Dataset Information

A mathematical approach to study and forecast racial groups interactions: deterministic modeling and scenario method.

ABSTRACT: Globalization and migratory fluxes are increasing the ethnic and racial diversity within many countries. Therefore, describing social dynamics requires models that are apt to capture multi-groups interactions. Building on the assumption of a relationship between multi-racial dynamics and socioeconomic status (SES), we introduce an aggregate, contextual, and continuous index of SES accounting for measures of income, employment, expected life, and group numerosity. After, taking into account that groups' SES assumes the form of a logit model, we propose a Lotka-Volterra system to study and forecast the interaction among racial groups. Last, we apply our methodology to describe the racial dynamics in the US society. In particular, we study the kind and the intensity of Asians-Blacks-Natives-Whites interactions in the US between 2002 and 2013. Moreover, we forecast the evolution of groups' SES and how interracial relations will unfold between 2013 and 2018 and in three alternative stylized scenarios.

SUBMITTER: Dominioni G

PROVIDER: S-EPMC5993863 | biostudies-other | 2018

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

A mathematical approach to study and forecast racial groups interactions: deterministic modeling and scenario method.

Dominioni Goran G Dominioni Goran G Marasco Addolorata A Romano Alessandro A

Quality & quantity 20171003 4

Globalization and migratory fluxes are increasing the ethnic and racial diversity within many countries. Therefore, describing social dynamics requires models that are apt to capture multi-groups interactions. Building on the assumption of a relationship between multi-racial dynamics and <i>socioeconomic status (SES)</i>, we introduce an aggregate, contextual, and continuous index of SES accounting for measures of income, employment, expected life, and group numerosity. After, taking into accoun ...[more]

PMID: 29937586

Similar Datasets

Project description:Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha-1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

Dataset Information

A mathematical approach to study and forecast racial groups interactions: deterministic modeling and scenario method.

Publications

A mathematical approach to study and forecast racial groups interactions: deterministic modeling and scenario method.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets