Network models have been widely used as a powerful tool to study complex systems. Existing approaches reconstruct pairwise networks whose interacting pairs of nodes are connected by edges, failing to characterize the high-order architecture of complex systems. In this talk, I will be presenting a statistical physics model that marries evolutionary game theory and ecology theory to leverage the definition and estimation of high-order interactions (HOI). This model can quantitatively reveal both patterns of how a node shapes interactions between pairs of other nodes (active HOI) and how a pairwise interaction influences the third nodes (passive HOI). We coalesce active and passive HOI into hypernetworks, shedding light on the mechanistic understanding of emergent properties of complex systems.