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

We apply our network scale-up model to estimate the number of people in the U.S. who know someone who experienced the terrorist attacks of September 11, 2001 and the number of people who know someone who knows someone who experienced those attacks.

The feasibility of using the network scale-up method to estimate heroin use is described. A random sample was asked “How many people do you personally know ” who use heroin, and how many in other subpopulations – robbery, assault, burglary, auto-theft victims, binge drinkers, and marijuana users – whose size is more accurately known. A model estimated the overall number of persons each respondent knew and the size of each subpopulation. Estimates of the subpopulation are compared with known subpopulation sizes to assess the plausibility of the model. Data came from the 1999 survey evaluating the “Fighting Back ” substance prevention program. Fourteen sites with clear political boundaries were used (n=5892). Heroin use varied from city to city. Rates estimated for heroin use correlated.832 with the level of respondents ’ sense of “crime in their neighborhood. ” The average ratio between the known populations and the estimates is.943. Members of each subpopulation, especially drug users, tended to know more people within their own subpopulation.

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We apply our network scale-up model to estimate the number of people in the U.S. who know someone who experienced the terrorist attacks of September 11, 2001 and the number of people who know someone who knows someone who experienced those attacks.