Strategy Use in Automation-Aided Decision Making

2021 ◽  
Vol 65 (1) ◽  
pp. 96-100
Author(s):  
Jackson Duncan-Reid ◽  
Jason S. McCarley
Keyword(s):  
Decision Aid ◽  
Process Model ◽  
Model Fit ◽  
Large Individual ◽  
Parameter Estimates ◽  
Discrete State ◽  
State Process ◽  
Human Operators ◽  
Response Classification ◽  
Better Than

When human operators make signal detection judgments with assistance from an automated decision aid, they perform better than they could unaided but fail to reach optimal sensitivity. We investigated the decision strategies that produce this suboptimal performance. Participants ( N = 130) performed a two-response classification task that required them to mentally estimate the mean of a set of randomly sampled values each trial. The task was performed with and without assistance from a 93% reliable decision aid. Psychometric functions were fit to the classification data, and data were fit with two cognitive models of automation use. The first model assumed that participants made automation-aided judgments using a contingent criterion strategy, adjusting their response cutoff for yes vs. no responses following a cue from the aid. The second strategy, a discrete state model, assumed that participants made aided judgments by simply deferring to the aid on some proportion of trials. A measure of model fit favored the discrete-state process model, with parameter estimates indicating large individual differences in deferral rate between participants (range = 2% and 95%).

Exploring the Multidimensional Facets of Authoritarianism: Authoritarian Aggression and Social Dominance Orientation

2008 ◽  
Vol 67 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Stefano Passini
Keyword(s):  
Social Dominance ◽  
Factor Model ◽  
Three Dimensional ◽  
Social Dominance Orientation ◽  
Model Fit ◽  
Regression Analyses ◽  
One Dimensional ◽  
Confirmatory Factor ◽  
The One ◽  
Better Than

The relation between authoritarianism and social dominance orientation was analyzed, with authoritarianism measured using a three-dimensional scale. The implicit multidimensional structure (authoritarian submission, conventionalism, authoritarian aggression) of Altemeyer’s (1981, 1988) conceptualization of authoritarianism is inconsistent with its one-dimensional methodological operationalization. The dimensionality of authoritarianism was investigated using confirmatory factor analysis in a sample of 713 university students. As hypothesized, the three-factor model fit the data significantly better than the one-factor model. Regression analyses revealed that only authoritarian aggression was related to social dominance orientation. That is, only intolerance of deviance was related to high social dominance, whereas submissiveness was not.

How success enhances self-serving bias: A multinomial process model of the Implicit Association Test

2019 ◽  
Vol 47 (7) ◽  
pp. 1-9 ◽  
Author(s):  
Zhongyang Wang ◽  
Yi Li ◽  
Zheng Jin ◽  
Timothy Tamunang Tamutana
Keyword(s):  
Undergraduate Students ◽  
Implicit Association Test ◽  
Process Model ◽  
Self Esteem ◽  
Regulation Mechanism ◽  
Implicit Association ◽  
Desirable Outcome ◽  
Subsequent Application ◽  
External Forces ◽  
Better Than

Self-serving bias is individuals' belief that leads them to blame external forces when bad things happen and to give themselves credit when good things happen. To evaluate how underlying evaluative associations toward the self or others differ between individuals, and/or how the regulation mechanism of the influence of such associations differs, we used a multinomial process model to measure the underlying implicit self-esteem in these processes with 56 Chinese undergraduate students. The results indicated that participants assessed themselves as being better than others when their performance was followed by a desirable outcome. Subsequent application of the quadruple processes showed that both activation of positive associations toward self and regulation of the associations played important roles in attitudinal responses. Our findings may provide a supplementary explanation to that of previous results, promoting understanding of the mechanism underlying self-serving bias.

Statistical Properties of a DNA Sample Under the Finite-Sites Model

Genetics ◽  
1996 ◽  
Vol 144 (4) ◽  
pp. 1941-1950 ◽  
Author(s):  
Ziheng Yang
Keyword(s):  
Process Model ◽  
Random Mating ◽  
Major Effect ◽  
Statistical Properties ◽  
Parameter Estimates ◽  
Rate Variation ◽  
Markov Process Model ◽  
D Loop ◽  
Infinite Sites Model ◽  
Segregating Sites

Statistical properties of a DNA sample from a random-mating population of constant size are studied under the finite-sites model. It is assumed that there is no migration and no recombination occurs within the locus. A Markov process model is used for nucleotide substitution, allowing for multiple substitutions at a single site. The evolutionary rates among sites are treated as either constant or variable. The general likelihood calculation using numerical integration involves intensive computation and is feasible for three or four sequences only; it may be used for validating approximate algorithms. Methods are developed to approximate the probability distribution of the number of segregating sites in a random sample of n sequences, with either constant or variable substitution rates across sites. Calculations using parameter estimates obtained for human D-loop mitochondrial DNAs show that among-site rate variation has a major effect on the distribution of the number of segregating sites; the distribution under the finite-sites model with variable rates among sites is quite different from that under the infinite-sites model.

Optimal Dispatching of Electrical Sensors for Automatic Control

2013 ◽  
Vol 676 ◽  
pp. 246-250 ◽  
Author(s):  
Jian Liang Liu ◽  
Jian Yang ◽  
Wei Yi Liu
Keyword(s):  
Automatic Control ◽  
Control Systems ◽  
Prediction Performance ◽  
Prediction Algorithm ◽  
Bayesian Decision ◽  
Discrete State ◽  
Automatic Control Systems ◽  
Continuous State ◽  
Dispatching Algorithm ◽  
Better Than

In this paper, we consider an electrical sensor dispatching problem for automatic control systems(ACS). We propose an algorithm which selects one (or a group of) electrical sensor at each time from a set of electrical sensors. Then, the automatic control prediction algorithm computes the estimates of the continuous state and the discrete state of the ACS based on the observation from the selected electrical sensors. As the electrical sensor dispatching algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. At the same time, the continuous state prediction performance of the proposed algorithm is better than that of automatic control prediction algorithms using only predetermined electrical sensors. Finally, our algorithm is validated though an illustrative target tracking example.

Modeling of Nonlinear Viscoelastic Creep of Polycarbonate

e-Polymers ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Wenbo Luo ◽  
Said Jazouli ◽  
Toan Vu-Khanh
Keyword(s):  
Creep Behavior ◽  
Room Temperature ◽  
Multiple Integral ◽  
Model Fit ◽  
Viscoelastic Creep ◽  
Commercial Grade ◽  
Nonlinear Viscoelastic ◽  
Schapery Model ◽  
Special Case ◽  
Better Than

AbstractThe creep behavior of a commercial grade polycarbonate was investigated in this study. 10 different constant stresses ranging from 8 MPa to 50 MPa were applied to the specimen, and the resultant creep strains were measured at room temperature. It was found that the creep could be modeled linearly below 15 MPa, and nonlinearly above 15 MPa. Different nonlinear viscoelastic models have been briefly reviewed and used to fit the test data. It is shown that the Findley model is a special case of the Schapery model, and both the Findley model and the simplified multiple integral representation are suitable for properly describing the creep behavior of the polycarbonate investigated in this paper; however, the Findley model fit the data better than the simplified multiple integral with three terms.

scholarly journals The updated Charlson comorbidity index is a useful predictor of mortality in patients with Staphylococcus aureus bacteraemia

2018 ◽  
Vol 146 (16) ◽  
pp. 2122-2130 ◽  
Author(s):  
H. G. Ternavasio-de la Vega ◽  
F. Castaño-Romero ◽  
S. Ragozzino ◽  
R. Sánchez González ◽  
M. P. Vaquero-Herrero ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Charlson Comorbidity Index ◽  
Regression Models ◽  
Model Fit ◽  
Logistic Regression Models ◽  
Relevant Variables ◽  
Comorbidity Index ◽  
The Mean ◽  
Related Mortality ◽  
Better Than

AbstractThe objective was to compare the performance of the updated Charlson comorbidity index (uCCI) and classical CCI (cCCI) in predicting 30-day mortality in patients with Staphylococcus aureus bacteraemia (SAB). All cases of SAB in patients aged ⩾14 years identified at the Microbiology Unit were included prospectively and followed. Comorbidity was evaluated using the cCCI and uCCI. Relevant variables associated with SAB-related mortality, along with cCCI or uCCI scores, were entered into multivariate logistic regression models. Global model fit, model calibration and predictive validity of each model were evaluated and compared. In total, 257 episodes of SAB in 239 patients were included (mean age 74 years; 65% were male). The mean cCCI and uCCI scores were 3.6 (standard deviation, 2.4) and 2.9 (2.3), respectively; 161 (63%) cases had cCCI score ⩾3 and 89 (35%) cases had uCCI score ⩾4. Sixty-five (25%) patients died within 30 days. The cCCI score was not related to mortality in any model, but uCCI score ⩾4 was an independent factor of 30-day mortality (odds ratio, 1.98; 95% confidence interval, 1.05–3.74). The uCCI is a more up-to-date, refined and parsimonious prognostic mortality score than the cCCI; it may thus serve better than the latter in the identification of patients with SAB with worse prognoses.

Calibration of three resuspension/sedimentation models

1998 ◽  
Vol 37 (3) ◽  
pp. 41-49 ◽  
Author(s):  
Gerard Blom ◽  
R. Hans Aalderink
Keyword(s):  
Suspended Solids ◽  
Statistical Evaluation ◽  
Model Fit ◽  
Parameter Estimates ◽  
Flume Experiments ◽  
Parameter Estimations ◽  
In Situ Observations ◽  
Squared Residuals ◽  
Very High

Three resuspension and sedimentation models (Blom, Lick and Partheniades and Krone) are calibrated and evaluated on data from flume experiments with sediments from Lake Ketel and in situ suspended solids measurements. We applied a formal parameter estimation technique in combination with a statistical evaluation of the model fit and parameter estimates. All three models produce a reasonable reconstruction of the data from the flume experiment and the in situ observations. The differences in the model fit of the three models are small, except for the in situ observations. Here the sum of squared residuals for Partheniades and Krone's is about twice the sum for Blom's and Lick's model. The correlation between parameters in resuspension/sedimentation models can be very high, leading to an uncertainty in parameter estimates of 25-50. The parameter estimations based on the flume data are up to orders of magnitude higher than those estimated from field observations.

scholarly journals Mapping species richness using opportunistic samples: a case study on ground-floor bryophyte species richness in the Belgian province of Limburg

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas Neyens ◽  
Peter J. Diggle ◽  
Christel Faes ◽  
Natalie Beenaerts ◽  
Tom Artois ◽  
...  
Keyword(s):  
Species Richness ◽  
Process Model ◽  
Cost Effective ◽  
Sampling Bias ◽  
Parameter Estimates ◽  
Multiple Sources ◽  
Ground Floor ◽  
Sampling Process ◽  
Ecological Predictors ◽  
Geostatistical Models

AbstractIn species richness studies, citizen-science surveys where participants make individual decisions regarding sampling strategies provide a cost-effective approach to collect a large amount of data. However, it is unclear to what extent the bias inherent to opportunistically collected samples may invalidate our inferences. Here, we compare spatial predictions of forest ground-floor bryophyte species richness in Limburg (Belgium), based on crowd- and expert-sourced data, where the latter are collected by adhering to a rigorous geographical randomisation and data collection protocol. We develop a log-Gaussian Cox process model to analyse the opportunistic sampling process of the crowd-sourced data and assess its sampling bias. We then fit two geostatistical Poisson models to both data-sets and compare the parameter estimates and species richness predictions. We find that the citizens had a higher propensity for locations that were close to their homes and environmentally more valuable. The estimated effects of ecological predictors and spatial species richness predictions differ strongly between the two geostatistical models. Unknown inconsistencies in the sampling process, such as unreported observer’s effort, and the lack of a hypothesis-driven study protocol can lead to the occurrence of multiple sources of sampling bias, making it difficult, if not impossible, to provide reliable inferences.

scholarly journals Inferring the Demographic History of Inbred Species from Genome-Wide SNP Frequency Data

2020 ◽  
Vol 37 (7) ◽  
pp. 2124-2136
Author(s):  
Paul D Blischak ◽  
Michael S Barker ◽  
Ryan N Gutenkunst
Keyword(s):  
Puma Concolor ◽  
Demographic History ◽  
Random Mating ◽  
Simulated Data ◽  
Conservation Agriculture ◽  
Model Fit ◽  
Parameter Estimates ◽  
History Of ◽  
Demographic Inference ◽  
Inbred Populations

Abstract Demographic inference using the site frequency spectrum (SFS) is a common way to understand historical events affecting genetic variation. However, most methods for estimating demography from the SFS assume random mating within populations, precluding these types of analyses in inbred populations. To address this issue, we developed a model for the expected SFS that includes inbreeding by parameterizing individual genotypes using beta-binomial distributions. We then take the convolution of these genotype probabilities to calculate the expected frequency of biallelic variants in the population. Using simulations, we evaluated the model’s ability to coestimate demography and inbreeding using one- and two-population models across a range of inbreeding levels. We also applied our method to two empirical examples, American pumas (Puma concolor) and domesticated cabbage (Brassica oleracea var. capitata), inferring models both with and without inbreeding to compare parameter estimates and model fit. Our simulations showed that we are able to accurately coestimate demographic parameters and inbreeding even for highly inbred populations (F = 0.9). In contrast, failing to include inbreeding generally resulted in inaccurate parameter estimates in simulated data and led to poor model fit in our empirical analyses. These results show that inbreeding can have a strong effect on demographic inference, a pattern that was especially noticeable for parameters involving changes in population size. Given the importance of these estimates for informing practices in conservation, agriculture, and elsewhere, our method provides an important advancement for accurately estimating the demographic histories of these species.

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