Relative Information Contributions Research Articles

Quantify the Information Contributions of External Drivers to Global Ionospheric TEC

AbstractThe ionospheric variabilities are influenced by various drivers, and quantifying their relative contributions is beneficial to ionospheric modeling and ionospheric forecasting, but this is still a significant challenge in the ionospheric community. In present paper, the global ionosphere maps, solar indices, solar wind and interplanetary parameters, geomagnetic indices, and lower atmospheric parameters from 2001 to 2021 are collected, and the Liang‐Kleeman information flow is used to measure the amount of information exchanges from external drivers to ionospheric TEC at different geographical regions, and the normalized Liang‐Kleeman information flow is adopted to quantify their relative information contributions. The results show that the geographical distribution of information flow has prominent hemispheric asymmetry, and overall, the information flows from solar indices (solar wind and interplanetary parameters, geomagnetic indices) to ionospheric TEC in the northern hemisphere are larger than those in the southern hemisphere. Ionospheric self‐influence (i.e., internal processes of the ionosphere) has the largest information contribution to ionospheric TEC, accounting for about 46.78%. Solar activity manifested as P index has the second largest contribution (nearly 35.29%). Solar wind and interplanetary activity represented as IMF magnitude contributes around 2.94%, geomagnetic activity characterized as ASYH index accounts for about 1.52%, lower atmospheric forcing contributes nearly 7.75%, and noise accounts for approximately 5.73%. What's more, the performances of the IRI‐2020 model are evaluated from the perspective of information flow.

On the Sources of Information in the Moment Structure of Dynamic Macroeconomic Models

What features of the data are the key sources of information about the parameters in structural macroeconomic models? As such models grow in size and complexity, the answer to this question has become increasingly difficult. This article shows how to identify the main sources of parameter information across different parts of the moment structure of macroeconomic models. In particular, we propose a measure of the relative contribution of information by a given subset of moments with respect to any parameter of interest. The measure is trivial to compute even for large-scale models with many free parameters and observed variables. We illustrate our method with an application to a news-driven business cycle model developed by Schmitt-Grohé and Uribe. Supplementary materials for this article are available online.

Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination.

BackgroundMathematical models are increasingly being used to evaluate strategies aiming to achieve the control or elimination of parasitic diseases. Recently, owing to growing realization that process-oriented models are useful for ecological forecasts only if the biological processes are well defined, attention has focused on data assimilation as a means to improve the predictive performance of these models.Methodology and principal findingsWe report on the development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations (MDAs) for calibrating three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations. The relative information contribution of site-specific data collected at the time points proposed by the WHO monitoring framework was evaluated using model-data updating procedures, and via calculations of the Shannon information index and weighted variances from the probability distributions of the estimated timelines to parasite extinction made by each model. Results show that data-informed models provided more precise forecasts of elimination timelines in each site compared to model-only simulations. Data streams that included year 5 post-MDA microfilariae (mf) survey data, however, reduced each model’s uncertainty most compared to data streams containing only baseline and/or post-MDA 3 or longer-term mf survey data irrespective of MDA coverage, suggesting that data up to this monitoring point may be optimal for informing the present LF models. We show that the improvements observed in the predictive performance of the best data-informed models may be a function of temporal changes in inter-parameter interactions. Such best data-informed models may also produce more accurate predictions of the durations of drug interventions required to achieve parasite elimination.SignificanceKnowledge of relative information contributions of model only versus data-informed models is valuable for improving the usefulness of LF model predictions in management decision making, learning system dynamics, and for supporting the design of parasite monitoring programmes. The present results further pinpoint the crucial need for longitudinal infection surveillance data for enhancing the precision and accuracy of model predictions of the intervention durations required to achieve parasite elimination in an endemic location.

Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion

AbstractSubstantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Three assimilation experiments were conducted with TECO‐C‐C (carbon (C)‐only model), TECO‐CN‐C (TECO‐CN coupled model with only C measurements as assimilating data), and TECO‐CN‐CN (TECO‐CN model with both C and N measurements). Our results showed that additional N data had greater effects on ecosystem C storage (+68% and +55%) compared with added N processes (+32% and −45%) at the end of the experimental period (2009) and the long‐term prediction (2100), respectively. The uncertainties mainly resulted from woody biomass (relative information contributions are +50.4% and +36.6%) and slow soil organic matter pool (+30.6% and −37.7%) at the end of the experimental period and the long‐term prediction, respectively. During the experimental period, the additional N processes affected C dynamics mainly through process‐induced disequilibrium in the initial value of C pools. However, in the long‐term prediction period, the N data and processes jointly influenced the simulated C dynamics by adjusting the posterior probability density functions of key parameters. These results suggest that additional measurements of slow processes are pivotal to improving model predictions. Quantifying the uncertainty of the additional N data and processes can help us explore the terrestrial C‐N coupling in ecosystem models and highlight critical observational needs for future studies.

Response to climate risks among smallholder farmers in Malawi: A multivariate probit assessment of the role of information, household demographics, and farm characteristics

Why do many smallholder farmers fail to adopt what appear to be relatively simple agronomic or management practices which can help them cope with climate-induced stressors? Using household and plot level data collected in 2011, we implement a multivariate probit model to assess the determinants of farmer adaptation behavior to climatic risks and the relative contribution of information, credit and education on the probability of adopting specific practices in response to adverse changes in weather patterns. We find that plot characteristics, credit constraints and availability of climate-related information explain the adoption of several of these practices. In relative terms, we also find that even when financial limitations are binding, making climate-related information available can still motivate farmers to adapt. Policy implications are that the deepening of extension access with information on the appropriate adaptation strategies is crucial to help farmers make adaptation choices. The need to foster credit markets for easy accessibility and affordability by farmers or otherwise strengthening access to assets is also important.

Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics

Biogeochemical models have been used to evaluate long-term ecosystem responses to global change on decadal and century time scales. Recently, data assimilation has been applied to improve these models for ecological forecasting. It is not clear what the relative information contributions of model (structure and parameters) vs. data are to constraints of short- and long-term forecasting. In this study, we assimilated eight sets of 10-year data (foliage, woody, and fine root biomass, litter fall, forest floor carbon [C], microbial C, soil C, and soil respiration) collected from Duke Forest into a Terrestrial Ecosystem model (TECO). The relative information contribution was measured by Shannon information index calculated from probability density functions (PDFs) of carbon pool sizes. The null knowledge without a model or data was defined by the uniform PDF within a prior range. The relative model contribution was information content in the PDF of modeled carbon pools minus that in the uniform PDF, while the relative data contribution was the information content in the PDF of modeled carbon pools after data was assimilated minus that before data assimilation. Our results showed that the information contribution of the model to constrain carbon dynamics increased with time whereas the data contribution declined. The eight data sets contributed more than the model to constrain C dynamics in foliage and fine root pools over the 100-year forecasts. The model, however, contributed more than the data sets to constrain the litter, fast soil organic matter (SOM), and passive SOM pools. For the two major C pools, woody biomass and slow SOM, the model contributed less information in the first few decades and then more in the following decades than the data. Knowledge of relative information contributions of model vs. data is useful for model development, uncertainty analysis, future data collection, and evaluation of ecological forecasting.

Contribution of onset/offset information of modulation to amplitude modulation depth discrimination

A previous study by [J. Lee, G. Long, and C. Jeung, J. Acoust. Soc. Am. 119, S3332 (2006)] found that information at the onset or offset of modulation could be utilized for improved amplitude modulation (AM) depth discrimination in a continuous carrier condition (carrier presented 250 ms earlier and later than the modulator). In this study, the relative contribution of information at the onset or offset of the modulation was examined with an onset-fringe carrier condition (carrier begins 250 ms earlier than the modulator) and an offset-fringe condition (carrier ends 250 ms later than the modulator). The results suggest that modulation information at the onset might be utilized more than at the offset.

The contrasting effects of a semantic versus a formal—semantic technique for the facilitation of naming in a case of anomia

Abstract Facilitation techniques deemed ‘semantic’ in nature appear to be superior to other techniques in the remediation of anomia (Howard et al. 1985a). In such techniques, however, information about the spoken or written form of the picture name is provided, i.e. the word is presented either in writing or auditorily, and is embedded in a task requiring semantic treatment of the stimulus. The objective of this study is to determine the relative contribution of information about the spoken or written form of the picture name to the facilitative effect of semantic techniques. A single-subject study of a patient with moderately severe mixed aphasia and anomia was undertaken. The patient had received regular therapy and 10 months had elapsed since the cerebrovascular accident (CVA) at the beginning of the experiment. An alternating treatment design compared the effects on naming of a formal-Semantic facilitation technique (including the spoken or written word form in a semantic comprehension task) to a pure...

Relative Information Contributions of Consumer Purchase Intentions and Management Judgment as Explanators of Sales

Relative Information Contributions of Consumer Purchase Intentions and Management Judgment as Explanators of Sales

Relative Information Contributions of Consumer Purchase Intentions and Management Judgment as Explanators of Sales

The author compares the information content from intention surveys with management's judgmental estimates of consumers’ intentions. Results indicate that both consumer data and judgmental estimates provide management with significant incremental information.