SYSYApr 9

Invariance of Competition Outcomes in Hypergraph Competitive Dynamics

arXiv:2604.0873061.3h-index: 7
AI Analysis

For network scientists, this provides theoretical justification for the robustness of competition outcomes in complex group interactions, offering guidance for designing selection mechanisms on higher-order networks.

The paper studies Lotka-Volterra competitive dynamics on hypergraphs, showing that winner-take-all-type outcomes are robust to higher-order interactions and determined by a few scalar parameters. Numerical experiments confirm that the outcome taxonomy (WTA/WSA/VWTA) remains similar to standard graphs.

Winner-take-all (WTA)--type selection is a fundamental mechanism in networked competition, yet its dependence on higher-order interactions remains insufficiently understood. We study a Lotka--Volterra competitive dynamics on higher-order networks, where classical pairwise inhibition is augmented by multi-way interaction terms induced by hyperedges of uniform hypergraphs. The proposed model shows multiple competitive outcomes, including WTA, winner-share-all (WSA), and variant winner-take-all (VWTA). The existence, uniqueness and stability of equilibria are rigorously proved through mathematical analysis, which relies on classical stability theory and recent advances in tensor algebra. We show that the eventual selection outcome is relatively insensitive to the hyperedge order and the specific higher-order coupling structure, and is instead determined by a small set of interpretable scalar parameters, such as the ratio between self-inhibition and lateral-inhibition and the external inputs. Numerical experiments support the theory by showing that higher-order interactions affect convergence and steady states, yet yield the similar outcome taxonomy (WTA/WSA/VWTA) as in standard graphs. These results provide a network-scientific explanation of the robustness of WTA-type outcomes under complex group interactions and offer principled guidance for designing selection mechanisms on higher-order networks.

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