Analysis of the stochastic evolution of live streaming e-commerce credit supervision under overconfidence
This paper studies the credit supervision mechanism of live streaming e-commerce by constructing an evolutionary game model involving live streaming e-commerce platform,platform sellers,and consumers.Considering the influence of overconfidence and random disturbance on the decision-making process,this study employs overconfidence to characterize the behaviors of the three players.Additionally,the Gaussian White noise and an Itô stochastic differential equation are introduced to develop a dynamical equation.Numerical results show that the overconfidence of the platform has a positive impact on its active regulation strategy selec-tion,and the overconfidence of sellers and consumers has a negative impact on their non-violation strategy and supervision strategy selection.Random disturbance has a negative impact on players'strategy selection.The greater the disturbance intensity,the greater the fluctuation,the slower the convergence speed,and the effect is more pronounced in non-overconfidence scenario.Besides,from the perspective of regulation and control on sellers'strategy convergence speed and changing degree,the regulatory effect of reputation mechanism is more pronounced and has a higher priority;secondly,when there is seller overconfidence,increasing the penalty for sellers is more effective;conversely,reducing the cost of credit supervision works better.
live streaming e-commercecredit supervisionoverconfidencestochastic evolutionary game
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