Automated modelling in empirical social sciences using a Genetic Algorithm

Bernd Brandl

Automated modelling in empirical social sciences using a Genetic Algorithm

The York Management School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Automated modelling is of increasing relevance in empirical social sciences because of the increasing availability of potentially important variables. The availability of many variables causes uncertainty about which variables should be included in parsimonious models for the explanation of phenomena in social sciences and which variables should be excluded. Given a large number of potentially informative variables this paper argues that the use of genetic algorithms for Bayesian model selection allows the efficient automated identification of an optimal subset of variables. The advantages of using a genetic algorithm as a method for automated modelling is exemplified by the identification of previously unknown but important causal relationships for long-run inflation, the share spent on defence and political rights on the basis of a cross-country data set.

Original language	English
Title of host publication	COMPUTER AIDED SYSTEMS THEORY- EUROCAST 2007
Editors	A Quesada-Arencibia, Jose Carlos Rodriguez, Roberto Moreno-Diaz jr, Roberto Moreno-Diaz
Place of Publication	BERLIN
Publisher	Springer
Pages	912-919
Number of pages	8
ISBN (Print)	978-3-540-75866-2
Publication status	Published - 2007
Event	11th International Conference on Computer Aided Systems Theory - Las Palmas Duration: 12 Feb 2007 → 16 Feb 2007

Conference

Conference	11th International Conference on Computer Aided Systems Theory
City	Las Palmas
Period	12/02/07 → 16/02/07

Cite this

@inproceedings{a5d0f2b65e5d477780250221c2780397,

title = "Automated modelling in empirical social sciences using a Genetic Algorithm",

abstract = "Automated modelling is of increasing relevance in empirical social sciences because of the increasing availability of potentially important variables. The availability of many variables causes uncertainty about which variables should be included in parsimonious models for the explanation of phenomena in social sciences and which variables should be excluded. Given a large number of potentially informative variables this paper argues that the use of genetic algorithms for Bayesian model selection allows the efficient automated identification of an optimal subset of variables. The advantages of using a genetic algorithm as a method for automated modelling is exemplified by the identification of previously unknown but important causal relationships for long-run inflation, the share spent on defence and political rights on the basis of a cross-country data set.",

author = "Bernd Brandl",

year = "2007",

language = "English",

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pages = "912--919",

editor = "A Quesada-Arencibia and Rodriguez, {Jose Carlos} and {Moreno-Diaz jr}, Roberto and Roberto Moreno-Diaz",

booktitle = "COMPUTER AIDED SYSTEMS THEORY- EUROCAST 2007",

publisher = "Springer",

address = "Germany",

note = "11th International Conference on Computer Aided Systems Theory ; Conference date: 12-02-2007 Through 16-02-2007",

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TY - GEN

T1 - Automated modelling in empirical social sciences using a Genetic Algorithm

AU - Brandl, Bernd

PY - 2007

Y1 - 2007

N2 - Automated modelling is of increasing relevance in empirical social sciences because of the increasing availability of potentially important variables. The availability of many variables causes uncertainty about which variables should be included in parsimonious models for the explanation of phenomena in social sciences and which variables should be excluded. Given a large number of potentially informative variables this paper argues that the use of genetic algorithms for Bayesian model selection allows the efficient automated identification of an optimal subset of variables. The advantages of using a genetic algorithm as a method for automated modelling is exemplified by the identification of previously unknown but important causal relationships for long-run inflation, the share spent on defence and political rights on the basis of a cross-country data set.

AB - Automated modelling is of increasing relevance in empirical social sciences because of the increasing availability of potentially important variables. The availability of many variables causes uncertainty about which variables should be included in parsimonious models for the explanation of phenomena in social sciences and which variables should be excluded. Given a large number of potentially informative variables this paper argues that the use of genetic algorithms for Bayesian model selection allows the efficient automated identification of an optimal subset of variables. The advantages of using a genetic algorithm as a method for automated modelling is exemplified by the identification of previously unknown but important causal relationships for long-run inflation, the share spent on defence and political rights on the basis of a cross-country data set.

M3 - Conference contribution

SN - 978-3-540-75866-2

SP - 912

EP - 919

BT - COMPUTER AIDED SYSTEMS THEORY- EUROCAST 2007

A2 - Quesada-Arencibia, A

A2 - Rodriguez, Jose Carlos

A2 - Moreno-Diaz jr, Roberto

A2 - Moreno-Diaz, Roberto

PB - Springer

CY - BERLIN

T2 - 11th International Conference on Computer Aided Systems Theory

Y2 - 12 February 2007 through 16 February 2007

ER -