13 research outputs found
Proximity, maps and conflict: New measures, New maps and New findings
This article introduces two new datasets. The first is a new interstate distance dataset. It is recognized that different theories regarding distance and conflict will call for different understandings of “distance” and accordingly, ten different types of distance measurement are presented. Moreover, it is argued that in order for a distance dataset to contain accurate distances, it is necessary for it to be based on maps reflecting state border changes over time. As such, a new map dataset is presented, including annualized maps for all states, stored in KML format. It will be shown that the frequent border changes experienced by states can have large impacts on distance calculations. The significance of the relationship between distance and conflict will be tested for the ten different types of distance measurement, not with the aim of finding a “best measure” but in order to demonstrate that distance remains an important variable and that each different form of distance measure can be significant
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Statistical Mechanics of the US Supreme Court
We build simple models for the distribution of voting patterns in a group, using the
Supreme Court of the United States as an example. The maximum entropy model consistent
with the observed pairwise correlations among justices’ votes, an Ising spin glass, agrees
quantitatively with the data. While all correlations (perhaps surprisingly) are positive, the
effective pairwise interactions in the spin glass model have both signs, recovering the intuition
that ideologically opposite justices negatively influence each another. Despite the competing
interactions, a strong tendency toward unanimity emerges from the model, organizing the
voting patterns in a relatively simple “energy landscape.” Besides unanimity, other energy
minima in this landscape, or maxima in probability, correspond to prototypical voting states,
such as the ideological split or a tightly correlated, conservative core. The model correctly
predicts the correlation of justices with the majority and gives us a measure of their influence
on the majority decision. These results suggest that simple models, grounded in statistical
physics, can capture essential features of collective decision making quantitatively, even in
a complex political context