From Personal Responsibility to Political Mobilization: Using Attribution Frames to Overcome Policy Feedback Effects

Public policies that promote personal responsibility while minimizing government responsibility are a key feature of modern American political economy. They can decrease Americans’ political participation on a given issue, with detrimental consequences for the wellbeing of economically insecure families. Can this pattern be overcome? I argue that attribution frames highlighting government’s role in and responsibility for policies may increase people’s propensity for political action on an issue, but only if the frame can increase the salience of their preexisting beliefs about government intervention. Drawing on the case of consumer financial protection, I administer an experiment to determine the effect of attribution framing on people’s willingness to act in support of a popular banking reform. I find that helping people draw parallels between an issue they feel responsibility for and one they accept government responsibility for can boost political engagement on behalf of the original policy.

Characterizing cycle structure in complex networks

A cycle is the simplest structure that brings redundant paths in network connectivity and feedback effects in network dynamics. An in-depth understanding of which cycles are important and what role they play on network structure and dynamics, however, is still lacking. In this paper, we define the cycle number matrix, a matrix enclosing the information about cycles in a network, and the cycle ratio, an index that quantifies node importance. Experiments on real networks suggest that cycle ratio contains rich information in addition to well-known benchmark indices. For example, node rankings by cycle ratio are largely different from rankings by degree, H-index, and coreness, which are very similar indices. Numerical experiments on identifying vital nodes for network connectivity and synchronization and maximizing the early reach of spreading show that the cycle ratio performs overall better than other benchmarks. Finally, we highlight a significant difference between the distribution of shorter cycles in real and model networks. We believe our in-depth analyses on cycle structure may yield insights, metrics, models, and algorithms for network science.