Geometric Complexity and the Information-Theoretic Comparison of Functional-Response Models

The assessment of relative model performance using information criteria like AIC and BIC has become routine among functional-response studies, reflecting trends in the broader ecological literature. Such information criteria allow comparison across diverse models because they penalize each model's fit by its parametric complexity—in terms of their number of free parameters—which allows simpler models to outperform similarly fitting models of higher parametric complexity. However, criteria like AIC and BIC do not consider an additional form of model complexity, referred to as geometric complexity, which relates specifically to the mathematical form of the model. Models of equivalent parametric complexity can differ in their geometric complexity and thereby in their ability to flexibly fit data. Here we use the Fisher Information Approximation to compare, explain, and contextualize how geometric complexity varies across a large compilation of single-prey functional-response models—including prey-, ratio-, and predator-dependent formulations—reflecting varying apparent degrees and forms of non-linearity. Because a model's geometric complexity varies with the data's underlying experimental design, we also sought to determine which designs are best at leveling the playing field among functional-response models. Our analyses illustrate (1) the large differences in geometric complexity that exist among functional-response models, (2) there is no experimental design that can minimize these differences across all models, and (3) even the qualitative nature by which some models are more or less flexible than others is reversed by changes in experimental design. Failure to appreciate model flexibility in the empirical evaluation of functional-response models may therefore lead to biased inferences for predator–prey ecology, particularly at low experimental sample sizes where its impact is strongest. We conclude by discussing the statistical and epistemological challenges that model flexibility poses for the study of functional responses as it relates to the attainment of biological truth and predictive ability.

It’s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US

Restricting in-person interactions is an important technique for limiting the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although early research found strong associations between cell phone mobility and infection spread during the initial outbreaks in the United States, it is unclear whether this relationship persists across locations and time. We propose an interpretable statistical model to identify spatiotemporal variation in the association between mobility and infection rates. Using 1 year of US county-level data, we found that sharp drops in mobility often coincided with declining infection rates in the most populous counties in spring 2020. However, the association varied considerably in other locations and across time. Our findings are sensitive to model flexibility, as more restrictive models average over local effects and mask much of the spatiotemporal variation. We conclude that mobility does not appear to be a reliable leading indicator of infection rates, which may have important policy implications.

The Excellent Method of Novel Uncertainty Predictor for Risk Assessment in E-Financial Institutions

Financial institutions are always engaged in risk assessment. In fact, the birth of finance in Europe was through dealing with risk. Risk is a situation that we have more than a possible future with different probabilities. Assessing different futures with different level of probabilities is not that easy to formulate. Neural network is a topology developed for dealing with cases in which formulating the problem is not easy due to model flexibility which is required by the conditions of risks we are dealing with in E-finance. E-finance is providing financial services over electronic devices and cyber space. With the prominent growth of E-finance, the need for developing new models for assessing risk associated with this kind of business seems inevitable. Continuous growth of E-finance brings on new issues such as E-trust; consequently, the need for developing a model of total risk assessment is the base of our study. The presented model is a prototype, future models should be developed specifically for different kind of risk E-finance provider are dealing with.

It’s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US

Restricting in-person interactions is an important technique for limiting the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although early research found strong associations between cell phone mobility and infection spread during the initial outbreaks in the United States, it is unclear whether this relationship persists across locations and time. We propose an interpretable statistical model to identify spatiotemporal variation in the association between mobility and infection rates. Using one year of US county-level data, we found that sharp drops in mobility often coincided with declining infection rates in the most populous counties in spring 2020. However, the association varied considerably in other locations and across time. Our findings are sensitive to model flexibility, as more restrictive models average over local effects and mask much of the spatiotemporal variation. We conclude that mobility does not appear to be a reliable leading indicator of infection rates, which may have important policy implications.

It’s a trust thing

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings The combination of authentic leadership, business model flexibility, and a rigorous and successful CSR initiative are crucial for organizational survival during a crisis. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

A Model Structure for Size-by-Liberation Recoveries in Flotation

This communication presents a model structure for the flotation recovery of middling particles (10–90% liberation). Fourteen datasets from the literature were studied (galena flotation), which involved different flotation systems and operating conditions. The flotation responses allowed the model flexibility to be evaluated under a range of recovery profiles. The modelling results showed that galena recovery can be characterized by the interaction between a linear function and a concave function (e.g., Gamma model), to account for the liberation and particle size effects, respectively. Liberation also impacts the location and dispersion of the recovery dependence on particle size. The proposed model structure showed there was adequate flexibility with five parameters, leading to adjusted coefficients of determination ranging from 0.863 to 0.998 for the studied datasets. Thus, an alternative approach for modelling the recovery of middling particles is proposed, which represents the liberation and particle size dependence with a few parameters.

Methodological approaches to imputing early-pregnancy weight based on weight measures collected during pregnancy

Background Early pregnancy weights are needed to quantify gestational weight gain accurately. Different methods have been used in previous studies to impute early-pregnancy weights. However, no studies have systematically compared imputed weight accuracy across different imputation techniques. This study aimed to compare four methodological approaches to imputing early-pregnancy weight, using repeated measures of pregnancy weights collected from two pregnancy cohorts in Tanzania. Methods The mean gestational ages at enrollment were 17.8 weeks for Study I and 10.0 weeks for Study II. Given the gestational age distributions at enrollment, early-pregnancy weights were extrapolated for Study I and interpolated for Study II. The four imputation approaches included: (i) simple imputation based on the nearest measure, (ii) simple arithmetic imputation based on the nearest two measures, (iii) mixed-effects models, and (iv) marginal models with generalized estimating equations. For the mixed-effects model and the marginal model with generalized estimating equation methods, imputation accuracy was further compared across varying degrees of model flexibility by fitting splines and polynomial terms. Additional analyses included dropping third-trimester weights, adding covariate to the models, and log-transforming weight before imputation. Mean absolute error was used to quantify imputation accuracy. Results Study I included 1472 women with 6272 weight measures; Study II included 2131 individuals with 11,775 weight measures. Among the four imputation approaches, mixed-effects models had the highest accuracy (smallest mean absolute error: 1.99 kg and 1.60 kg for Studies I and II, respectively), while the other three approaches showed similar degrees of accuracy. Depending on the underlying data structure, allowing appropriate degree of model flexibility and dropping remote pregnancy weight measures may further improve the imputation performance. Conclusions Mixed-effects models had superior performance in imputing early-pregnancy weight compared to other commonly used strategies.

Methodological Approaches to Imputing Early-Pregnancy Weight Based on Weight Measures Collected During Pregnancy

Background: Early pregnancy weights are needed to quantify gestational weight gain accurately. Different methods have been used in previous studies to impute early-pregnancy weights. However, no studies have systematically compared imputed weight accuracy across different imputation techniques. This study aimed to compare four methodological approaches to imputing early-pregnancy weight, using repeated measures of pregnancy weights collected from two pregnancy cohorts in Tanzania.Methods: The mean gestational ages at enrollment were 17.8 weeks for Study I and 10.0 weeks for Study II. Given the gestational age distributions at enrollment, early-pregnancy weights were extrapolated for Study I and interpolated for Study II. The four imputation approaches included: (i) simple imputation based on the nearest measure, (ii) simple arithmetic imputation based on the nearest two measures, (iii) mixed-effects models, and (iv) generalized estimating equations. For the mixed-effects model and the generalized estimating equation model methods, imputation accuracy was further compared across varying degrees of model flexibility by fitting splines and polynomial terms. Additional analyses included dropping third-trimester weights, adding covariate to the models, and log-transforming weight before imputation. Mean absolute error was used to quantify imputation accuracy. Results: Study I included 1,472 women with 6,272 weight measures; Study II included 2,131 individuals with 11,775 weight measures. Among the four imputation approaches, mixed-effects models had the highest accuracy (smallest mean absolute error: 1.99 kg and 1.60 kg for Studies I and II, respectively), while the other three approaches showed similar degrees of accuracy. Depending on the underlying data structure, allowing appropriate degree of model flexibility and dropping remote pregnancy weight measures may further improve the imputation performance. Conclusions: Mixed-effects models had superior performance in imputing early-pregnancy weight compared to other commonly used strategies.

Corporate social responsibility during unprecedented crises: the role of authentic leadership and business model flexibility

PurposeThe purpose of the present research is to explore the relationships between corporate social responsibility (CSR), authentic leadership and business model flexibility during times of unprecedented crises.Design/methodology/approachThe research approach in this study is conceptual. After a brief review of the literature associated with CSR, authentic leadership and business models, the authors introduce a model describing the interaction of authentic leadership and business model flexibility on CSR heterogeneity.FindingsThis research explains how firms that are led by authentic leaders and that have flexible business models will be more engaged with their stakeholders than firms with less authentic leaders or more rigid business models during unprecedented crises.Practical implicationsPrescriptions for practitioners are suggested for improving authentic leadership as well as making adaptations to the firm's business model. Regarding authentic leadership, firms can screen potential new hires and existing employees for authentic leadership qualities. Firms can also rely upon existing interventions shown to assist in authentic leadership development for current leaders. At the business model level, firms can focus on core resources and their application in related product and service markets.Originality/valueFirms engaged in CSR activities benefit more from those activities when leaders are authentic. However, in times of unprecedented crises, business model flexibility may also dictate the extent to which firms can satisfy their stakeholders. The authors introduce a conceptual model that takes the elements of authentic leadership and business model flexibility into account to explain CSR heterogeneity.