This paper investigates between-voter interactions in a social network model of turnout. It shows that if 1) there is a small probability that voters imitate the behavior of one of their acquaintances, and 2) individuals are closely connected to others in a population (the “smallworld” effect), then a single voting decision may affect dozens of other voters in a “turnout cascade. ” If people tend to be ideologically similar to other people they are connected to, then these turnout cascades will produce net favorable results for their favorite candidate. By changing more than one vote with one’s own turnout decision, the turnout incentive is thus substantially larger than previously thought. We analyze conditions that are favorable to turnout cascades and show that the effect is consistent with real social network data from Huckfeldt and Sprague’s South Bend and Indianapolis-St. Louis election surveys. We also suggest that turnout cascades may help explain over-reporting of turnout and the ubiquitous belief in a duty to vote. I thank Robert Bates, Lars-Erik Cederman, Eric Dickson, Paul E. Johnson, Orit Kedar, Gary King, Ferran Martinez I Coma, and Ken Shepsle for valuable feedback on earlier drafts. How does the turnout decision of a single person affect an election? Decision-theoretic models of voting show that the probability of one vote being “pivotal ” in a large electorate is extremely small (Tullock

Using large-scale network analysis I map the cosponsorship networks of all 280,000 pieces of legislation proposed in the U.S. House and Senate from 1973 to 2004. In these networks, a directional link can be drawn from each cosponsor of a piece of legislation to its sponsor. I use a number of statistics to describe these networks such as the quantity of legislation sponsored and cosponsored by each legislator, the number of legislators cosponsoring each piece of legislation, the total number of legislators who have cosponsored bills written by a given legislator, and network measures of closeness, betweenness, and eigenvector centrality. I then introduce a new measure I call ‘‘connectedness’ ’ which uses information about the frequency of cosponsorship and the number of cosponsors on each bill to make inferences about the social distance between legislators. Connectedness predicts which members will pass more amendments on the floor, a measure that is commonly used as a proxy for legislative influence. It also predicts roll call vote choice even after controlling for ideology and partisanship. 1

Voting research is rich in empirical regularities yet a parsimonious theory of voter turn-out that can match the facts has proven to be elusive. This paper argues that voter turnout patterns can be explained by extending the traditional rational voter model to include limited information. A model is presented in which utility-ma~ximizing consumers receive higher payoffs from voting the more confident they are of their vote choice. The model provides an explanation for the most important cross-sectional voter turnout patterns. In addition, it suggests a novel explanation for the post-1960 decline in U.S. participation.

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.