Javier García-Bernardo

University of Amsterdam

Juyl 12th, 2019

computational social science

The CORPNET research group uncovers, investigates and aims to understand global networks of corporate power in contemporary global capitalism

What are we doing?

How?

  • Using "big data" and bringing new methods from complex systems
  • For the analysis of corporate ownership and corporate elites

 

Data collection

  • Macro-data: e.g. Country indicators
  • Micro-data: Surveys
  • Linear regression
  • By hand in Excel

"standard" social science framework

Data wrangling

Data

analysis

Theoretical understanding --> Hypothesis --> Test

(2) Modeling data-missingness

(6) Non-linear modeling

(4) Machine learning:

  • Database merging
  • Data classification

computational social science framework

Data collection

(1) New methods to collect data: Data scraping

Data wrangling

Data

analysis

(3) Set up appropriate data structures

(7) Motif-detection

(5) Description (data viz)

Data --> Patterns/Hypothesis  -->  (Generative mechanisms) --> Theory embedding

(1) New methods to collect data: Data scraping

(2) Modeling data-missingness

(3) Set up appropriate data structures

(4) Machine learning:

  • Database merging
  • Data classification

(5) Description (data viz)

(6) Motif-detection

(7) Non-linear modeling

  1. Ising model to understand information spread across the network of interlocks (w/ Rick Quax)
  2. Higher-order markov chains to understand combinations of countries that are important
  3. SBM to predict the tax rate using corporate networks (w/ Leto Peel)

 

 

 

 

...however this is our weaker part. Most of our research is descriptive.

https://CSSamsterdam.github.io/

deck

By Javier GB

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