There are two models for scalable information-theoretic systems that are generally applicable to human systems: the small-worlds model and the hierarchal command and control model.
In the case of the hierarchal model, there are no limits on an individual's ability to absorb and process information. However, in the small-worlds model, a connectionist node is saturated with specialized, local information and a small number of connections outside of that immediate proximity. The flattening of a small-worlds network scales negative exponentially with the removal of intermediate layers while the hierarchal model scales linearly. In short, the small-worlds model is realistic for human systems whereas the hierarchal model fails catastrophically with small changes in scaling of human systems. Hierarchal human systems only exist in the real world as abstractions.
In our personal lives, we may want to have a degree of privacy that is disproportional to the good that we would like to gain from our interdependence with others. Thus there is a natural tension for the individual. For the system, there is no such tension. The system finds equilibrium at all points and does not hold itself against imbalances. A large positive imbalance in the individual is immediately offset by smaller negative imbalances in many others within the local neighborhood. The wants and needs of the individual are washed away by the wants and needs of the many.
Fortunately, the information from the individual does not scale in the same ratio as the individual's ability to absorb and process information. The flow of information from the individual is asymmetric to the same scaling factor as the connections of the small-worlds model. The specialized local nodes of the small-worlds model report up the net discrepancies of relatively similar subordinate nodes amplified by their own processing filters. Thus the individual acting through the local connectionist node can transmit information throughout the system to the degree in which it is a useful innovation throughout several layers rather than to the degree to which it is "voted" by the aggregate of the nodes in the local and subordinate connected layers.
In equilibrium, the usefulness of an individual's information dies out as quickly as under the hierarchal model. The virtue of the small-worlds model is in its ability to adapt to changing information on short time scales.
The destruction of privacy of the individual is akin to the substitution of a hierarchal model rather than a small-worlds model. In short, it is an unnatural and statist substitution that leads to systems that are incapable of adaptation on the same time scale as natural exogenous inputs. Hierarchal systems fail to adapt readily and are most often made obsolescent by competitors and outside disturbances. Thus, to safeguard the system at large, the privacy of the individual must be safeguarded.
In the case of the hierarchal model, there are no limits on an individual's ability to absorb and process information. However, in the small-worlds model, a connectionist node is saturated with specialized, local information and a small number of connections outside of that immediate proximity. The flattening of a small-worlds network scales negative exponentially with the removal of intermediate layers while the hierarchal model scales linearly. In short, the small-worlds model is realistic for human systems whereas the hierarchal model fails catastrophically with small changes in scaling of human systems. Hierarchal human systems only exist in the real world as abstractions.
In our personal lives, we may want to have a degree of privacy that is disproportional to the good that we would like to gain from our interdependence with others. Thus there is a natural tension for the individual. For the system, there is no such tension. The system finds equilibrium at all points and does not hold itself against imbalances. A large positive imbalance in the individual is immediately offset by smaller negative imbalances in many others within the local neighborhood. The wants and needs of the individual are washed away by the wants and needs of the many.
Fortunately, the information from the individual does not scale in the same ratio as the individual's ability to absorb and process information. The flow of information from the individual is asymmetric to the same scaling factor as the connections of the small-worlds model. The specialized local nodes of the small-worlds model report up the net discrepancies of relatively similar subordinate nodes amplified by their own processing filters. Thus the individual acting through the local connectionist node can transmit information throughout the system to the degree in which it is a useful innovation throughout several layers rather than to the degree to which it is "voted" by the aggregate of the nodes in the local and subordinate connected layers.
In equilibrium, the usefulness of an individual's information dies out as quickly as under the hierarchal model. The virtue of the small-worlds model is in its ability to adapt to changing information on short time scales.
The destruction of privacy of the individual is akin to the substitution of a hierarchal model rather than a small-worlds model. In short, it is an unnatural and statist substitution that leads to systems that are incapable of adaptation on the same time scale as natural exogenous inputs. Hierarchal systems fail to adapt readily and are most often made obsolescent by competitors and outside disturbances. Thus, to safeguard the system at large, the privacy of the individual must be safeguarded.
