Measuring Information Propagation and Retention in Boolean Networks and its Implications to a Model of Human Organizations

A system structure, i.e., how elements of a system are connected, is a key factor for information retention and transmission through its elements. From the system dynamics, i.e., the states of the elements over time, we measure the system’s ability to propagate information through its elements as the pairwise mutual information (pMI) between the elements at moments t and t + L, where L is the minimum path length between the two elements. Information retention is measured with Lempel-Ziv (LZ), a measure of the complexity of transmitted information, from the same time series of states. We propose a combined measure of information propagation and ability to retain information efficiently, to determine optimal structures for information propagation and retention. We present the results on information propagation and retention, as a function of topology (random and small world structures), connectivity, noise and clustering coefficient. The conclusions are applicable in any context where these networks are used to model the system. Here, we apply our findings to a model of human organizations and than propose a generalization of the model to capture more realistic features, such as more complex internal states for elements and simulating information exchange with the environment outside of the system. As more features are incorporated, this model will capture many important features of human organizations, and other complex systems. Key-Words: Organization, System, Information, Complexity, Lempel-Ziv, Information propagation and retention, Knowledge Management, Organizational Development.