Does big data serve policy?: not without context : an experiment with in silico social science
Year of publication: |
2023
|
---|---|
Authors: | Graziul, Chris ; Belikov, Alexander ; Chattopadyay, Ishanu ; Chen, Ziwen ; Fang, Hongbo ; Girdhar, Anuraag ; Jia, Xiaoshuang ; Krafft, P. M. ; Kleiman-Weiner, Max ; Lewis, Candice ; Liang, Chen ; Muchovej, John ; Vientós, Alejandro ; Young, Meg ; Evans, James A. |
Published in: |
Computational & mathematical organization theory. - Dordrecht [u.a.] : Springer Science + Business Media B.V, ISSN 1572-9346, ZDB-ID 2012211-1. - Vol. 29.2023, 1, p. 188-219
|
Subject: | Computational social science | Deep learning | Machine learning | Policy | Quantitative social science | Simulated societies | Simulation | Sozialwissenschaft | Social sciences | Künstliche Intelligenz | Artificial intelligence | Big Data | Big data | Wirtschaftswissenschaft | Economics |
-
Modeling organizational performance with machine learning
Pap, Jozsef, (2022)
-
One-to-one marketing in grocery retailing
Gabel, Sebastian, (2019)
-
Visual listening in: extracting brand image portrayed on social media
Liu, Liu, (2019)
- More ...
-
Shen, Feng, (2022)
-
How NAFTA affected U.S. vehicle emissions : demand growth and usage patterns
Lian, Zeng, (2025)
-
Liang, Chen, (2024)
- More ...