A multiple binary Markov Chain model for explaining the behaviour of diverse biological phenomena

The behaviour of many biological systems can be attributed to that of a large number of units, with each unit swinging between two competing states. During the past few years efforts have been made (e.g., CHUNG and KENNEDY, 1996) to describe such discrete systems using a multiple binary Markov chain model. Here we explore the gamut of these models and classify their behaviour into five qualitatively distinct types, corresponding to subregions of the parameter space. It is suggested that these model behaviours may correspond to behaviours observed in nature. A simple method for fitting the model to data is presented.