Voinea - Political Attitudes Computational and Simulation Modeling

1. Chapter 03
2. Chapter 04
3. Chapter 05
4. Chapter 06
5. Chapter 07
6. Chapter 08
7. Chapter 12
8. Chapter 17
9. Chapter 19
10. Chapter 20

Behavioral Computational Social Science
by Riccardo Boero
July 2015
Tipping Points: Modelling Social Problems and Health
by John Bissell (Editor), Camila Caiado (Editor), Sarah Curtis (Editor), Michael Goldstein (Editor), Brian Straughan (Editor)
April 2015
Understanding Large Temporal Networks and Spatial Networks:
Exploration, Pattern Searching, Visualization and Network Evolution
by Vladimir Batagelj, Patrick Doreian, Anuska Ferligoj, Natasa Kejzar
September 2014
Analytical Sociology: Actions and Networks
by Gianluca Manzo (Editor)
March 2014
Computational Approaches to Studying the Coevolution of Networks and Behavior in Social Dilemmas
by Rense Corten
February 2014
The Visualisation of Spatial Social Structure
by Danny Dorling
August 2012

Camelia Florela Voinea

Department of Political Science, International Relations and Security Studies, University of Bucharest, Bucharest, Romania

This edition first published 2016
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To My Parents, Puica and Martinel

Once strongly conceived by both its schoolmasters and apprentices as an exclusive area of qualitative research, political science nonetheless developed during the twentieth century on experimental research dimensions. This systematic orientation took almost one century to get established on solid methodological and epistemological backgrounds. Though quite long, this process has proved wrong all those who either occasionally or systematically blamed, contested or doubted that political science had tremendous potential for quantitative analysis and an everincreasing appetite for paradigmatic change.

Otherwise unavoidable, this process of change was sustained and reinforced by technological advances which enhanced the use of artificial media from single computer platforms to computer networks and the Internet. Huge volumes of public survey data put considerable pressure on the capacity of political science methodology to face the challenge of data processing. This kind of pressure demanded a powerful response. All this transformed the exquisite analytical machine, developed and refined over the entire past century, into what has only lately been established as Experimental Political Science (Druckman et al., 2011). This has revealed, first and foremost, how political science has employed experimental research and rich analytical resources to understand, explain and predict political phenomena, no matter if we talk about the outcomes of elections, about the variability of public opinion or about the public perception and the sustainability of governmental policies. This is but one of the trends which explain the methodological and paradigmatic shift toward enforcing experimental research. This has placed the utility of experimental and analytical research beyond doubt. However, utility alone would not be able to describe the everincreasing research methodological needs in political science. In fact, it has not. Instead, it has complicated the methodology picture in one particular area of political science research: modelling.

It was precisely in the modelling area that the sociological and political methodology research based on empirical data had its golden age: the age of the nomothetic modelling approach working on modelinvariant patterns in huge volumes of empirical data. However, it was also here where its decline started.

The modelling area, especially the realm of the nomothetic theory of modelling, proved to be a true battlefield for two competing methodological approaches: while one, namely Experimental Political Science, seems to have lost terrain and prestige as its performances diminish after a centurylong dominance and a stable period of development, the other one, namely Computational Political Science, seems to have currently emerged in a sustained (and sustainable) effort to replace the nomothetic modelling paradigm with the complexityoriented paradigmatic alternatives reinforced by the new artificial life technologies. The nomothetic view in the political science methodological picture has finally run out of breath, crashed by mountains of survey data, rigidly anchored in determinism and modelinvariant patterns, stiffened in too static a paradigm.

Notwithstanding high recognition, survey analytical research has been the target of harsh criticism. The reasons, now and then, concern not only measurement issues, but mainly the true capacity of survey data to provide for the modelling of realworld phenomena on large scales and in highly complex contexts. One of the longstanding criticisms against the experimental methods and their analytical approach targets the static perspective provided by the empirical models. In political science research, this issue has a particular relevance, since political phenomena show not only high variability, but also sophisticated degrees of context dependency whose complexity could hardly be captured by empirical data and theoretical modelling. Panel techniques as well as longitudinal analytical studies have thus forced penetration of the mathematicalstatistics theories and instruments aimed at overcoming this weakness. Moreover, the subsequently developed mathematical design of dynamic nonlinear variablebased modelling has added value to the analytical power of the theoretical models. Besides the strong requirements for the processing of massive amounts of survey data in empirical research, the study of the spacetime unfolding of political phenomena raised one more challenge: complexity. To cope with it, a new modelling paradigm was needed. And this reinforced the demands for a change in political modelling methodology.