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Social interactions are rich, complex, and dynamic. One way to understand these is to model interactions that fascinate us. Some of the more realistic and powerful models are computer simulations. Simple, elegant and powerful, tools are available in user-friendly free software to help you design, build and run your own models of social interactions that intrigue you, and do this on the most basic laptop computer. Focusing on a well-known model of housing segregation, this Element is about how to unleash that power, setting out the fundamentals of what is now known as 'agent based modeling'.
Building on the Cambridge Element Agent Based Models of Social Life: Fundamentals (Cambridge, 2020), we move on to the next level. We do this by building agent based models of polarization and ethnocentrism. In the process, we develop: stochastic models, which add a crucial element of uncertainty to human interaction; models of human interactions structured by social networks; and 'evolutionary' models in which agents using more effective decision rules are more likely to survive and prosper than others. The aim is to leave readers with an effective toolkit for building, running and analyzing agent based modes of social interaction.
Empirical social science often relies on data that are not observed in the field, but are transformed into quantitative variables by expert researchers who analyze and interpret qualitative raw sources. While generally considered the most valid way to produce data, this expert-driven process is inherently difficult to replicate or to assess on grounds of reliability. Using crowd-sourcing to distribute text for reading and interpretation by massive numbers of nonexperts, we generate results comparable to those using experts to read and interpret the same texts, but do so far more quickly and flexibly. Crucially, the data we collect can be reproduced and extended transparently, making crowd-sourced datasets intrinsically reproducible. This focuses researchers’ attention on the fundamental scientific objective of specifying reliable and replicable methods for collecting the data needed, rather than on the content of any particular dataset. We also show that our approach works straightforwardly with different types of political text, written in different languages. While findings reported here concern text analysis, they have far-reaching implications for expert-generated data in the social sciences.
Summary: Although political science in Ireland got off to an earlier start than almost anywhere else (with a first chair appearing in 1855, and the oldest current established chair dating back to 1908), it has faced the same challenges as those encountered elsewhere in Europe. These include a difficulty in establishing autonomy in relation to adjacent disciplines, and a problem in maintaining its own integrity given the diversity of its subfields. Nevertheless, the discipline was able to record steady progress from the 1960s onwards, as the number of staff members grew and the infrastructural support base improved. Especially since the economic crisis that began in 2008, however, the discipline has come under stress, with many of the best qualified and most mobile young academics leaving for posts abroad in a context of domestic austerity. The discipline has survived this development, though, and has been significantly reinforced by links at European level. These have helped in the development of the political science curriculum (notably, as a consequence of the “Bologna process”), and in encouraging research (an area in which the European Consortium for Political Research played a big role). The capacity of the discipline to grow and thrive, and to survive budgetary setbacks, has been assisted by its popularity with students and its continuing relevance to policy makers.
It is now 60 years since one of the dominant figures of international politics, Hans Morgenthau (1955, p. 439), observed that “today the curriculum of political science bears the unmistakeable marks of its haphazard origins and development.” We might expect that, well into the twenty-first century, this generalisation would no longer hold true: that decades of teaching and research would have resulted in a streamlined discipline with an agreed methodology and clearly defined priorities for analysis.
The current study of the state of political science in Ireland, however, will show that in this country, at least, this is not the case – that, as in other European countries, political science continues to be methodologically divided and extraordinarily diverse in focus.
Government formation in multiparty systems is of self-evident substantive importance, and the subject of an enormous theoretical literature. Empirical evaluations of models of government formation tend to separate government formation per se from the distribution of key government pay-offs, such as cabinet portfolios, between members of the resulting government. Models of government formation are necessarily specified ex ante, absent any knowledge of the government that forms. Models of the distribution of cabinet portfolios are typically, though not necessarily, specified ex post, taking into account knowledge of the identity of some government ‘formateur’ or even of the composition of the eventual cabinet. This disjunction lies at the heart of a notorious contradiction between predictions of the distribution of cabinet portfolios made by canonical models of legislative bargaining and the robust empirical regularity of proportional portfolio allocations – Gamson’s Law. This article resolves this contradiction by specifying and estimating a joint model of cabinet formation and portfolio distribution that, for example, predicts ex ante which parties will receive zero portfolios rather than taking this as given ex post. It concludes that canonical models of legislative bargaining do increase the ability to predict government membership, but that portfolio distribution between government members conforms robustly to a proportionality norm because portfolio distribution follows the much more difficult process of policy bargaining in the typical government formation process.
Chin up; chest out; don't panic, yet. Yes, we who work in universities do face epic challenges described eloquently by King and Sen. Yes, we have indeed mostly ignored these—or rather have tended to wring our hands and wail operatically before planting our heads deeply in the sand. We will certainly experience massive technological disruptions of our traditional modus operandi and have only dim notions about how to respond to these. And all of us, if we are brutally honest, are deeply complicit in contributing to an unsustainably rising cost curve that, unchecked, will blow apart the very system that currently sustains our comfortable lives. These sorry truths are self-evident.
The Comparative Manifesto Project (CMP) provides the only time series of estimated party policy positions in political science and has been extensively used in a wide variety of applications. Recent work (e.g., Benoit, Laver, and Mikhaylov 2009; Klingemann et al. 2006) focuses on nonsystematic sources of error in these estimates that arise from the text generation process. Our concern here, by contrast, is with error that arises during the text coding process since nearly all manifestos are coded only once by a single coder. First, we discuss reliability and misclassification in the context of hand-coded content analysis methods. Second, we report results of a coding experiment that used trained human coders to code sample manifestos provided by the CMP, allowing us to estimate the reliability of both coders and coding categories. Third, we compare our test codings to the published CMP “gold standard” codings of the test documents to assess accuracy and produce empirical estimates of a misclassification matrix for each coding category. Finally, we demonstrate the effect of coding misclassification on the CMP's most widely used index, its left-right scale. Our findings indicate that misclassification is a serious and systemic problem with the current CMP data set and coding process, suggesting the CMP scheme should be significantly simplified to address reliability issues.
People who talk about politics talk sooner or later about positions of political actors, be these citizens, voters, activists, or politicians. It is hard to have a serious discussion about the substance of real politics without referring to where key actors stand on important matters. Position implies distance (between two positions); distance implies movement; movement involves direction and can only be described relative to some benchmark. Indeed, it is difficult to analyse real political competition without using positional language and reasoning. This is why the spatial model is one of the ‘workhorse’ models of political science (Cox, 2001).
Spatial models of party competition typically involve two species of agent: voters and politicians. Voters have preferences about political outcomes. Politicians compete for voters’ support by offering policy packages, in essence promised outcomes that appeal to these preferences. Voters’ preferences are typically assumed to be single-peaked over the set of potential outcomes. This implies an ideal point for each voter, describing the most-preferred outcome. Less-preferred outcomes are described as points in some cognitive space that are increasingly far from this ideal. Although not a logical necessity, many models of party competition assume the set of voter ideal points to be both exogenously given and mapped into a common space. Basis vectors of real-world political spaces are typically interpreted as policy dimensions. Examples of such policy dimensions include economic left-right, social liberal-conservative, and foreign policy hawk-dove. In the ‘proximity-voting’ models that are common characterisations of voting in large electorates, each voter is assumed to support the politician offering the policy package closest to their ideal point. More complex assumptions may be made about strategic behaviour by voters. While these may be appropriate in small voting bodies where the voter has some realistic rational expectation that a single vote will affect the outcome, we do not consider these assumptions realistic in relation to voting in very large electorates, which is the setting we concern ourselves with here. Hinich and Munger provide an accessible introduction to spatial models of party competition (Hinich and Munger, 1994, 1997). Austen-Smith and Banks provide a comprehensive technical overview (2000, 2005).
Long-standing results demonstrate that, if policy choices are defined in spaces with more than one dimension, majority-rule equilibrium fails to exist for a general class of smooth preference profiles. This article shows that if agents perceive political similarity and difference in ‘city block’ terms, then the dimension-by-dimension median can be a majority-rule equilibrium even in spaces with an arbitrarily large number of dimensions and it provides necessary and sufficient conditions for the existence of such an equilibrium. This is important because city block preferences accord more closely with empirical research on human perception than do many smooth preferences. It implies that, if empirical research findings on human perceptions of similarity and difference extend also to perceptions of political similarity and difference, then the possibility of equilibrium under majority rule re-emerges.
In “A Robust Transformation Procedure,” Martin and Vanberg (2007, hereafter MV) propose a new method for rescaling the raw virgin text scores produced by the “Wordscores” procedure of Laver, Benoit, and Garry (2003, hereafter LBG). Their alternative method addresses two deficiencies they argue exist with the transformation of virgin text scores proposed by LBG: First, that the LBG transformation is sensitive to the selection of virgin texts, and second, that it distorts the reference metric by failing to recover the original reference scores when reference texts are scored and transformed as if they were virgin texts. Their proposed alternative is “robust” in the sense that it avoids both shortcomings. Not only is MV's transformation a welcome contribution to the Wordscores project but also the critical analysis on which it is based brings to light a number of assumptions and choices that face the analyst seeking to estimate actors' policy positions using statistical analyses of the texts they generate. When first describing the possibility of rescaling the raw virgin text estimates, we emphasized that our
particular approach to rescaling is not fundamental to our word-scoring technique but, rather, is a matter of substantive research design unrelated to the validity of the raw virgin text scores… Other transformations are of course possible. (LBG, 316)
To explore more fully into the assumptions and choices behind alternative transformations and the research designs which motivate them, we offer the following comments.
Mixed-member electoral systems require voters simultaneously to cast ballots in single-member districts (SMD) and multimember, proportional representation (PR) constituencies. It may be that not all parties offer candidates in both electoral contexts, however. In this event would-be voters for some parties may find themselves ‘frustrated’ by the restricted choice menu on offer in the SMD, being effectively forced to split their vote between different parties. Here we explore the different behaviours of frustrated voters in the 1996 mixed-member election to Italy's Chamber of Deputies, characterizing these as being either in some sense non-strategic (concerned above all with the relative policy platforms of candidates) or strategic (concerned above all to influence the eventual composition of government). Using an extended method for ecological inference, we parameterize and estimate rates of different types of ticket-splitting at the district level, and link the degree of what we characterize as strategic voting to the relative policy distance between the respective local representatives of the Italian pre-electoral coalitions.
The Social Logic of Politics: Personal Networks as Contexts for
Political Behavior. Edited by Alan S. Zuckerman. Philadelphia: Temple
University Press, 2005. 368p. $72.50 cloth, $25.95 paper.
The central premise of this edited collection is set out with
admirable clarity on the first page of the opening chapter: “It is
both obvious and well-known that the immediate social circumstances of
people's lives influence what they believe and do about politics.
Even so, relatively few political scientists incorporate these principles
into their analysis” (p. 3). Alan Zuckerman tackles this problem
with a selection of chapters, written by authors with a range of
intellectual pedigrees, that set out to show how what is “both
obvious and well-known” can be incorporated into rigorous political
science. The individual chapters are too numerous and diverse to review in
detail here. What is perhaps more useful is to consider the extent to
which, taken together, they map out a potentially fruitful line of future
development for the discipline.
This paper first reviews a number of epistemological and methodological issues relating to the estimation of party policy positions, particularly in a comparative context, with special reference to the methodology of ‘expert surveys’. It is argued that expert surveys, as systematic summaries of the views of country specialists, have a particular role in assessing the content validity of other types of estimates of party policy positions. The paper moves on to analyze the positions of Japanese political parties in a comparative context, using results from a new 47-country expert survey. Attention is paid both to the substantive policy content of the left–right dimension in Japan, and to the locations of Japanese parties in policy spaces, relative to the locations of comparable parties in other political systems.
This paper proposes a model that takes the dynamic agent-based analysis of policy-driven party competition into a multiparty environment. In this, voters continually review party support and switch parties to increase their expectations; parties continually readapt policy positions to the shifting affiliations of voters. Different algorithms for party adaptation are explored, including “Aggregator” (adapt party policy to the ideal policy positions of party supporters), Hunter (repeat policy moves that were rewarded; otherwise make random moves), Predator (move party policy toward the policy position of the largest party), and “Sticker” (never change party policy). Strong trends in the behavior of parties using different methods of adaptation are explored. The model is then applied in a series of experiments to the dynamics of a real party system, described in a published opinion poll time series. This paper reports first steps toward endogenizing key features of the process, including the birth and death of parties, internal party decision rules, and voter ideal points.
The estimation of vote splitting in mixed-member electoral systems is a common problem in electoral studies, where the goal of researchers is to estimate individual voter transitions between parties on two different ballots cast simultaneously. Because the ballots are cast separately and secretly, however, voter choice on the two ballots must be recreated from separately tabulated aggregate data. The problem is therefore of one of making ecological inferences. Because of the multiparty contexts normally found where mixed-member electoral rules are used, furthermore, the problem involves large-table (R × C) ecological inference. In this chapter we show how vote-splitting problems in multiparty systems can be formulated as ecological inference problems and adapted for use with King's (1997) ecological inference procedure. We demonstrate this process by estimating vote splitting in the 1996 Italian legislative elections between voters casting party-based list ballots in proportional representation districts and candidate-based plurality ballots in single-member districts. Our example illustrates the pitfalls and payoffs of estimating vote splitting in multiparty contexts, and points to directions for future research in multiparty voting contexts using R × C ecological inference.