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- Herausgeber: John Wiley & Sons
- Kategorie: Geisteswissenschaft
- Serie: Wiley Series in Computational and Quantitative Social Science
- Sprache: Englisch
Political Science has traditionally employed empirical research and analytical resources to understand, explain and predict political phenomena. One of the long-standing criticisms against empirical modeling targets the static perspective provided by the model-invariant paradigm. In political science research, this issue has a particular relevance since political phenomena prove sophisticated degrees of context-dependency whose complexity could be hardly captured by traditional approaches. To cope with the complexity challenge, a new modeling paradigm was needed. This book is concerned with this challenge. Moreover, the book aims to reveal the power of computational modeling of political attitudes to reinforce the political methodology in facing two fundamental challenges: political culture modeling and polity modeling. The book argues that an artificial polity model as a powerful research instrument could hardly be effective without the political attitude and, by extension, the political culture computational and simulation modeling theory, experiments and practice.
This book:
- Summarizes the state of the art in computational modeling of political attitudes, with illustrations and examples featured throughout.
- Explores the different approaches to computational modeling and how the complexity requirements of political science should determine the direction of research and evaluation methods.
- Addresses the newly emerging discipline of computational political science.
- Discusses modeling paradigms, agent-based modeling and simulation, and complexity-based modeling.
- Discusses model classes in the fundamental areas of voting behavior and decision-making, collective action, ideology and partisanship, emergence of social uprisings and civil conflict, international relations, allocation of public resources, polity and institutional function, operation, development and reform, political attitude formation and change in democratic societies.
This book is ideal for students who need a conceptual and operational description of the political attitude computational modeling phases, goals and outcomes in order to understand how political attitudes could be computationally modeled and simulated. Researchers, Governmental and international policy experts will also benefit from this book.
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Veröffentlichungsjahr: 2016
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Table of Contents
Cover
Title Page
Preface
Political Science …
… and Computational Modelling
Out of the Shadow
Past Challenges and Answered Questions
Approach
Goals
The Picture
References
Acknowledgements
Introduction
A Half‐Century‐Long History
Emergent Area
Now and Then: Methodology Inertia
First Research Programmes
The Challenge of Political Culture
The Assault
A ‘Two‐Way Ticket’ Approach
Criteria in Model Selection: The Addressed Modelling Aspects
References
Part I: SOCIAL AND POLITICAL ATTITUDE MODELLING
1 Attitudes
Attitudes in the Philosophy of Mind
Attitudes in Social Psychology
In Search of Definition
Cognitive Modelling of Attitude Change
Web Resources
References
2 Political Attitudes
Conceptual Modelling Backgrounds
Attitude Research in the Early Days of Social Psychology …
… and Political Psychology
Attitudes in Political Science
The Ages
About the ‘Model’ in Philosophy, Logic and Science
Near and Far Future Trends in Political Attitude Modelling Research
References
Part II: SOCIAL AND POLITICAL INFLUENCE MODELS OF ATTITUDE CHANGE
3 Voting Choice Computer Simulation Model
Conceptual Model
Operational Model
Political Attitude
Philosophy of Computational Modelling and Simulation
References
4 Community Referendum Model
Conceptual Aspects: The Individual Agent
Operational Model
Philosophy of Simulation
Conclusions
References
Part III: THE ROLE OF PHYSICAL SPACE IN POLITICAL ATTITUDE MODELLING
5 Social Impact Theory and Model
Physical Space and Social Influence Modelling
Modelling Social Pressure as a Force Field
Social Impact Theory (1981)
References
6 Dynamic Social Impact Theory and Model
What Does It Take To Change a Static Theory Into a Dynamic One?
Historical Context of Nonlinear Models in Social Psychology
Conceptual Model
Computer Simulation Model
Simulation Modelling
Generations of Computer Simulations
The Relevance of Social Impact Theories for Political Attitude Modelling
Web Resources
References
Part IV: POLITICAL ATTITUDE APPROACHES BASED ON SOCIAL INFLUENCE, CULTURE CHANGE AND COLLECTIVE ACTION MODELLING
7 Culture Dissemination Model
Modelling Principles
Web Resources
References
8 Diversity Survival Model
Conceptual Model
Persuasion Process: Political Attitude Change
Web Resources
References
9 Collective Action Modelling
Political Contagion Model
Closing Remarks
Web Resources
References
Part V: MULTIDIMENSIONAL SPATIAL MODELS
Attitude Change Modelling
10 The System Dynamics Modelling Paradigm
Hierarchical Models
References
11 Multidimensional Attitude Change Models. Galileo
Galileo Model
Web Resources
References
Part VI: POLITICAL COGNITION MODELLING
Information Processing, Affect and Cognition
General Picture and Cognitive Background
Political Attitude: The ‘Hot Potato’ of the 1960s
Political Information Processing
Motivated Reasoning
12 The JQP Model
Political Cognition and Political Judgment in Political Attitude Change
Conceptual Model
Mechanisms
Web Resources
References
13 Political Attitude Strength Simulation Modelling
Attribute‐Based Modelling of Political Attitudes
The PASS Model
References
Part VII: COMPUTATIONAL AND SIMULATION MODELLING OF IDEOLOGY
Political Attitude and Other Approaches on Ideology: A Brief Review
Definitional Frameworks in Ideology Modelling
Early Ideology Empirical and Computer Models
Spatial Paradigm
Cognitive Paradigm
14 Ideological Polarization Model
The Dynamics of Ideological Distance: Conceptual Aspects
Computational and Simulation Model
References
15 Ideological Landscapes Model
Generated Versus Empirical Ideological Landscapes
Web Resources
References
16 Complex Integrative Models of Political Ideology
Cognitive Affinities Model
Computational and Simulation Modelling
Web Resources
References
Part VIII: POLITY MODELLING
Polity Modelling: The Old and the New Look
17 Polity Instability Models Featuring Ethnic and Nationalist Insurgence
Geopolitical Strategic Competition, GeoSim
Nationalist Insurgency Model
GROWLab: Advances in the Computational and Simulation Modelling of Spatial Conflict Emergence
Web Resources
References
18 Polity Instability Model Featuring Reconstruction after State Failure
Model Definition
Post‐Confrontation Polity Reconstruction Modelling
Conflict Analysis in Virtual States Model
Web Resources
References
19 Polity Dynamics Model Featuring the Relationship between Public Issue Emergence and Public Policy Development
Conceptual Model
Model Structure
Model’s Generative Architecture
Operational Model
Relevance of the Model
Web Resources
References
20 Polity Instability Model Featuring Revolution against Authoritarian Regime
Conceptual Aspects
Open Questions and Closing Remarks
References
Part IX: EPILOGUE
21 Shaping New Science
Why Would Political Attitude Computational Modelling Be Important?
Experimental Versus Computational Political Science
Diversity and Convergence: Current Research Trends
References
Author Index
Subject Index
End User License Agreement
List of Illustrations
Chapter 03
Figure 3.1 The individual agent, its internal representations and the flow of electoral stimulation.
Figure 3.2 Individual actor.
Figure 3.3
Variables, mechanisms and processes involved in the modelling of political influence.
Variables
are used to represent (a) individual attributes and (b) stimuli from the social environment.
Mechanisms
controlling the political influence processes: (a) a threshold mechanism controlling the value of the political interest (i.e. subjective utility); and (b) a cumulative mechanism for predispositions update during the learning process.
Processes
: (a) grey arrows indicate stimulation processes; (b) black arrows indicate influence processes.
Chapter 04
Figure 4.1 Individual agent’s internal representations of the electoral campaign.
Figure 4.2 Community referendum simulation model: structure and processes.
Chapter 05
Figure 5.1 The static model of social impact: the social pressure forces are uniformly distributed around the individual subject.
Chapter 06
Figure 6.1 The dynamic model of social impact. There are two types of social persuasive forces: supportive and persuasive.
Figure 6.2 The social space is a squared grid of n × n cells, each cell representing an individual.
Figure 6.3 The individual agent.
Figure 6.4 Clustering phenomena in the simulations performed with SITSIM. (SITSIM is a software program on the Modeling Social Dynamics website. It has been developed by James Kitts, Michael Macy and Martina Morris under NSF Grant SES‐0433086. http://socdynamics.org/index.html (See Web Resources).)
Figure 6.5
Simulations performed with the NetLogo SITSIM model. The configurations obtained show (a) emergence and (b) polarization phenomena. NetLogo implementation of the SITSIM model follows the original model (Nowak
et al.,
1990). The NetLogo SITSIM model has been implemented by Nigel Gilbert and can be found in the public domain at http://ccl.northwestern.edu/netlogo/models/community/Sitsim (see Web Resources).
Figure 6.6 Computer simulation model.
Figure 6.7 SITSIM cycle of theoretical and experimental research. Reductive simulation procedure. Black: empirically derived theories help guide the computer simulations, and use their results to enhance theoretical discoveries. Grey: computer simulations help evaluate the empirical data, while empirical data help in designing computer programs and driving the next rounds in simulations, until the validation of both simulation results and empirical observations is possible.
Chapter 07
Figure 7.1 The environment is represented as a grid of 10 × 10 cells. Agents are represented as cells on a square grid. Agents are ‘villages’; they are geographically situated in this environment such that in each cell there is an individual agent. Each agent has four neighbours (von Neumann neighbours). Agents interact with each other following simple rules which define their behaviour: (1) selection and (2) cultural change.
Figure 7.2 Culture representation.
Figure 7.3 Two agents have a similar feature and a different feature. An interaction might occur with a probability which is equal to the degree of similarity (i.e. proportion of similar features). In the case that the interaction finally occurs, the agent that initiates the interaction (agent A) selects (at random) another feature from agent B on which they differ and copies it.
Figure 7.4 Experimental results. Simulations were performed on the NetLogo site with the simulator: ‘Dissemination of Culture’ by Iain Weaver (see Web Resources section).
Chapter 08
Figure 8.1 The individual agent has an internal modular structure. The individual agent’s structural design includes four main modules: (a) features, (b) communication and interaction, (c) memory of interaction experience and (d) influence mechanism.
Figure 8.2 An individual agent is characterized in terms of features and traits. The set of features and traits takes a numerical description for each agent. Identical traits allow for similarity‐based interactions to occur.
Figure 8.3 An individual agent is simultaneously influenced by multiple types of networks of interpersonal relations: (1) the place where they live (residence or neighbourhood), (2) the place where they go to work (workplace) or (3) the place where they go for religious services (churches etc.).
Chapter 12
Figure 12.1 The accessibility of any object in the memory is achieved as the cumulative effect of basic (past) activation of the object, the variability of connection links to other objects in the memory (strength and affective congruency) and the role of new information.
Figure 12.2 Attitude construction process cumulates values of the evaluative strength and evaluative tag (valence) for each of the objects connected to a given object i.
Figure 12.3 Online evaluations are history dependent and affect dependent. They cumulate all the affectively charged information from all the objects with which a given object i is connected.
Figure 12.4 Online updating of object’s evaluation: as new information is received, an object’s evaluative tag (valence) is updated by either increasing or decreasing the strength of existing evaluation (if any), such that an evaluation of the object (attitude toward object) is constructed and memorized. When remembering, the valenced evaluation is recalled so that the agent recalls mostly what they liked/disliked in a candidate.
Figure 12.5 Parameter values for memory‐based, online and hybrid processing models.
Figure 12.6 Ideological groups.
Figure 12.7 Campaign information about two candidates is presented to a set of agents belonging to different ideological groups.
Chapter 17
Figure 17.1 GeoSim (Cederman, 1997) and GeoContest (Weidmann and Cederman, 2009). (a, b) GeoSim Model and (c, d) GeoContest demo simulations of the interactions between neighbourhood states. Both models could be employed for geopolitical strategy design. The snapshots were obtained by running demo simulations, which were performed with the GROWLab platform, GROWLab v. 0.9.4 for Windows (56 MB); GeoSim and GeoContest are available for download as options within the GROWLab platform: http://www.icr.ethz.ch/research/growlab/box_feeder/download. GeoSim and GeoContest accessed, downloaded and run: 24 July 2015. See the Web Resources section for this chapter.
Figure 17.2 A simple scheme featuring the ethnic group hierarchical distribution in NIM. (a) Hierarchy levels: centre and periphery. (b) Polity is modelled as a star'like, two‐level hierarchy of ethnic groups.
Figure 17.3 GROWLab concepts for polity modelling. Basic concepts: layer, topology, configuration.
Chapter 19
Figure 19.1 General structural concepts in the RebeLand Model.
Figure 19.2 Emergence of a public issue: the various forms of stress generate a public issue.
Figure 19.3 RebeLand operational model: generative mechanisms (implemented as simulation loops) describing each type of agent, its specific tasks and entry points to the other simulation loops.
Figure 19.4 Individual agent (typical for the general population) and its associated generative mechanism: the emergence of a public issue.
Figure 19.5 City agent and its generative mechanism: development of a public policy.
Figure 19.6 State agent and its generative mechanism: welfare redistribution.
Chapter 20
Figure 20.1 Polity model with two structural components: government and society.
Figure 20.2 Polity stability is achieved as a balance between two coupled processes: growth and decay.
Figure 20.3 Stable polity model.
Figure 20.4 Unstable polity model. Polity resilience to the insurgent tendencies is weakened by the increased number of participants which get free communication access.
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WILEY SERIES IN COMPUTATIONAL AND QUANTITATIVE SOCIAL SCIENCE
Behavioral Computational Social Scienceby Riccardo BoeroJuly 2015Tipping Points: Modelling Social Problems and Healthby John Bissell (Editor), Camila Caiado (Editor), Sarah Curtis (Editor), Michael Goldstein (Editor), Brian Straughan (Editor)April 2015Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution by Vladimir Batagelj, Patrick Doreian, Anuska Ferligoj, Natasa KejzarSeptember 2014Analytical Sociology: Actions and Networksby Gianluca Manzo (Editor)March 2014Computational Approaches to Studying the Co‐evolution of Networks and Behavior in Social Dilemmasby Rense CortenFebruary 2014The Visualisation of Spatial Social Structureby Danny DorlingAugust 2012
Political Attitudes
Computational and Simulation Modelling
Camelia Florela Voinea
Department of Political Science, International Relations and Security Studies, University of Bucharest, Bucharest, Romania
This edition first published 2016© 2016 John Wiley & Sons, Ltd
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Cover Image: DigtialStorm/Getty
To My Parents, Puica and Martinel
Preface
Political Science …
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 ever‐increasing 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 ever‐increasing 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 model‐invariant 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 century‐long 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 complexity‐oriented 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 model‐invariant 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 real‐world phenomena on large scales and in highly complex contexts. One of the long‐standing 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 mathematical–statistics theories and instruments aimed at overcoming this weakness. Moreover, the subsequently developed mathematical design of dynamic nonlinear variable‐based 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 space–time 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.
In this book we are concerned with this change, which started emerging in political science more than half a century ago. Once initiated, the main problem is to understand where it is heading to.
This change process started in the early 1950s and is still going on. It has merged two modelling schools of thought: modelling in social and political sciences, on the one hand, and computational modelling and simulation, on the other hand. What has resulted from this blending is, perhaps, the most important question so far. Answering this question is not a trivial task, and reflection on this issue has guided the project of writing this book.
In order to answer this question, we need to assume a conceptual perspective on social and political modelling in general and on political attitude modelling in particular. Research in these two areas has met a common boundary.
Let us take a look at their separate histories and the side effects of their merging into a paradigm of evaluation for political attitude phenomena.
… and Computational Modelling
Starting with the early 1940s, the computational modelling approach began to take shape in both theoretical and experimental research. John von Neumann and Oskar Morgenstern’s ([1944]2007) work on economic behaviour laid the foundations of game theory, but also the foundations of a new approach in modelling theory: computational modelling. The decade between the mid‐1940s and the mid‐1950s brought the fastest, the deepest and the most amazing advances in computer technology, memory storage capacity and computational speed (Forrester, 1989). It was also the time when digital computation techniques, though in their infancy, suddenly got a modelling flavour, making the same decade and the next one appear as a time of explosive computational modelling development. Jay Forrester laid the theoretical and experimental foundations of the computational modelling of complex systems like organizational, economic and social systems (Forrester, [1956]2003, 1958, 1961, 1964). As theorized by Forrester in the early 1960s (Lane and Sterman, 2011), system dynamics was the first computational modelling paradigm which applied to the study of structural and behavioural dynamics of social systems. In this paradigm, computational modelling is approached in terms which distinguish between three fundamental concepts, that is, the real‐world system as the modelled system, the computational model and the simulation of a computer model, which is necessary in order for the model to exhibit its (designed) behaviour and provide (expected or unexpected) outcomes to be evaluated.
The same period of time covers some other famous theories which marked the later development of computational modelling theories, like Simon’s (1957, 1972) works on bounded rationality as a modelling theory of decision‐making, and the works in social communication and persuasion developed by Carl Hovland and his collaborator, Milton Rosenberg, in the Yale Team (Rosenberg and Hovland, 1960).
It was against this background that electoral studies in general and political attitude studies in particular employed computational modelling as a research methodology. It was perhaps too soon for doing so in political science.
In political science research, the process of paradigmatic change, going from qualitative to analytical and experimental, started to diversify itself. The preferred area was that of electoral studies. At a glance, the history of the American presidential election studies offers a picture of the first challenge: it was during the 1950s that a computational modelling approach seemed to raise for the first time a serious methodological challenge. Two decades later, it became a prevailing one, taking the community somehow by surprise, since very few political science researchers were mastering computer skills in order to face the challenge. By the end of the 1970s, computational modelling research finally took an independent position from the empirical and experimental branch, and issued some true characteristics of a new branch within classic political science. The beginning of the twenty‐first century found the political science community facing a delicate question: Is there a ‘Computational Political Science’ about to be born?
The boost in computational modelling on relevant political science issues appears to be a puzzle in which the computational modelling of political attitudes is but one of the numerous (known or still unknown) pieces: would this turn into a labyrinth‐like puzzle? The computational modelling of political attitudes is but the thread which helps any wanderer achieve a map of this quite sophisticated world and, eventually, find a way out. It is this puzzle that has challenged the construction of this book such that it could achieve its specific structural, explanatory, and prospective goals.
Out of the Shadow
Political attitude modelling might seem to many a quite narrow area of research. Often included in public opinion survey research, individual and collective behaviour research, or in research on beliefs, values or normative systems, political attitudes have always represented a very sensitive poll subject and a too‐complex modelling issue for the analytical power of empirical research. No matter how strong or weak such research approaches are, they are meant to provide answers to the questions concerning the dynamics of political attitudes and the role they play in understanding the short‐, mid‐ and long‐term evolutions of political regimes, governance strategies or policies. The societal demand for these answers is continuously increasing, and the pressure it exerts on political regimes is considerable. Political, social and economic crises, as Europe and the world at large have known during the past years, have emphasized once more the strong societal need for preventing political deadlocks by modelling polity dynamics and predicting its potential contextual evolutions.
As an area of research, the political attitudes domain is anything but narrow. As a matter of fact, its complexity has often narrowed the type of research approach, making it, for quite a long time, a preferred issue for the development of measurement theories and methodologies in public survey research. It is only lately that political attitude modelling research has employed computer technologies and computational methodologies. This has increasingly and considerably opened this subject to sophisticated modelling research.
It is the computational modelling of political attitudes that has effectively got this issue out of the shadow in which it was waiting for more than half a century. Offered thus plainly for a much wider range of research instruments and complex types of investigation, the issue has proved unexpectedly precious for a number of research areas with a quite huge social and political impact: political psychology, political marketing, political persuasion and political communication in electoral campaigns. Not to mention its being highly valuable in impact studies over the high‐risk investment and financial sectors and in financial market research. But first and foremost, it has proved far more valuable for an area of political science research that has been regarded for a long time as a Cinderella: political culture research.
Past Challenges and Answered Questions
This book aims to reveal the power of computational modelling of political attitudes to contribute and to reinforce political science research in facing two fundamental challenges.
One such challenge is the renewal of political methodology, long requested, explained and particularly voiced by Charles Tilly (Tilly, 1995, 2001; McAdam et al., 2001; Goodin and Tilly, 2006). This book aims to present the history and arguments of how political attitude computational modelling has provided the means for methodological advances in political methodology. Each chapter in the book approaches such a research methodological dimension. This is meant to explain the roots of methodological change in political attitude modelling research and what it is heading to.
Another strong challenge is the emergence of a new discipline, namely, Computational Political Science. This might happen in much the same way as Computational Sociology emerged in less than two decades of social simulation research (Squazzoni, 2012). This book tries to aggregate the available research literature and technical reports in searching for the critical mass of qualitative contributions which could provide for a new appearance in the political science range of disciplinary fields.
Both challenges are meant to show that political attitudes – important as they are in political psychology research, with all its implications in areas connected to political participation and collective action – are far more important in political science research for their potentially major implications in explaining both micro‐to‐macro and macro‐to‐micro polity phenomena.
The construction of an artificial polity model has already been approached by several authors all over the world. However, so far it has not proved as effective or as robust as the artificial society model. The explanation we are trying to provide is that a macro polity model and an artificial polity research instrument could hardly be effective without a political attitude and, by extension, a political culture basis. To this end, this book on political attitude computational modelling provides the first brick.
Approach
First, the book follows this process of transformation in conceptual and operational details such that it can reveal the substance of this major paradigmatic change from empirical to computational type.
Second, it evaluates the relevance of the main modelling approaches to political attitude. Political attitudes prove their relevance to two fundamental areas in political science: political psychology and political culture. Both are relevant for modelling political participation and decision‐making in mass publics. Moreover, political culture seems to play a role which might prove essential in feeding the macro (emergent) phenomena back into the micro level of individual behaviours, preferences and choices. The evaluation of the relevance goes from the conceptual level to the operational and the simulation levels of the model.
In order to emphasize the way in which political attitude modelling research has discovered computational technologies and employed them in the working methods, a few basic details are provided about each dimension of this subject: the political attitude dimension and the computational modelling dimension.
Goals
The goals of this book are many: structural, explanatory and prospective.
Structural goals are working goals; they guided the structuring of the initial puzzle of political attitudes modelling approaches as a collection organized on several explicit dimensions which have divided it in ‘parts’. Each ‘part’ thus includes a collection of modelling approaches which satisfies requirements concerning (i) some fundamental contribution to political science research (either conceptual or methodological, or both) and (ii) the relevance of the approach for political science in general and for political attitude and culture research in particular.
The structural goals are meant to organize the initial puzzle of models on several dimensions: historical (temporal dimension), theoretical (conceptual dimension) and operational (methodological dimension). Structuring it, however, is not an easy task. The structural dimensions have been identified and approaches have been selected such that each modelling approach satisfies all criteria (all requirements concerning history, concept, method and relevance).
The chronological dimension was meant to emphasize the history of the development of both conceptual and technological aspects. These two classes of aspects – conceptual and technological – concern both political attitude modelling research and computational modelling research. Moreover, the issue of ‘computational modelling of political attitudes’ involves the ‘merging’ of different (and often independently developed) conceptual and paradigmatic approaches into a single unifying approach, like, for example, the JQP model, which integrates in a single unifying modelling approach several conceptual models developed in attitude research (political information processing, remembering and cognition), on the one hand, and in the artificial intelligence and semantic networks, on the other hand.
Explanatory goals are meant to explain the outcome of a particular combination between concept and method in the computational modelling of some political attitude issue, like change. Such combinations are usually constrained by the fundamental modelling requirement of analogy between model and real‐world phenomenon. It is based on the capacity of the model to represent and reproduce or replicate the real‐world phenomenon which the modelling approach is actually addressing.
The combinations between mechanisms in political and social science modelling, on the one hand, and the mechanisms in computational modelling, on the other hand, constitute one of the subject matter of our approach in this book. Such combinations are subjects of endless debate in the philosophy of science as well as in political philosophy. What is truly relevant to the approach in this book regards a particular, however essential, characteristic of computational modelling: models, no matter if computer models, artificial intelligence models, semantic networks, neural networks, agent‐based models or cellular automata, need simulation in order to produce outcomes. These outcomes are used to evaluate the model’s relevance, validity and effectiveness. Extending and adapting from the way Hartmann (1996: 82) put it, modelling such combinations of psychological, cognitive, social and political mechanisms is a matter of simulation, that is, a matter of reproducing some mechanism(s) by other mechanism(s).
Finally, the prospective dimension of this approach is far‐reaching; it is meant to reveal the role and the contribution that computational modelling of political attitudes might have on the emergence of a new (inter)disciplinary field within political science. Social simulation and computational sociology have become a fundamental reference in this respect; this reference suggests that political science might experience the same phenomenon of disciplinary diversification by the emergence of a new discipline able to involve new (computational and/or artificial life) technologies as support for new research methodologies and new philosophy of modelling approaches.
The Picture
This book is meant to offer a comprehensive picture of the past 80 years in the computational modelling of political attitude research. The book aggregates, for the first time, an overall picture of this field of research by including several most relevant modelling approaches of political attitude phenomena, including various references to the connected issues of (political) belief, value, knowledge, information processing, cognition and behaviour research.
This picture includes different pieces of interest in political attitude research, from the original social attitude measurement research initiated at the beginning of the twentieth century to the highly sophisticated political attitude change models elaborated at the beginning of the twenty‐first century. It is meant to reveal the main orientations in political attitude research by uncovering the conceptual roots, the influences induced by other disciplines, and the tendencies emphasized before and, especially, after the computational modelling technologies were adopted and included in the political attitude research methodologies. Besides a state‐of‐the‐art of this field of research, the book aims to reveal the type of contribution this field might have to political science research in general and to polity modelling in particular.
Since the early days of social and political psychology research, political attitude research has been traditionally developed on an empirical basis. Computer technologies as well as modelling theory offered during the 1950s the necessary support and motivation for approaching classic political science research issues. The best example is represented by the issues usually approached in electoral studies, like the aggregation of voting preferences during electoral campaigns. Computational modelling has transformed political attitude research, traditionally anchored in the public survey paradigm, into a spearhead of political methodology change.
Political attitude research has succeeded in overcoming its own limitations, long induced by the intensive use of self‐report methodology used in collecting survey data, by the early inclusion of explicit modelling aspects, like the evaluation of patterns in the aggregate data which were aimed at serving explanatory purposes, and prediction. The computational modelling era in political science research, in particular in political attitude studies, was initiated during the 1950s, when the computational modelling methodologies were introduced in the American universities and research laboratories with a special focus on electoral studies. This difficult transformation from a qualitative type of research to the experimental and, finally, to the computational type has materialized in a long list of paradigms, both conceptual and methodological, which have been employed in this area of research. The results are not fully obvious, and the long‐term effects of this change process are still to be evaluated.
Camelia Florela VoineaUniversity of BucharestFebruary 2015
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Acknowledgements
I am grateful to Flaminio Squazzoni and Bruce Edmonds for their support and help.
I have greatly appreciated discussions and collaboration with Takuto Sakamoto, Luc Girardin, Edward Fink, Joseph Woelfel, David Redlawsk and Paul Thagard.
I am grateful to Paul Johnson and Jon Krosnick for their support during the early phase of preparation for this book project when their encouragement was decisive.
I would like to emphasize the valuable help and encouragement received from Dr Robert B. Smith, for the documentation he provided to me, and for correspondence and e‐mail discussions about his collaboration with William McPhee. His help is hereby gratefully acknowledged and highly appreciated.
I wish to thank Wiley for making this book project possible and each and every (former and actual) member of the Wiley team for their tremendous effort of publishing this book, for their exquisite collaboration, and for their exemplary attitude toward book and author.
I am grateful to my parents, Puica and Martinel, for their love and support, for their patience and for their sacrifice. I would not have been able to complete this book project without their kind care and support.
Camelia Florela VoineaBucharest, 21 December 2015
Introduction
A Half‐Century‐Long History
Attitude is one of the fundamental concepts in social psychology. A long and complicated conceptual elaboration process was needed to define it. As a late outcome of the philosophical debate on the ‘mind–body’ problem, it goes beyond this classic separation, identifying a locus of human choice and action. Around the mid‐nineteenth century, scholars began thinking of it as a sophisticated concept which combines issues from psychology and sociology, philosophy of mind and philosophy of cognition, emotion and rationality, moral and action. Associating it with the complex process of the historical separation of psychology from philosophy, attitude has become the fundamental concept of a new discipline which emerged at the beginning of the twentieth century: social psychology.
Social psychology has aggregated various research interests in concentrating on attitude studies. As previously spread in various scientific areas going from experimental psychology and psychophysics to sociology or philosophy of mind, this multidimensionality richly endowed it with a strong and deep interdisciplinary character. At the beginning of the twentieth century, when the fundamental research on attitudes started being systematically developed, it was basically focused on definition and measurement (Thurstone, 1928; Thurstone and Chave, 1929; Allport, 1935).
Along with the social psychology research developed in this very period, another domain has addressed the issue of attitude: political psychology. Social psychology has approached attitudes in a more quantitative fashion induced by the strong orientations towards behaviour, experiment and measurement inherited from the psychological research framework of the time. As a noticeable difference, political psychology has approached the concept from the perspective of the individual personality, much dominated by Freudian psychoanalysis and moral evaluations (Lasswell, 1936, 1948).
In a short while after their foundation, both social psychology and political psychology underwent drastic paradigmatic changes. The first wave of change came during the 1950s and was induced by the ‘behavioural revolution’, which seized voting behaviour modelling research for a long time. Despite its indisputable supremacy, the behaviourist paradigm fell into disfavour by the end of the 1970s. It has never truly recovered, though it has never truly surrendered either.
The second wave of change came with the ‘cognitive revolution’. Under the strong impact of the New Look, both social psychology and political psychology changed views. Social psychology replaced the functional paradigm based in behaviourist thinking with the cognitive consistency paradigm, and the Pavlovian ‘stimulus–response’ (S‐R) model of behavioural response to stimuli with the force field model (Katz, 1989). The New Look had a strong impact on political psychology as well, such that the domain opened up for the age of electoral studies: voting behaviour and political belief studies dominated the political science stage until the late 1980s when the issues of political information processing, political judgement and political cognition took the lead. Sustained and stimulated by the influence exerted by social psychology experimental research, political psychology re‐emerged during the 1970s and redefined its domain by including an orientation towards empirical experimentation (Kuklinski, 2009; Druckman et al., 2011; Holbrook, 2011; Iyengar, 2011).
Political attitudes were included in the early experimental developments within social psychology research as a particular kind of attitude traditionally associated with the political space, and especially with the area of electoral studies. Studies of electoral campaigns, candidates and voting behaviour proved, however, that along with social contextual variability, political attitudes underlie the variability arising from both the individuals’ cognitive characteristics and the way they relate to the issues of social and political life. As attitudes address the most basic as well as the most elevated dimensions of social and political life, the interest in political attitudes has thus generated new frontiers in both social and political sciences’ fundamental research by adopting, among others, new methodologies able to cope with the challenging aspects of studying political attitudinal phenomena at the mass level. Modelling, in particular computational modelling, provides for such a methodology. Its impact on political attitude research increased while stimulated by the modelling approaches developed in psychology, sociology and social psychology.
In social sciences, modelling has been used to explain and test theories, improve old ones and build up new theories. To put it in just a few words, the classic nomothetic modelling paradigm is basically a reductionist method to achieve a representation of a real‐world phenomenon. This representation employs a universal principle able to explain why a real‐world phenomenon looks as it looks and not otherwise, and why it behaves as it behaves. It has been intensively, and for a long while almost exclusively, used in the empirical research for acquiring causality‐based explanations of a given phenomenon by identifying the universal law which governs its behaviour. Computational modelling has pushed classic modelling beyond its traditional concepts and limits. The explicit purpose has always been that of achieving more believable models and better explanations.
The computational modelling paradigms have been appreciated for their capacity to bring forth an optimal compromise between the model’s complexity and the complexity of the real world: models succeeded in preserving as much as possible from the complexity of the real‐world phenomena such that their relevant aspects could still be replicated and systematically varied.
In political science, modelling has been employed in its classic mathematical form as a way of expressing a theory by means of a system of equations. Such a model takes on what is relevant about a real‐world phenomenon so as to explain one or more of its structural, functional or behavioural aspects. As a fundamental difference, computational modelling allows for the translation of a theory (mathematical model) to a computational form, thus making possible the model construction and operation in virtual media. The main advantage of virtual experiments resides in their considerable power to tackle data and complexity without the need to involve human subjects in time‐ and resources‐consuming, error‐prone field experiments as traditional empirical research does. Computational media allow for virtual experiments which could be repeated as many times as needed without requiring human intervention and the repeated exposure of human respondents. Moreover, simulation modelling technologies which are often associated with computational modelling allow the replacement of empirical data with generated data, thus reducing the field work or simply avoiding the traditional empirical data collection aimed at model testing. The simulation modelling of the real phenomena provides for both top‐down designs, which are more appropriate to rationalistic models, and bottom‐up designs, which are appropriate to the models based on self‐organization and emergence.
There are other aspects, however, which have fuelled the endless hot debates concerning the meaning of the patterns and of the type of outcomes such simulations provide. Epistemological considerations have long been the battlefield for the pros and cons with regard to the appropriateness of the method for the study of the dynamics of political attitude phenomena in artificial social systems. Though contested and criticized from both inside and outside of social and political methodology areas, the computational modelling of political attitudes (with or without simulation modelling) has provided the proper means to achieve considerable advances in explaining the phenomena generated by political attitude formation and change processes. Such advances would not have been possible on an empirical basis alone.
Emergent Area
Computational modelling has appeared in political attitude research as an auxiliary means of supporting the necessary calculations in the analytical data processing. The first goal it has served is that of increasing the efficiency in the processing of huge amounts of survey and panel data. Thus, from the very beginning, it has played a constant role in enhancing the explanatory and predictive power of an empirical model of individuals’ political preferences and voting choices. Such descriptions were employed by the Columbia Model, the first model to be translated into a computer simulation model of political attitudes, aimed at predicting the political voting choices in U.S. presidential elections. With time, the range of such phenomena has been extended and diversified so as to include not only the relationship between political attitudes and voting behaviour but also their relationships to political beliefs as in the Michigan Model, or political information processing, judgement and cognition as in the John Q. Public (JQP) Model. It has also diversified as a reaction to the fast technological and methodological advances, but also for raising awareness of the increased relevance of the role it could play in providing accounts on the complexity of political attitude phenomena and explaining their dynamics.
Nowadays, political attitude computational modelling research is meant to provide answers to rather complicated questions concerning the political preferences, choices, behaviours, judgements and cognition in individuals, groups and entire societies. The computational aspects combine more often and in increasingly sophisticated ways with simulation modelling technologies and employ sophisticated simulation instruments and media. During the past decade, this mix has offered the most interesting suggestions for understanding what roles information, communication, persuasion, symbols and emotions play in shaping, influencing or changing individuals’ and groups’ political evaluations, judgements, deliberations, action choices and attitudes.
Notwithstanding its impressive, though rather short history, political attitude computational modelling appears as an advanced area of research with powerful approaches in almost all political science aspects from elections, ideology, decision making and polity to interaction, information processing, communication and cognition.
However, one thing should be noted in the first place: political attitude computational modelling is not properly what one might call an established area of research. It might rather be viewed as one which is currently emerging from a puzzle of modelling approaches spread in many areas of psychological, sociologic, social‐psychological, political and economic research. Accumulating a considerable amount of knowledge and methods, political attitude computational modelling seems to make a political science dream come true, that of endowing political science research with a methodology able to provide appropriate support for modelling the complexity of political phenomena. Though not the only one, but perhaps one of the most advanced, it undoubtedly represents a potentially relevant component of a newly emerging discipline of research within the political science domain: a computational counterpart to the already established and highly recognized Experimental Political Science.
First and foremost, political attitude computational modelling brings forth a precious modelling experience and methodology in a political science area which has long proved resistant to change: political methodology. It has been a long while since several political science scholars, especially Charles Tilly, strongly argued and voiced their demands with regard to the necessity of methodological change from the classic nomothetic to other modelling paradigms able to cope with the variability, complexity and dynamics of political phenomena.
Now and Then: Methodology Inertia
The experimental approach has long been a disputable aspect in political science research and remains debatable notwithstanding its impressive advances and the paradigmatic changes it has induced. The experimental research methodology took more than a century to get accepted and systematically employed in political methodology. It is only a couple of years ago that experimental political science acquired an established, highly recognized status and confirmed the decisive role it plays in political science (Druckman et al., 2011).
Its massively dominant status has nevertheless been ‘threatened’ during the past half‐century by a different kind of methodology and epistemology: the arsenal of computational technologies and methodologies based on the virtual experiment, complexity and generative data has undermined the strong, dominant position of the empirical tradition in both experimental and modelling research. The introduction of the new methodology has faced strong opposition. Now and then, ‘methodology inertia’ manifests itself in the same way.
For the particular area of political attitude research, experimentation has been fostered by a massive influence from social psychology research methodology.
Notwithstanding formal agreement of the political science research community on the concept and acknowledgement of its utility, experimental research has often faced opposition from scholars who proved resistant to accepting the new methods and techniques of quantitative evaluations. Their opposition was rooted in a traditional qualitative style of scientific investigation. The opposition to the challenge of methodological change has usually been approached with interdisciplinary training programmes aimed at stimulating methodological interest and training those interested to get new skills and to make use of them. In the political psychology of the 1970s, for example, the opposition towards the experimental research approach was tackled with consistent long‐term programmes of interdisciplinary training of doctoral and post‐doctoral fellows, a tradition initiated at Yale University with an interdisciplinary psychology–politics programme (Iyengar, 2011). Things are not much different nowadays: the same kind of concerns are given academic support in undergraduate and graduate programmes to both students in political science and mature scholars willing to use the computational and simulation tools (Yamakage et al., 2007). This book is aimed at serving this purpose and enduring initiative.
Computational modelling, as well as computational simulation research, has faced this challenge too. The difficulties in getting accepted as modelling research methodologies in political science have concerned the high levels of demanding skills and knowledge about computational technologies. Evaluating the research community’s response to this challenge, Paul Johnson identified a phenomenon which was generalized in social and political sciences during the 1950s and the 1960s: the methodological background of many political science researchers was consistently based in survey methods and analytical tools and much less in computer science, programming skills and even less in computer simulation (Johnson, 1999, pp. 1511–1512).
Approached mainly in the context of theories of democracy, the modelling of political change phenomena required, in the early 1990s, a modelling paradigm change, extensively explained and strongly advocated by Charles Tilly (1995, 2000, 2001). His formulation of the problem was the most direct and, perhaps, the most demanding in what regards the necessity to develop research methods able to cope with the variability of political phenomena, with the recurrent nature of political change processes, and with the context‐ and path‐dependent dynamics of its spatial and temporal evolutions. Moreover, as he explains, the empirical variable‐based, model‐invariant design needs to be replaced by a design based on mechanisms and processes, more prone to uncover the dynamics of phenomena (Tilly, 2000, p. 4). Tilly shows that the dynamic variability of the contextual processes does influence the way political processes are described and explained. Model‐invariant explanations are often too much reductionist since they actually eliminate context. Providing as illustration an example of a village drama in Romania after the 1990s, when people demanded the restoration of their land property rights held before the communist regime came into power, Tilly requires a political process modelling which should take into account the context as it enhances the identification of regular patterns which characterize political phenomena (Goodin and Tilly, 2006: 6). A similar position has been advocated by other scholars in various areas of social and political sciences: in political science by Lars‐Erik Cederman (1997, 2001, 2005), and in social science by Charles Taber (2001).
Notwithstanding such strong positions as well as the criticisms formulated by these and other scholars, the paradigmatic conservatism in political methodology once again proved its resistance to change. Compared with similar methodology revision programmes in sociology and social psychology research, the means to make a political methodological change programme operational and efficient remained poor as long as experimental political science seized the methodological resource. The nomothetic modelling paradigm in the experimental research acquired too powerful a tradition to easily make room for change in political methodology. For a long while, mathematical and empirical modelling not only dominated the methodological scene but also took over the view.
When noted, and finally agreed and accepted, political attitude computational modelling already had a rich past and was looking ahead to a richer future. Not to speak about the political culture theory, a graceful host for much of the latest approaches in political attitude modelling research (though not fully computational). Political attitude computational modelling research thus appeared much as a methodology provider whose know‐how was developed outside political methodology or at the thin border between social and political research methodology. Its value cannot and has not been denied in political methodology, but it has not been praised either. As regards its contribution to the classic political methodology, it brings the conceptual and operational means as well as a rich experimental background for approaching the dynamics, recurrent nature and the context‐dependent aspects of political change processes.
First Research Programmes
The first systematic computational modelling approach of political attitudes was initiated in the late 1950s by a Columbia sociologist, William McPhee, who had the idea of evaluating the public survey data on a computational basis. McPhee is credited as having actually discovered what a political attitude computational model in reality is, how it works and what it does: his page on the Columbia University website, carefully maintained by one of his early collaborators, Robert B. Smith,1 reminds us of a great mind and a visionary research programme leader.
At that time, both computers and public survey methodology were used for the first time in such research: the computer programming tasks required skills almost unknown in social and political research, while surveys brought such huge amounts of empirical data as to require a ‘calculation machine’ for providing the analytical results. McPhee designed a computer simulation in which a three‐process system was able to associate individual voting choices with several dynamic variables describing the individual preferences, and the local social context: the dynamic variation of an individual’s internal predisposition towards one or another of the candidates in the presidential campaign was associated with the variation in the individual’s interest in political participation. This relationship was subjected to the political persuasion exerted by the electoral campaign media communication. The classic paradigm of the small worlds
