Soft Markov chain relations for modeling organizational behavior

Organizations have various neural characteristics in that organizational subsystems interact with each other through communication, influences, and direct actions, each of which can have positive or negative weight, and where architecture and weights can be reconfigured based on subsystem and system output metrics that are compared to overall goals. In a Markov chain model of these interrelations, actions depend on the individual behaviors of particular subsystems, the time at which the subsystem is responding, and the history of occurrences leading up to the response time. Aggregation of effects leading to a result is rarely linear, so a nonlinear weighted sum called “chained soft aggregation” is proposed as an appropriate model. The method is readily combined with any available objective information in a hybrid analysis.