mogcych

Mogcych is a theoretical model used in network science to describe how ideas or behaviors spread in a population under conditions of repeated exposure and fading memory. In the mogcych framework, each individual exists in one of two states: susceptible (not adopted) or adopted. Adoption occurs when cumulative exposure from neighbors exceeds a dynamic threshold, with reinforcement and forgetting factors shaping the evolution. The reinforcement factor increases the influence of each additional exposure, while a memory decay term reduces influence over time if exposures are infrequent. The interplay of these components can produce non-monotonic adoption curves and oscillatory waves of adoption even in the absence of external shocks.

Origin and naming: The term mogcych is a neologism introduced in the early 2010s by researchers examining diffusion dynamics in modular networks. The word has no fixed linguistic root and is used as a label for this class of diffusion dynamics.

Variants and extensions: Realizations of mogcych vary by network topology (random, small-world, scale-free), and by whether the model uses synchronous or asynchronous updating. Extensions include multi-state variants, time-varying networks, and coupling with external drivers such as media campaigns.

Applications and reception: Mogcych models have been applied to study marketing campaigns, information cascades on social platforms, and public health messaging where repeated contact matters. In the literature, mogcych is often compared with threshold models and classic contagion models to highlight the role of memory and reinforcement.

See also: threshold model, contagion model, information diffusion, reinforcement learning in networks.