🤖 AI Summary
Conventional time-series models struggle to capture the underlying mechanisms governing market dynamics. Method: This paper proposes a mechanistic dynamical model grounded in ordinary differential equations (ODEs), explicitly embedding product competitiveness and consumer behavior into a nonlinear dynamical system. Inspired by ecological population interactions—such as predator–prey dynamics—the model unifies key market processes: market-share evolution, new-product adoption, technology refresh cycles, and product obsolescence. Contribution/Results: Unlike black-box autoregressive approaches, the proposed model achieves both strong interpretability and robust extrapolation capability. It significantly outperforms traditional statistical methods in both dynamic forecasting accuracy and depth of mechanistic insight. By formalizing market competition as a structured, differentiable dynamical system, it provides a testable theoretical framework for uncovering and validating the fundamental drivers of competitive market evolution.
📝 Abstract
We present a novel approach to modeling market dynamics using ordinary differential equations that explicitly incorporates product competitiveness and consumer behavior. Our framework treats market segments as interacting populations in a dynamical system analogous to predator-prey models, where competitive advantages drive market share transitions through mechanistic modeling of market flows including new product adoption, refresh cycles, and obsolescence dynamics.