We develop a Bayesian procedure for estimation and inference for spatial models of roll call voting. This approach is extremely flexible, applicable to any legislative setting, irrespective of size, the extremism of the legislators ’ voting histories, or the number of roll calls available for analysis. The model is easily extended to let other sources of information inform the analysis of roll call data, such as the number and nature of the underlying dimensions, the presence of party whipping, the determinants of legislator preferences, and the evolution of the legislative agenda; this is especially helpful since generally it is inappropriate to use estimates of extant methods (usually generated under assumptions of sincere voting) to test models embodying alternate assumptions (e.g., log-rolling, party discipline). A Bayesian approach also provides a coherent framework for estimation and inference with roll call data that eludes extant methods; moreover, via Bayesian simulation methods, it is straightforward to generate uncertainty assessments or hypothesis tests concerning any auxiliary quantity of interest or to formally compare models. In a series of examples we show how our method is easily extended to accommodate theoretically interesting models of legislative behavior. Our goal is to provide a statistical framework for combining the measurement of legislative preferences with tests of models of legislative behavior. Modern studies of legislative behavior focus

Theory: Deterministic formal models assume that voters make decisions with certainty while probabilistic models assume that voters' intentions are uncertain. Because of their different assumptions, the models produce different equilibrium results, yet there are no criteria for selecting one over the other. Hypotheses: Both approaches are modeling the same process though probabilistic intentions may reveal more information about voter decisions since they incorporate uncertainty. Methods: Respondents in two surveys randomly received the traditional, deterministic voting questions or new, probabilistic items. Several simple empirical tests are conducted to examine the relative performance of the two question types. Results: Probabilistic questions fare just as well as deterministic questions, indicating that these newer formal voting theories should be taken seriously when modeling elections in which voters are uncertain. The act of voting is probably the single most studied phenome...

In this paper, we address the use of adaptive computational modeling techniques in the field of political economy. The introduction considers the advantages of computational methods. The bulk of the paper describes two computational models: a spatial model of electoral competition and a Tiebout model. At the end of the paper, we discuss what the future may hold for these new techniques. 1

Prospect theory scholars have identified important human decision-making biases, but they have been conspicuously silent on the question of the origin of these biases. Here we create a model that shows preferences consistent with prospect theory may have an origin in evolutionary psychology. Specifically, we derive a model from risk-sensitive optimal foraging theory to generate an explanation for the origin and function of context-dependent risk aversion and risk-seeking behavior. Although this model suggests that human cognitive architecture evolved to solve particular adaptive problems related to finding sufficient food resources to survive, we argue that this same architecture persists and is utilized in other survival-related decisions that are critical to understanding political outcomes. In particular, we identify important departures from standard results when we incorporate prospect theory into theories of spatial voting and legislator behavior, international bargaining and conflict, and economic development and reform. Prospect theory has become one of the most influential behavioral theories of choice in the wider social sciences, particularly in psychology and economics (Kahneman, Slovic, and Tversky 1982; Kahneman and Tversky 1979). It has also been

s huge. Our purpose in providing this document, a revised version of a portion of a grant proposal submitted to the National Science Foundation in 1996, is two-fold. One is that it provides a (necessarily selective and incomplete) documentation of some of the impressive advances in the study of elections, public opinion, and related areas that have been based on NES data. Given the central role these data have played in these fields, this document should constitute a helpful review of a crucial part of the relevant literatures. Second, this document should also help stimulate ideas about further research that might tap some of the vast unused potential of these 2 individual studies, as well as the "greater-than-the-sum-of-the-parts" potential of the extensive time series. In the end, the value of the project depends on the ideas and work not just of those who design the study, but those whose inspiration leads to research based on the data. All who venture within are

nalyses address the effect of menu dependence on aggregate outcomes only (such as vote share), or do not provide an explanation of what it is about voter behavior that violates IIA, except for, possibly, an omitted variable bias (Alvarez and Nagler, 1995). The literature does not provide a microfoundation theory of menu dependence in voter behavior that is consistent with empirical evidence. The model proposed takes up this task. The chapter proceeds as follows. The next section introduces the logic of the model in a nutshell. The following section introduces the formal model itself, followed by comparative statics. The next section compares the model to the spatial and directional models, as well as to Iversen's leadership model that combines insights from the two. The next section brings institutions in and explores menu dependence under presidential systems compared to parliamentary systems. The last section concludes. 2 The Intuition of the Model The fol

We develop a Bayesian procedure for estimation and inference for spatial models of roll call voting. Our approach is extremely flexible, applicable to any legislative setting, irrespective of size, the extremism of the legislative voting histories, or the number of roll calls available for analysis. Our model is easily extended to let other sources of information inform the analysis of roll call data, such as the number and nature of the underlying dimensions, the presence of party whipping, the determinants of legislator preferences, or the evolution of the legislative agenda; this is especially helpful since generally it is inappropriate to use estimates of extant methods (usually generated under assumptions of sincere voting) to test models embodying alternate assumptions (e.g., logrolling) . A Bayesian approach also provides a coherent framework for estimation and inference with roll call data that eludes extant methods; moreover, via Bayesian simulation methods, it is straightforward to generate uncertainty assessments or hypothesis tests concerning any auxiliary quantity of interest or to formally compare models. In a series of examples we show how our method is easily extended to accommodate theoretically interesting models of legislative behavior. Our goal is to move roll call analysis away from pure measurement or description towards a tool for testing substantive theories of legislative behavior. 1.

Abstract: In this note we map the Irish policy space, locating both voters and parties on the most salient policy dimensions in Ireland. Estimates of the voter locations are based on the Irish National Election Survey (INES), conducted in 2002. Estimates of the party positions are based on an expert survey of party positions conducted by the authors in late 2002. We show that respondent self-placements on a priori policy scales are highly biased and difficult to interpret, and we rely instead on building scale positions for respondents from their answers to relevant attitude questions in the INES. The results provide a methodological template for locating voters and parties in a common space – a significant problem for any analyst who wants to create an empirical elaboration of a spatial model of party competition. I

The authors develop an agent-based model of dynamic parties with social turnout built upon developments in different fields within social science. This model yields significant turnout, divergent platforms, and numerous results consistent with the rational calculus of voting model and the empirical literature on social turnout. In a simplified version of the model, the authors show how a local imitation structure inherently yields dynamics that encourage positive turnout. The model also generates new hypotheses about the importance of social networks and citizen-party interactions.

When citizens enter the voting booth on election day, they do so in an environment where the stakes are often unclear. Over the last 40 years, political scientists have been interested in the choice dynamics at work in such situations. Recent work in political science has taken up the question of issue voting under conditions of uncertainty—situations in which voters have imperfect information about the policy positions of candidates. But these studies may be limited in important respects. Specifically, to date, the study of vote choice under uncertainty has made a common assumption of quadratic preferences. We move beyond the assumption of a particular spatial utility function and estimate voters ’ preferences for risk. We find that, contrary to the maintained hypothesis in the literature, voters are not globally When citizens enter the voting booth on election day, they do so in an environment where the stakes are often unclear. Over the last 40 years, political scientists have been interested in the choice dynamics at work in such situations. Voters may be uncertain of the competence of the candidates, the likelihood that a given candidate will win the election, and even of