Optimization in e-commerce market network based on value order parameter

This paper aims to provide a new perspective of optimization in a “scale invariant” e-commerce market network. It attempts to do so by building an optimization model based on self-organization theory and complex network. In order to investigate another path leading to some specific structures, order parameter—systemic value, a tradeoff between total cost and efficiency, is the critical optimization constraint. Simulation results reveal a strong correlation between some specific structures of e-commerce market network and order parameter. There are four key insightful findings. First, during the variation of tradeoff coefficient λ from 0 to 1, there are 3 main phases separated by 2 transitions at $$ \lambda_{1}^{*} \approx 0.3 $$ λ 1 ∗ ≈ 0.3 and $$ \lambda_{2}^{*} \approx 0.7 $$ λ 2 ∗ ≈ 0.7 which are so-called self-organization critical points. When $$ \lambda \in [\lambda_{1}^{*} ,\lambda_{2}^{*} ] $$ λ ∈ [ λ 1 ∗ , λ 2 ∗ ] , ordered macrostructure will emerge and the optimization results are more valuable. Second, we find that the system order and systemic value are essentially the same, and optimization of network structure improves the capacity of holding energy, and converts it to systemic value. Third, during the optimization of an initial random network, as cost is decreased, several typical network topology structures emerge. Finally, during the optimization against t with λ = 0.5, we find that there are also 3 phases separated by 2 transitions, and network topology changes.