  Photonic accelerators have recently attracted soaring interest, harnessing
the ultimate nature of light for information processing. Collective
decision-making with a laser network, employing the chaotic and synchronous
dynamics of optically interconnected lasers to address the competitive
multi-armed bandit (CMAB) problem, is a highly compelling approach due to its
scalability and experimental feasibility. We investigated essential network
structures for collective decision-making through quantitative stability
analysis. Moreover, we demonstrated the asymmetric preferences of players in
the CMAB problem, extending its functionality to more practical applications.
Our study highlights the capability and significance of machine learning built
upon chaotic lasers and photonic devices.
