Bayesian Inference of Natural Rankings in Incomplete Competition Networks
This work addresses the ambiguous task of ranking in incomplete networks, which is crucial for reward mechanisms in complex systems, but it appears incremental as it builds on existing ranking methods with a Bayesian approach.
The authors tackled the problem of determining dominance rankings from incomplete competition networks by introducing 'Natural Ranking' and a Bayesian inference model to estimate rankings with error margins. They applied the method to a real-world economic and social network to demonstrate its potential.
Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest -- essential in determining reward or penalty -- is almost always an ambiguous task due to the incomplete nature of competition networks. Here we introduce ``Natural Ranking," a desirably unambiguous ranking method applicable to a complete (full) competition network, and formulate an analytical model based on the Bayesian formula inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in solving issues in ranking by applying to a real-world competition network of economic and social importance.