A Novel Information Measure for Predictive Learning in a Social System Setting

We introduce a new theoretical framework, based on Shannon’s communication theory and on Ashby’s law of requisite variety, suitable for artificial agents using predictive learning. The framework quantifies the performance constraints of a predictive adaptive controller as a function of its learning stage. In addition, we formulate a practical measure, based on information flow, that can be applied to adaptive controllers which use hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning. The framework is also useful in quantifying the social division of tasks in a social group of honest, cooperative food foraging, communicating agents. Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise by reducing the amount of sensory information or, equivalently, reducing the complexity of the perceived environment from the agents perspective.