Abstract for: Modeling Hype

Technology “hype cycles” are frequently invoked in trade press and managerial discourse to describe the rise and fall of expectations surrounding emerging technologies. While decision makers often act as if such dynamics are real, empirical evidence remains mixed. This study investigates whether feedback interactions among expectations, media attention, and technological progress can endogenously produce hype-cycle patterns. We develop a parsimonious system dynamics model linking capability accumulation, expectation formation, media amplification, and innovation diffusion. Expectations adjust to perceived performance but can be amplified by media and social signaling. Adoption dynamics feed back into reinvestment decisions, shaping technological progress through learning effects and diminishing returns near a performance frontier. Preliminary simulations generate qualitative dynamics consistent with hype-cycle narratives, including expectation overshoot, temporary slowdowns in adoption following performance disappointment, and subsequent recovery as capability improves. Sensitivity analysis indicate that media responsiveness, expectation adjustment delays, and learning rates strongly influence the depth and timing of disillusionment phases. The model offers an endogenous explanation for hype-cycle. Ongoing work will explore calibration with industry evidence and extensions incorporating implementation constraints and heterogeneous adopters. The framework aims to clarify structural conditions under which hype-like dynamics emerge in technology markets to and provide practical advise for managers.