Understanding Factors of Bias in the Way Information Is Processed Through a Metamemetic and Multilogical Approach

Training in complex thinking is required in fields like computer science and discussing sensitive topics that can easily polarize internet users’ propensities. Multilogicality and Metamemetic reasoning are strongly suggested as an approach to identifying and analyzing factors related to AI Bias and human biases. This approach entails identifying problems and deducting invalid premises, distinguishing them from valid premises or those we are uncertain about. The theme of this paper focuses on four groups of people: curators, developers, businesses, and users (the fourth group being the main focus). This approach offers a new way to apply critical thinking strategies in the context of living in a digital age.

Antithetic Power Transformed Random Variables in Computer Simulations: An Error Correction Mechanism

A traditional method of Monte Carlo computer simulation is to obtain uniformly distributed random numbers on the interval from zero to one from a linear congruential generator (LCG) or other methods. Random variates can then be obtained by the inverse transformation technique applied to random numbers. The random variates can then be used as input to a computer simulation. A response variable is obtained from the simulation results. The response variable may be biased for various reasons. One reason may be the presence of small traces of serial correlation in the random numbers. The purpose of this paper is to introduce an alternative method of response variable acquisition by a power transformation applied to the response variable. The power transformation produces a new variable that is negatively correlated with the response variable. The response variable is then regressed on its power transformation to convert the units of the power transformed variable back to those of the original response variable. A weighted combination of these two variables gives the final estimate. The combined estimate is shown to have negligible bias. The correlations of various antithetic variates obtained from the power transformation are derived and illustrated to provide insights for this research and for future research into this method.

Understanding and improving Yangtze River Basin summer precipitation prediction using an optimal multi-Physics ensemble

This study employs the regional Climate-Weather Research and Forecasting model(CWRF)to first investigate the primary physical mechanisms causing biases in simulating summer precipitation over the Yangtze River Basin(YRB),and then enhance its predictive ability through an optimal multi-physics ensemble approach.The CWRF 30-km simulations in China are compared among 28 combinations of varying physics parameterizations during 1980−2015.Long-term average summer biases in YRB precipitation are remotely correlated with those of large-scale circulations.These teleconnections of biases are highly consistent with the observed correlation patterns between interannual variations of precipitation and circulations,despite minor shifts in their primary action centers.Increased YRB precipitation aligns with a southward shifted East Asian westerly jet,an intensified low-level southerly flow south of YRB,and a south-eastward shifted South Asian high,alongside higher moisture availability over YRB.Conversely,decreased YRB precipitation corresponds to an opposite circulation pattern.The CWRF control configuration using the ensemble cumulus parameterization(ECP),compared to other cumulus schemes,best captures the observed YRB precipitation characteristics and associated circulation patterns.Coupling ECP with the Morrison or Morrison-aerosol microphysics and the CCCMA or CAML radiation schemes enhances the overall CWRF skills.Compared to the control CWRF,the ensemble average of these skill-enhanced physics configurations more accurately reproduces YRB summer precipitation’s spatial distributions,interannual anomalies,and associated circulation patterns.The Bayesian Joint Probability calibration to these configurations improves the ensemble’s spatial distributions but compromises its interannual anomalies and teleconnection patterns.Our findings highlight substantial potential for refining the representation of climate system physics to improve YRB precipitation prediction.This is notably achieved by realistically coupling cumulus,microphysics,and radiation processes to accurately capture circulation teleconnections.Further enhancements can be achieved by optimizing the multi-physics ensemble among skill-enhanced configurations.

Confident Estimation for Density of a Biological Population Based on Line Transect Sampling

Line transect sampling is a very useful method in survey of wildlife population. Confident interval estimation for density D of a biological population is proposed based on a sequential design. The survey area is occupied by the population whose size is unknown. A stopping rule is proposed by a kernel-based estimator of density function of the perpendicular data at a distance. With this stopping rule, we construct several confidence intervals for D by difference procedures. Some bias reduction techniques are used to modify the confidence intervals. These intervals provide the desired coverage probability as the bandwidth in the stopping rule approaches zero. A simulation study is also given to illustrate the performance of this proposed sequential kernel procedure.