  The paper presents a hierarchical Bayesian model for simultaneous inference
of tournament graphs and informant error. From multiple informant reports or
measurement instrument outputs, the model estimates the structure of a
criterion (i.e., true) tournament graph with possibly tied outcomes. Tournament
graphs with possibly tied outcomes are graphs in which there are three possible
states for each unordered pair of graph nodes: node i wins and node j loses;
node j wins and node i loses; neither node wins (i.e., tied outcome). The model
also estimates the rates at which individual informants (or instruments)
mistake the winning and losing dyad members, falsely report a tied outcome, and
falsely report a decisive outcome. The model was developed to infer social
dominance structure from multiple informants' reports, but is potentially
useful for inferring any structure that can be characterized by a tournament
graph, and which is measured from multiple reports.
