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Highly anisotropic zirconium is a material used in the cladding of nuclear fuel rods, ensuring containment of the radioactive material within. The complex material structure of anisotropic zirconium requires model developers to replicate not only the macro-scale stresses but also the meso-scale material behavior as the crystal structure evolves; leading to strongly coupled multi-scale plasticity models. Such strongly coupled models can be achieved through partitioned analysis techniques, which couple independently developed constituent models through an iterative exchange of inputs and outputs. Throughout this iterative process, biases, and uncertainties inherent within constituent model predictions are inevitably transferred between constituents either compensating for each other or accumulating during iterations. The paper aims to discuss these issues.

A finite element model at the macro-scale is coupled in an iterative manner with a meso-scale viscoplastic self-consistent model, where the former supplies the stress input and latter represents the changing material properties. The authors present a systematic framework for experiment-based validation taking advantage of both separate-effect experiments conducted within each constituent’s domain to calibrate the constituents in their respective scales and integral-effect experiments executed within the coupled domain to test the validity of the coupled system.

This framework developed is shown to improve predictive capability of a multi-scale plasticity model of highly anisotropic zirconium.

For multi-scale models to be implemented to support high-consequence decisions, such as the containment of radioactive material, this transfer of biases and uncertainties must be evaluated to ensure accuracy of the predictions of the coupled model. This framework takes advantage of the transparency of partitioned analysis to reduce the accumulation of errors and uncertainties.

Purpose: This chapter aims: (a) to extend biased net models of homophily to complete networks; (b) to extend the scope of application of these models to processes of social exchange in a small-group laboratory setting; and (c) to link the theoretical model of attraction and repulsion with a standard statistical model of logistic regression as a way of estimating and evaluating the model.

Methods: We discuss the logic of biased net theory and show how it leads to formal mathematical models of tie formation and tie renewal under mechanisms of attraction and repulsion. We then estimate key theoretical parameters in the models by means of logistic regression.

Findings: The estimated effects of homophily in our models are moderate in strength, weaker than corresponding reciprocity effect, and processes of tie formation and tie renewal are driven more by considerations of direct reciprocity than group membership. Under attraction, homophily effects are stronger for tie renewal than tie formation. Under repulsion, the opposite holds.

Limitations: Participants in our study are divided into two groups based on a criterion that is likely to have been too weak to induce strong group identity. Measures that enhance the sense of group identity need to be introduced in future studies.

Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered.

As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm.

This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice.

To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.

Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively.

The in-sample results reveal that the prediction model including the model bias can obtain bigger R², and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models.

The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

The purpose of this study was to measure the bias on a binary option's effect estimate that appeared in the types of questions asked and in the placement changes of public service users.

The author designed Monte Carlo simulations with the analytical strategy of latent trait theory leveraging a probability of care-placement change. The author used difference-in-difference (DID) method to estimate the effects of care settings.

The author explained the extent of discrepancy between the estimates and the true values of care service effects in changes across time. The time trend of in-home care for the combined effect of in-home care, general maturity, and other environmental factors was estimated in a biased manner, while the bias for the estimate of the incremental effect for foster care could be negligible.

This study was designed based on individual child-unit only. Therefore, higher-level units, such as care setting or cluster, county, and state, should be considered for the simulation model.

This study contributed to illuminating an overlooked facet in causal inferences that embrace disproportionate selection biases that appear in categorical data scales in public management research.

To model the nuance of a disproportionate self-selection problem, the author constructed a scenario surrounding a caseworker's judgment of care placement in the child welfare system and investigated potential bias of the caseworker's discretion. The unfolding model has not been widely used in public management research, but it can be usefully leveraged for the estimation of a decision probability.

Guided by several pioneered studies, the purpose of this paper is to comprehensively investigate the investment behaviours of Malaysian retail and institutional investors in an attempt to identify whether the influence of psychological biases is equally applicable to investor divides.

The researchers have adopted a quantitative research design by way of survey methodology to obtain data from institutional and retail investors in Malaysia. In addition, the authors have mainly employed second-order measurement invariance analysis to uncover the difference across investor divides.

The tests of measurement invariance at the model level indicate an insignificant difference between institutional investors and retail investors. The post hoc test (at the path level) reveals that institutional and retail investors are similar with respect to representative heuristic, overconfidence bias and anchoring bias; though the results also show that they are different with respect to religious bias and herding bias.

Based on the findings of this study, it is generally not logical to assume that institutional investors completely behave rational during investment decisions. Besides, future researchers are called upon to directly compare the investment decisions of institutional and retail investors with respect to whether the influence of psychological biases is equally applicable to them, particularly on the investigated psychological biases and other psychological biases that are not covered in this study.

This study has offered insight into whether the influence of psychological biases is equally applicable to institutional and retail investors in Malaysia using second-order measurement invariance analysis. This study is unique in context and the approach it has adopted.

This study aims to highlight the system-wide potential relationships between forms of human bias, selected Lean tools and types of waste in a manufacturing process.

A longitudinal single-site ethnographic case study using digital processing to make a material receiving process Lean was adopted. An inherent knowledge process with internal stakeholders in a stimulated situation alongside process requirements was performed to achieve quality data collection. The results of the narrative analysis and process observation, combined with a literature review identified widely used Lean tools, wastes and biases that produced a model for the relationships.

The study established the relationships between bias, Lean tools and wastes which enabled 97.6% error reduction, improved on-time accounting and eliminated three working hours per day. These savings resulted in seven employees being redeployed to new areas with delivery time for products reduced by seven days.

The single site case study with a supporting literature survey underpinning the model would benefit from testing the model in application to different industries and locations.

Application of the model can identify potential relationships between a group of human biases, 25 Lean tools and 10 types of wastes in Lean manufacturing processes that support decision makers and line managers in productivity improvement. The model can be used to identify potential relationships between forms of human biases, Lean tools and types of wastes in Lean manufacturing processes and take suitable remedial actions. The influence of biases and the model could be used as a basis to counter implementation barriers and reduce system-wide wastes.

To the best of the authors’ knowledge, this is the first study that connects the cognitive perspectives of Lean business processes with waste production and human biases. As part of the process, a relationship model is derived.

It is shown in the literature that the Arellano–Bond type generalized method of moments (GMM) of dynamic panel models is asymptotically biased (e.g., Hsiao & Zhang, 2015; Hsiao & Zhou, 2017). To correct the asymptotical bias of Arellano–Bond GMM, the authors suggest to use the jackknife instrumental variables estimation (JIVE) and also show that the JIVE of Arellano–Bond GMM is indeed asymptotically unbiased. Monte Carlo studies are conducted to compare the performance of the JIVE as well as Arellano–Bond GMM for linear dynamic panels. The authors demonstrate that the reliability of statistical inference depends critically on whether an estimator is asymptotically unbiased or not.

The aim is to study the impact of schooling on between-groups wage inequality beyond the lens of the standard approach in the literature.

Simple econometric theory is used to make the main point of the paper. Supporting empirical evidence is also presented.

Disregarding the persistence of current earnings implies a bias in the estimation of the wage return to schooling both at labour-market entry and in the rest of the working life.

The use of current earnings as a dependent variable in wage-schooling models may be problematic and requires specific handling.

The impact of schooling on the between-groups dimension of wage inequality may be different than previously thought.

The paper is the first to show that, when current earnings are used as a dependent variable, the identification of a wage-schooling model with the standard (time-invariant external instrument-variable) approach may lead to misleading conclusions.