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Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.

Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.

Altruism refers to a willingness to pay a personal cost to make others better off. Past research has established a link between altruism and political participation, primarily among college students. We show that dictator game behavior predicts support for humanitarian norms and donations to Hurricane Katrina victims, suggesting that dictator game allocations are valid measures of altruism. Moreover, we show that this measure of altruism predicts participation in politics, suggesting that past results with students can be generalized to a broader population. Finally, consistent with the argument that altruists only participate when they think doing so will make everyone better off, we show that there is no relationship between altruism and voter turnout in an election where the outcome is distributive and where it is not clear that either political outcome will produce a net societal gain.

A comprehensive understanding of the relational processes that constitute policy networks requires both an analysis of exogenous (i.e. covariates) determinants of the link structures in the network and a study of how the relationships that form the network depend upon each other, the endogenous determinants of the network. The Exponential Random Graph Model (ERGM) is an increasingly popular method for the statistical analysis of networks that can be used for both of these inferential tasks. Conventional interpretation of ERGM results is conducted at the network level, such that effects are related to overall frequencies of network structures (e.g., the number of closed triangles in a network). This limits the utility of the ERGM because there is often interest, particularly in the social sciences, in network dynamics at the actor or relationship levels. We present a comprehensive framework for interpretation of the ERGM at these lower levels, which casts network formation as block-wise updating of a network. These blocks can represent, for example, each potential link, each dyad, and the out or in-going ties of each actor. We contrast this interpretive framework with the popular actor-oriented model of network dynamics. The alternative models we discuss and the interpretation methods we propose are illustrated on estuary network data introduced in Berardo and Scholz (2010).