Extending the Wisdom of Crowds: Quantifying Uncertainty Using the Mean and Variance of a Collection of Point Estimates

2017 ◽  
Author(s):  
Asa B. Palley
Keyword(s):  
Wisdom Of Crowds ◽  
Point Estimates ◽  
Mean And Variance ◽  
The Mean ◽  
Quantifying Uncertainty ◽  
The Wisdom Of Crowds

Design Under Uncertainty: Balancing Expected Performance and Risk

2010 ◽  
Vol 132 (11) ◽  
Author(s):  
James A. Reneke ◽  
Margaret M. Wiecek ◽  
Georges M. Fadel ◽  
Sundeep Samson ◽  
Dimitri Nowak
Keyword(s):  
Epistemic Uncertainty ◽  
Design Decision ◽  
Aleatory Uncertainty ◽  
Stochastic Forcing ◽  
Design Performance ◽  
Expected Performance ◽  
Decision Methods ◽  
Mean And Variance ◽  
The Mean ◽  
Quantifying Uncertainty

The problem of quantifying uncertainty in the design process is approached indirectly. Nonquantifiable variability resulting from lack of knowledge is treated as epistemic uncertainty and quantifiable variability caused by random influences is treated as aleatory uncertainty. The emphasis in this approach is on the effects of epistemic uncertainty, left unquantified, on design performance. Performance is treated as a random function of the epistemic uncertainties that are considered as independent variables, and a design decision is based on the mean and variance of design performance. Since the mean and variance are functions of the uncertainties, multicriteria decision methods are employed to determine the preferred design. The methodology is illustrated on a three-spring model with stochastic forcing and two uncertain damping coefficients. Based on the example, the concept of balancing expected performance and risk is explored in an engineering context. Risk is quantified using aleatory uncertainty for fixed values of epistemic uncertainty. The study shows the unique features of this approach in which risk-based design decisions are made under both aleatory and epistemic uncertainties without assuming a distribution for epistemic uncertainty.

scholarly journals Do low-confidence individuals decrease group judgments’ accuracy? Investigations in terms of the wisdom of crowds framework

2021 ◽  
Author(s):  
Masaru Shirasuna ◽  
Hidehito Honda
Keyword(s):  
Binary Choice ◽  
Wisdom Of Crowds ◽  
High Confidence ◽  
Behavioral Experiments ◽  
Judgment Accuracy ◽  
Confidence Levels ◽  
Group Judgment ◽  
The Mean ◽  
Practical Implications ◽  
The Wisdom Of Crowds

Abstract In group judgments in a binary choice task, the judgments of individuals with low confidence (i.e., they feel that the judgment was not correct) may be regarded as unreliable. Previous studies have shown that aggregating individuals’ diverse judgments can lead to high accuracy in group judgments, a phenomenon known as the wisdom of crowds. Therefore, if low-confidence individuals make diverse judgments between individuals and the mean of accuracy of their judgments is above the chance level (.50), it is likely that they will not always decrease the accuracy of group judgments. To investigate this issue, the present study conducted behavioral experiments using binary choice inferential tasks, and computer simulations of group judgments by manipulating group sizes and individuals’ confidence levels. Results revealed that (I) judgment patterns were highly similar between individuals regardless of their confidence levels; (II) the low-confidence group could make judgments as accurate as the high-confidence group, as the group size increased; and (III) even if there were low-confidence individuals in a group, they generally did not inhibit group judgment accuracy. The results suggest the usefulness of low-confidence individuals’ judgments in a group and provide practical implications for real-world group judgments.

scholarly journals Do low-confidence individuals decrease group judgments’ accuracy? Investigations in terms of the wisdom of crowds framework

2021 ◽  
Author(s):  
Masaru Shirasuna ◽  
Hidehito Honda
Keyword(s):  
Binary Choice ◽  
Wisdom Of Crowds ◽  
High Confidence ◽  
Behavioral Experiments ◽  
Judgment Accuracy ◽  
Confidence Levels ◽  
Group Judgment ◽  
The Mean ◽  
Practical Implications ◽  
The Wisdom Of Crowds

In group judgments in a binary choice task, the judgments of individuals with low confidence (i.e., they feel that the judgment was not correct) may be regarded as unreliable. Previous studies have shown that aggregating individuals’ diverse judgments can lead to high accuracy in group judgments, a phenomenon known as the wisdom of crowds. Therefore, if low-confidence individuals make diverse judgments between individuals and the mean of accuracy of their judgments is above the chance level (.50), it is likely that they will not always decrease the accuracy of group judgments. To investigate this issue, the present study conducted behavioral experiments using binary choice inferential tasks, and computer simulations of group judgments by manipulating group sizes and individuals’ confidence levels. Results revealed that (I) judgment patterns were highly similar between individuals regardless of their confidence levels; (II) the low-confidence group could make judgments as accurate as the high-confidence group, as the group size increased; and (III) even if there were low-confidence individuals in a group, they generally did not inhibit group judgment accuracy. The results suggest the usefulness of low-confidence individuals’ judgments in a group and provide practical implications for real-world group judgments.

scholarly journals Weighted statistical binary patterns for facial feature representation

2021 ◽  
Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim
Keyword(s):  
Comprehensive Evaluation ◽  
Facial Feature ◽  
Input Image ◽  
Feature Representation ◽  
Illumination Variation ◽  
Straight Line ◽  
Mean And Variance ◽  
The Mean ◽  
Face Datasets ◽  
Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

scholarly journals Groups and Individuals: Optical Flow Patterns of Broiler Chicken Flocks Are Correlated with the Behavior of Individual Birds

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 568
Author(s):  
Sabine G. Gebhardt-Henrich ◽  
Ariane Stratmann ◽  
Marian Stamp Dawkins
Keyword(s):  
Optical Flow ◽  
Broiler Chicken ◽  
Average Length ◽  
Flow Patterns ◽  
Group Level ◽  
Positive Correlation ◽  
Water Test ◽  
Mean And Variance ◽  
The Mean

Group level measures of welfare flocks have been criticized on the grounds that they give only average measures and overlook the welfare of individual animals. However, we here show that the group-level optical flow patterns made by broiler flocks can be used to deliver information not just about the flock averages but also about the proportion of individuals in different movement categories. Mean optical flow provides information about the average movement of the whole flock while the variance, skew and kurtosis quantify the variation between individuals. We correlated flock optical flow patterns with the behavior and welfare of a sample of 16 birds per flock in two runway tests and a water (latency-to-lie) test. In the runway tests, there was a positive correlation between the average time taken to complete the runway and the skew and kurtosis of optical flow on day 28 of flock life (on average slow individuals came from flocks with a high skew and kurtosis). In the water test, there was a positive correlation between the average length of time the birds remained standing and the mean and variance of flock optical flow (on average, the most mobile individuals came from flocks with the highest mean). Patterns at the flock level thus contain valuable information about the activity of different proportions of the individuals within a flock.

scholarly journals A New Model for the Stochastic Point Reactor: Development and Comparison with Available Models

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 955
Author(s):  
Alamir Elsayed ◽  
Mohamed El-Beltagy ◽  
Amnah Al-Juhani ◽  
Shorooq Al-Qahtani
Keyword(s):  
Kinetic Model ◽  
Differential Equations ◽  
Test Cases ◽  
System Of Differential Equations ◽  
Reactor Model ◽  
Time System ◽  
New Model ◽  
Reactor Dynamics ◽  
Mean And Variance ◽  
The Mean

The point kinetic model is a system of differential equations that enables analysis of reactor dynamics without the need to solve coupled space-time system of partial differential equations (PDEs). The random variations, especially during the startup and shutdown, may become severe and hence should be accounted for in the reactor model. There are two well-known stochastic models for the point reactor that can be used to estimate the mean and variance of the neutron and precursor populations. In this paper, we reintroduce a new stochastic model for the point reactor, which we named the Langevin point kinetic model (LPK). The new LPK model combines the advantages, accuracy, and efficiency of the available models. The derivation of the LPK model is outlined in detail, and many test cases are analyzed to investigate the new model compared with the results in the literature.

A model for random instantaneous growth on an interval

1991 ◽  
Vol 28 (3) ◽  
pp. 529-538
Author(s):  
M. P. Quine
Keyword(s):  
Asymptotic Normality ◽  
Instantaneous Growth ◽  
Mean And Variance ◽  
The Mean

Points arrive in succession on an interval and immediately ‘cover' a region of length ½ to each side (less if they are close to the boundary or to a covered part). The location of a new point is uniformly distributed on the uncovered parts. We study the mean and variance of the total number of points ever formed, in particular as a → 0, in which case we also establish asymptotic normality.

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