt game, the model predicts that different representations of the partner lead to different Nash equilibria. In a joint two via-point (2-VP) reaching task, in which the actions consist of complex trajectories, the model captures well the observed temporal evolution of performance. For this task we also estimated the model parameters from experimental observations, which provided a comprehensive characterization of individual dyad participants. Computational models of joint action may help identifying the factors preventing or facilitating the development of coordination. They can be used in clinical settings, to interpret the observed behaviors in individuals with impaired interaction capabilities. They may also provide a theoretical basis to devise artificial agents that establish forms of coordination that facilitate neuromotor recovery.

Processing of prosodic cues of uncertainty in autistic and non-autistic adults: a study based on articulatory speech synthesis.

We investigated the prosodic perception of uncertainty cues in adults with Autism Spectrum Disorder (ASD) compared to neurotypical adults (NTC). We used articulatory synthetic speech to express uncertainty in a human-machine scenario by varying the three acoustic cues pause, intonation, and hesitation. Twenty-eight adults with ASD and 28 NTC adults rated each answer for uncertainty, naturalness, and comprehensibility. Both groups reliably perceived different levels of uncertainty. Stimuli were rated as less uncertain by the ASD group, but not significantly. Only when we pooled the recipients' ratings for all three cues, did we find a significant group difference. In terms of reaction time, we observed longer reaction times in the ASD group compared to the neurotypical comparison group for the uncertainty level hesitation & strong intonation, but the differences were not significant after Bonferroni correction. Furthermore, our results showed a significant group difference between the correlation of uncertainty and naturalness, i.e. the correlation in the ASD group is significantly lower than in the NTC group. Obtained effect size estimates can inform sample size calculations in future studies for the reliable identification of group differences. In future work, we would like to further investigate the interaction of all three cues and uncertainty perception. It would be interesting to further vary the duration of the pause and also to use different types of fillers. From a developmental perspective, uncertainty perception should also be investigated in children and adolescents with ASD.

Integration of auditory and visual cues in spatial navigation under normal and impaired viewing conditions.

Auditory landmarks can contribute to spatial updating during navigation with vision. Whereas large inter-individual differences have been identified in how navigators combine auditory and visual landmarks, it is still unclear under what circumstances audition is used. Further, whether or not individuals optimally combine auditory cues with visual cues to decrease the amount of perceptual uncertainty, or variability, has not been well-documented. Here, we test audiovisual integration during spatial updating in a virtual navigation task. In Experiment 1, 24 individuals with normal sensory acuity completed a triangular homing task with either visual landmarks, auditory landmarks, or both. In addition, participants experienced a fourth condition with a covert spatial conflict where auditory landmarks were rotated relative to visual landmarks. Participants generally relied more on visual landmarks than auditory landmarks and were no more accurate with multisensory cues than with vision alone. In Experiment 2, a new group of 24 individuals completed the same task, but with simulated low vision in the form of a blur filter to increase visual uncertainty. Again, participants relied more on visual landmarks than auditory ones and no multisensory benefit occurred. Participants navigating with blur did not rely more on their hearing compared with the group that navigated with normal vision. These results support previous research showing that one sensory modality at a time may be sufficient for spatial updating, even under impaired viewing conditions. Future research could investigate task- and participant-specific factors that lead to different strategies of multisensory cue combination with auditory and visual cues.

Identifying the drivers of economic uncertainty perception in China: a news-based approach

ABSTRACT Relying on news media as a reflection of public sentiment, we build the Uncertainty Perception Indicator (UPI) to measure perceived economic uncertainty in China. Applying Latent Dirichlet Allocation to n = 5,600 news articles, we decompose UPI into three components: Uncertainty relating to the real economy, financial markets, and politics. We find that on average, overall uncertainty perception is mainly driven by uncertainty about the real economy. This is especially true since 2018, when around 60 percent of the corpus was related to the real economy. We also find that stock price exposure is highest for real economic uncertainty.

Association between genetic information provision and decisional conflict in patients with cancer.

227 Background: The decisional conflict scale (DCS) measures personal perception of uncertainty when facing a decision. In patients with cancer, decision conflict is likely when considering uncertainty in oncologic systemic treatment outcomes. Cancer genomic information can introduce more uncertainty and complexity in care. This study aims to evaluate patients’ DCS score pre or post discussing next generation sequencing (NGS) results with a medical oncologist, and assess factors that correlate with high decision conflict. Methods: Patients diagnosed with incurable/metastatic cancer who underwent molecular characterization with tissue+/- liquid NGS testing were enrolled. Survey instruments included the validated DCS (five subscales: informed, values clarity, support, uncertainty, effective decision), and EQ-5D-5L. DCS was administered pre or post medical oncology consultation that included review of tissue +/- liquid NGS results and treatment options. Higher DCS score correlates with higher perception of uncertainty. Descriptive statistics were used to assess clinical and demographic risk factors. Multivariable logistic regression analysis estimated the correlation between high DCS and clinical factors. Results: 335 DCS surveys were completed by 227 patients: 188 before and 147 after discussing tissue+/- liquid NGS results with a medical oncologist. Baseline characteristics: 56% female, median age 65, ECOG 0-1 58%, median EQ5D VAS score 68, GI/lung/gyne/breast/other 42/32/10/5/11%, white/Asian/other/unknown 66/12/5/17%. Tier 1 variants were identified in 35% of patients. Patients reported decreased decision conflict after consultation. Multivariable logistic regression analysis including sex, age, race and tumor group did not predict for high DCS pre-consultation. MVA post NGS results that also included Tier 1 variants (present / absent) did not predict for high DSC post consultation. Conclusions: Communication regarding genomic-based tumor assessment can positively support patients in decision conflict, and improve their confidence in their treatment choice, regardless of the findings of the report. Low DCS after consultation supports the goal of shared decision making for cancer treatment. Clinical trial information: NCT05057234 . DCS subscale Pre-consultation Post-consultation p value Informed 49.3 37.5 <0.001 Values clarity 45.3 35.5 <0.001 Support 34.4 28.2 0.005 Uncertainty 46.8 36.1 <0.001 Effective decision 40.0 31.4 <0.001 TOTAL 42.9 33.6 <0.001

What drives AI-based risk information-seeking intent? Insufficiency of risk information versus (Un)certainty of AI chatbots

This study explored the factors influencing the U.S. public's intent to seek risk information via AI-powered channels, such as ChatGPT. It focused on cognitive and affective pathways that lead to uncertainty about both risk information and AI chatbots in the context of climate change risk. We conducted a comparative analysis to discern the impacts of risk perceptions related to climate change and AI-caused privacy risks on public uncertainty and decision-making regarding the use of AI chatbots. Specifically, we assessed how different risk-related perceptions and emotions contribute to subsequent uncertainty perceptions and decision-making regarding AI chatbot use for climate change risk information. We enlisted 1023 U.S. citizens aged 21–65 via CloudResearch in September 2023. The results reveal that high levels of perceived risk, strong negative emotions, and information insufficiency drive information-seeking behavior through AI chatbots. Perceived privacy concerns about AI technology significantly increase AI anxiety, which is positively associated with perceived uncertainty. Both AI anxiety and perceived uncertainty negatively affect the intent to seek information via AI chatbots. Conversely, perceived trust in AI chatbots significantly increases positive emotional responses, reduces perceived uncertainty, and enhances the intent to seek information via AI chatbots. We also investigated the mediation effects within each study model tested. The findings offer theoretical and practical implications for future studies on the public's adoption of AI services for risk information seeking, influenced by both risk-related and technology-based contexts.

The effect of illness uncertainty and self-efficacy on the perception of shared decision-making among parents of infants in the neonatal intensive care unit.

Shared decision-making is essential for improving infant prognoses. Medical staff should consider the effect of illness uncertainty and self-efficacy on parents' perceptions of shared decision-making. This study examined the impact of illness uncertainty and self-efficacy on the perception of shared decision-making among parents of infants in the neonatal intensive care unit. A descriptive-analytical cross-sectional study design was used. Data were collected from April to June 2023. A total of 103 parents of infants admitted to the neonatal intensive care unit participated in this study. The participants used a self-report questionnaire that included general characteristics of their infants, uncertainty of illness, self-efficacy and perception of shared decision-making. Data were analysed using descriptive statistics, independent t-test, analysis of variance, Scheffe's test, Pearson's correlation coefficient and multiple linear regression. Illness uncertainty (r = -.659, p < .001, 95% confidence interval = [-1.209, -0.765]) was negatively correlated with self-efficacy, and self-efficacy (r = .255, p = .009, 95% confidence interval = [0.082, 0.569]) was positively correlated with the perception of shared decision-making. Using multiple linear regression, the number of visits to the intensive care unit (β = -1.939, p = .015, 95% confidence interval = [-3.490, -0.389]) and parents' self-efficacy (β = .271, p = .028, 95% confidence interval = [0.030, 0.512]) had a statistically significant effect on the perception of shared decision-making, accounting for 11.9% of the total explanatory power. The results support that higher self-efficacy is associated with a higher degree of perception of shared decision-making among parents of infants in the intensive care unit. Therefore, clinicians might support parents in taking a more active role in shared decision-making by discussing available options with clinicians and reaching treatment plans together. The study suggests creating scales to assess nurses' participation in shared decision-making and recommends educational programmes to boost parents' self-efficacy with infants, significantly affecting perceptions of shared decision-making.

Firms’ uncertainty perception and asset–debt maturity mismatch

This study examines a sample of Chinese A-share listed companies from 2012 to 2022 and explores the relationship between a significant increase in firm uncertainty perception and asset–debt maturity mismatch. The findings show that increasing firms’ perceptions of uncertainty can exacerbate asset–debt maturity mismatch. Furthermore, the impact of firm perceptions of uncertainty on asset–debt maturity mismatch is achieved through financial constraints, financing difficulties, and the effects of investment preferences. Specifically, when enterprises perceive increasing uncertainty, they may experience insufficient cash flow, operational difficulties, and increased external risks, forcing enterprises to adopt short- and long-lending behavior to address the issues.

Discrimination thresholds reflect task-related, cognitive processes rather than cue uncertainty in depth perception

Discrimination thresholds reflect task-related, cognitive processes rather than cue uncertainty in depth perception

Bayesian inference of perceptual uncertainty, behavioral costs, and prior beliefs for continuous perception-action tasks

Bayesian inference of perceptual uncertainty, behavioral costs, and prior beliefs for continuous perception-action tasks

The firms’ perception of global uncertainty and R&D investment: Evidence from China

This study explores how the firms’ perception of global uncertainty (FGU) affects R&D investment of listed firms in China. Our analysis is rooted in the understanding and measurement of FGU. Consistent with the theory of strategic growth, we find a significantly positive correlation between FGU and R&D investment. We further elucidate the mechanism underlining this correlation. When FGU increases, financing and labour resources are shifted towards R&D activities, promoting R&D investment. We also examine and explain how this correlation is magnified for firms in less competitive industries, those with higher tangible asset ratios, those related to states, and those with lower deleveraging levels. Finally, we divide FGU into five categories and investigate which subcomponent of FGU has the relatively greatest impact on R&D investment. FGU related to criminal and political events has a greater influence on R&D investment than other types of FGU.

Factorizers for distributed sparse block codes

Distributed sparse block codes (SBCs) exhibit compact representations for encoding and manipulating symbolic data structures using fixed-width vectors. One major challenge however is to disentangle, or factorize, the distributed representation of data structures into their constituent elements without having to search through all possible combinations. This factorization becomes more challenging when SBCs vectors are noisy due to perceptual uncertainty and approximations made by modern neural networks to generate the query SBCs vectors. To address these challenges, we first propose a fast and highly accurate method for factorizing a more flexible and hence generalized form of SBCs, dubbed GSBCs. Our iterative factorizer introduces a threshold-based nonlinear activation, conditional random sampling, and an ℓ ∞ -based similarity metric. Its random sampling mechanism, in combination with the search in superposition, allows us to analytically determine the expected number of decoding iterations, which matches the empirical observations up to the GSBC’s bundling capacity. Secondly, the proposed factorizer maintains a high accuracy when queried by noisy product vectors generated using deep convolutional neural networks (CNNs). This facilitates its application in replacing the large fully connected layer (FCL) in CNNs, whereby C trainable class vectors, or attribute combinations, can be implicitly represented by our factorizer having F-factor codebooks, each with C F fixed codevectors. We provide a methodology to flexibly integrate our factorizer in the classification layer of CNNs with a novel loss function. With this integration, the convolutional layers can generate a noisy product vector that our factorizer can still decode, whereby the decoded factors can have different interpretations based on downstream tasks. We demonstrate the feasibility of our method on four deep CNN architectures over CIFAR-100, ImageNet-1K, and RAVEN datasets. In all use cases, the number of parameters and operations are notably reduced compared to the FCL.

From code to coin: Unravelling the practical business impact of algorithmic pricing

This paper examines the impact of algorithmic pricing on company value and presents the results of 31 expert interviews in various industries. The focus is on nine hypotheses, including the efficiency of automation, segmentation and value-based pricing, efficient resource allocation, perception of fairness, price transparency and uncertainty, the use of personal data and price competition. The surveys show that algorithmic pricing has considerable potential for efficiency gains through automation, particularly in terms of revenue growth. The potential for cost savings, however, varies from sector to sector. Segmentation and value-based pricing are proving to be feasible, but their implementation requires clear justification and economic rationale. Efficient resource allocation through dynamic pricing shows potential, especially in industry-specific scenarios. The perception of unfairness can have a negative impact, however, and personalised pricing risks alienating customers and reducing business value. Price transparency and uncertainty have a mixed effect, as customers often place more value on the transparency of alternative offers than on the company’s pricing process. The use of personal data poses a low risk to company value if it is transparent and mutually beneficial. The potential intensification of competition through algorithmic pricing depends on the market structure and the company’s objectives; however, algorithmic pricing can be used as an instrument to reduce price competition and increase company value. Overall, the results make it clear that the integration of algorithmic pricing strategies must be carefully and ethically designed in order to sustainably increase company value.

Local government fiscal stress and regional entrepreneurship: Evidence from a natural experiment in China

Employing the abolition of the agricultural taxes in 2005 in China as a natural experiment, this paper investigates the impact of local government fiscal stress on regional entrepreneurship. We find that the reform results in increased fiscal expenditure and decreased new firm entry. This result suggests that local government fiscal stress is a significant hindrance to regional entrepreneurship, particularly in areas with low fiscal self-sufficiency, limited financial development, and weak legal systems. We also explore the mechanisms through which agricultural tax abolition affects entrepreneurial activities. Our findings indicate that four mechanisms are effective in influencing entrepreneurship, including enhanced tax collection and management, increased local debt, rising land prices, and heightened uncertainty perception by entrepreneurs. Overall, our research provides new insights into the relationship between local fiscal stress and economic development from the perspective of regional entrepreneurship.

Political uncertainty, bank loans, and corporate behavior: New investigation with machine learning

This paper investigates how uncertain term length, a novel source of political uncertainty, affects the behaviors of banks and firms using a machine-learning approach. China's local authorities do not have a fixed term, creating an ideal environment for studying how economic agents react to their perception of political uncertainty without an actual political turnover. We implement a machine-learning method to predict the term length of city leaders by observing others with similar backgrounds. Combining this new measurement of political uncertainty and bank- and firm-level data, we find an inverted U-shaped relationship between city leaders' predicted remaining term length and bank loans, corporate liabilities, and investment, which matches the change of political uncertainty over the term. We also record the potential adverse consequences of the politically motivated loan and investment expansion, such as a loss of corporate efficiency and a disruption in market order.

Serving with a Smile on Airbnb: Analyzing the Economic Returns and Behavioral Underpinnings of the Host’s Smile

Abstract Non-informational cues, such as facial expressions, can significantly influence judgments and interpersonal impressions. While past research has explored how smiling affects business outcomes in offline or in-store contexts, relatively less is known about how smiling influences consumer choice in e-commerce settings even when there is no face-to-face interaction. In this paper, we use a longitudinal Airbnb dataset and a facial attribute classifier to quantify the effect of a smile in the host’s profile photo on property demand and identify factors that influence when a host smile is likely to have the biggest effect. A smile in the host’s profile photo increases property demand by 3.5% on average. This effect is moderated by a variety of host and property characteristics that provide evidence for the role of uncertainty underlying why smiling increases demand. Specifically, when there is greater uncertainty regarding either the quality of the accommodations or the interaction with the host, a host smile will have a greater effect on demand. Online experiments confirm this pattern, offering further support for uncertainty perceptions driving the effect of smiling on increased Airbnb demand, and show that the effect of smiling on demand generalizes beyond Airbnb.

Clinically established early Parkinson's disease patients do not show impaired use of priors in conditions of perceptual uncertainty

The ability to use past learned experiences to guide decisions is an important component of adaptive behavior, especially when decision-making is performed under time pressure or when perceptual information is unreliable. Previous studies using visual discrimination tasks have shown that this prior-informed decision-making ability is impaired in Parkinson's disease (PD), but the mechanisms underlying this deficit and the precise impact of dopaminergic denervation within cortico-basal circuits remain unclear. To shed light on this problem, we evaluated prior-informed decision-making under various conditions of perceptual uncertainty in a sample of 13 clinically established early PD patients, and compared behavioral performance with healthy control (HC) subjects matched in age, sex and education. PD patients and HC subjects performed a random dot motion task in which they had to decide the net direction (leftward vs. rightward) of a field of moving dots and communicate their choices through manual button presses. We manipulated prior knowledge by modulating the probability of occurrence of leftward vs. rightward motion stimuli between blocks of trials, and by explicitly giving these probabilities to subjects at the beginning of each block. We further manipulated stimulus discriminability by varying the proportion of dots moving coherently in the signal direction and speed-accuracy instructions. PD patients used choice probabilities to guide perceptual decisions in both speed and accuracy conditions, and their performance did not significantly differ from that of HC subjects. An additional analysis of the data with the diffusion decision model confirmed this conclusion. These results suggest that the impaired use of priors during visual discrimination observed at more advanced stages of PD is independent of dopaminergic denervation, though additional studies with larger sample sizes are needed to more firmly establish this conclusion.