The maximal coordination principle in regulatory Boolean networks

We introduce a coordination index in regulatory Boolean networks and we expose the maximal coordination principle (MCP), according to which a cohesive society reaches the dynamics characterized by the highest coordination index. Based on simple theoretical examples, we show that the MCP can be used to infer the influence graph from opinion dynamics/gene expressions. We provide some algorithms to apply the MCP and we compare the coordination index with existing statistical indexes (likelihood, entropy). The advantage of the coordination approach is its simplicity; in particular, we do not need to impose restrictions on the aggregation functions.