Integrating explanation and prediction in computational social science.
Nature
; 595(7866): 181-188, 2021 07.
Article
em En
| MEDLINE
| ID: mdl-34194044
ABSTRACT
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions-the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes-and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Ciências Sociais
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Simulação por Computador
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Previsões
/
Ciência de Dados
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Modelos Teóricos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Nature
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos