An Artificial Intelligence Approach to Modeling in Social Science

Juan Carlos Jesus Vazquez; Julio Javier Castillo; Leticia Edith Constable; Marina Elizabeth Cardenas; Juan Carlos Guillermo Vazquez

doi:doi:10.11648/j.jher.20210701.20

An Artificial Intelligence Approach to Modeling in Social Science

Juan Carlos Jesus Vazquez, Julio Javier Castillo, Leticia Edith Constable, Marina Elizabeth Cardenas, Juan Carlos Guillermo Vazquez

Published in Journal of Health and Environmental Research (Volume 7, Issue 1)

Received: 4 January 2021 Accepted: 20 February 2021 Published: 17 March 2021

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Abstract

Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.

Published in	Journal of Health and Environmental Research (Volume 7, Issue 1) This article belongs to the Special Issue Health and the Environment as a Resource for the Reduction of Social Inequalities in Argentina
DOI	10.11648/j.jher.20210701.20
Page(s)	58-68
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Artificial Neural Network, Family Clinic History, Risk, Health, Housing

References

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[6]	Rojas, M. C. “Población, vivienda salud y vulnerabilidad global. Propuesta teórico-metodológica para la estimación del riesgo de la vivienda urbana para la salud basada en el análisis de la vulnerabilidad sociodemográfica.” [doctoral thesis] Faculty of Economic Sciences of the National University of Cordoba; Cordoba, Argentina; 2006.
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APA Style

Juan Carlos Jesus Vazquez, Julio Javier Castillo, Leticia Edith Constable, Marina Elizabeth Cardenas, Juan Carlos Guillermo Vazquez. (2021). An Artificial Intelligence Approach to Modeling in Social Science. Journal of Health and Environmental Research, 7(1), 58-68. https://doi.org/10.11648/j.jher.20210701.20

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ACS Style

Juan Carlos Jesus Vazquez; Julio Javier Castillo; Leticia Edith Constable; Marina Elizabeth Cardenas; Juan Carlos Guillermo Vazquez. An Artificial Intelligence Approach to Modeling in Social Science. J. Health Environ. Res. 2021, 7(1), 58-68. doi: 10.11648/j.jher.20210701.20

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AMA Style

Juan Carlos Jesus Vazquez, Julio Javier Castillo, Leticia Edith Constable, Marina Elizabeth Cardenas, Juan Carlos Guillermo Vazquez. An Artificial Intelligence Approach to Modeling in Social Science. J Health Environ Res. 2021;7(1):58-68. doi: 10.11648/j.jher.20210701.20

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@article{10.11648/j.jher.20210701.20,
  author = {Juan Carlos Jesus Vazquez and Julio Javier Castillo and Leticia Edith Constable and Marina Elizabeth Cardenas and Juan Carlos Guillermo Vazquez},
  title = {An Artificial Intelligence Approach to Modeling in Social Science},
  journal = {Journal of Health and Environmental Research},
  volume = {7},
  number = {1},
  pages = {58-68},
  doi = {10.11648/j.jher.20210701.20},
  url = {https://doi.org/10.11648/j.jher.20210701.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jher.20210701.20},
  abstract = {Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.},
 year = {2021}
}

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TY  - JOUR
T1  - An Artificial Intelligence Approach to Modeling in Social Science
AU  - Juan Carlos Jesus Vazquez
AU  - Julio Javier Castillo
AU  - Leticia Edith Constable
AU  - Marina Elizabeth Cardenas
AU  - Juan Carlos Guillermo Vazquez
Y1  - 2021/03/17
PY  - 2021
N1  - https://doi.org/10.11648/j.jher.20210701.20
DO  - 10.11648/j.jher.20210701.20
T2  - Journal of Health and Environmental Research
JF  - Journal of Health and Environmental Research
JO  - Journal of Health and Environmental Research
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EP  - 68
PB  - Science Publishing Group
SN  - 2472-3592
UR  - https://doi.org/10.11648/j.jher.20210701.20
AB  - Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.
VL  - 7
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Author Information

Juan Carlos Jesus Vazquez

Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina
Julio Javier Castillo

Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina
Leticia Edith Constable

Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina
Marina Elizabeth Cardenas

Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina
Juan Carlos Guillermo Vazquez

Information Systems Engineering Department of Cordoba Regional Faculty, National Technological University, Cordoba, Argentina

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APA Style

Juan Carlos Jesus Vazquez, Julio Javier Castillo, Leticia Edith Constable, Marina Elizabeth Cardenas, Juan Carlos Guillermo Vazquez. (2021). An Artificial Intelligence Approach to Modeling in Social Science. Journal of Health and Environmental Research, 7(1), 58-68. https://doi.org/10.11648/j.jher.20210701.20

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ACS Style

Juan Carlos Jesus Vazquez; Julio Javier Castillo; Leticia Edith Constable; Marina Elizabeth Cardenas; Juan Carlos Guillermo Vazquez. An Artificial Intelligence Approach to Modeling in Social Science. J. Health Environ. Res. 2021, 7(1), 58-68. doi: 10.11648/j.jher.20210701.20

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AMA Style

Juan Carlos Jesus Vazquez, Julio Javier Castillo, Leticia Edith Constable, Marina Elizabeth Cardenas, Juan Carlos Guillermo Vazquez. An Artificial Intelligence Approach to Modeling in Social Science. J Health Environ Res. 2021;7(1):58-68. doi: 10.11648/j.jher.20210701.20

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@article{10.11648/j.jher.20210701.20,
  author = {Juan Carlos Jesus Vazquez and Julio Javier Castillo and Leticia Edith Constable and Marina Elizabeth Cardenas and Juan Carlos Guillermo Vazquez},
  title = {An Artificial Intelligence Approach to Modeling in Social Science},
  journal = {Journal of Health and Environmental Research},
  volume = {7},
  number = {1},
  pages = {58-68},
  doi = {10.11648/j.jher.20210701.20},
  url = {https://doi.org/10.11648/j.jher.20210701.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jher.20210701.20},
  abstract = {Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - An Artificial Intelligence Approach to Modeling in Social Science
AU  - Juan Carlos Jesus Vazquez
AU  - Julio Javier Castillo
AU  - Leticia Edith Constable
AU  - Marina Elizabeth Cardenas
AU  - Juan Carlos Guillermo Vazquez
Y1  - 2021/03/17
PY  - 2021
N1  - https://doi.org/10.11648/j.jher.20210701.20
DO  - 10.11648/j.jher.20210701.20
T2  - Journal of Health and Environmental Research
JF  - Journal of Health and Environmental Research
JO  - Journal of Health and Environmental Research
SP  - 58
EP  - 68
PB  - Science Publishing Group
SN  - 2472-3592
UR  - https://doi.org/10.11648/j.jher.20210701.20
AB  - Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.
VL  - 7
IS  - 1
ER  -

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