Dynamical Systems Models for Market Evolution: A Mechanistic Alternative to Autoregressive Methods
Aparna Komarla, Max Hill
TL;DR
The paper addresses the need for mechanistic market models beyond autoregressive forecasts. It introduces a dynamical-systems framework using ordinary differential equations that model market flows among segments, including obsolescence, refresh, and de novo entrants, guided by competitiveness scores. The contributions include a modular competitiveness scoring system, flexible demand-allocation mechanisms ranging from winner-take-all to distributed outcomes, and a sample implementation showing how competitiveness shapes market shares under different behavioral assumptions. The approach provides causal, nonlinear, and interpretable insights with practical potential for scenario analysis and strategic decision support in technology markets.
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.