Tolerance acceleration
First let’s explain the functions involved in the computation:
First argument: CA-j state augmentation.
Second argument: CA-0 state which activates the augmentation.
First argument: CA-j which is activated.
Second argument: set point definition.
Third argument: CA-0 state which activates delivery.
p[j]: daily CA-j production.
First argument: CA-j receiving the delivery.
Second argument: CA-j-1 delivering the product.
Third argument: Delivery condition.
Fourth argument: Delivery amount.
p[j]: CA-j daily production.
First argument: state transfer from CA-0 to CA-j
Second argument: CA-0 state which activates the change.
delivery: [2,1, While[p[1] > set point], 2]
change state: [state[1, i+1] = state[0. i], state[0, 43]]; CA-1
maintains its structure.
change state: [state[3, i+1] = state[0. i], state[0, k]] {k,
1, 46}
augment state: [state[2, i+1] += state[1, i], state[0, k]]
{k,1,46}
delivery: [1,1, While[p[1] > set point]: 2] {delivers into the environment}
change state: [state[1, i+1] = state[0. i], state[0, k]] {k,
1, 46}
The surface depicts tolerance rates of 46 * 46 solutions. The central plain is occupied by isolated CA in which delivery was not activated and augment state was not effective. Even in their isolated state they accumulate tolerance at varying rates. Below this plain tolerance accumulation rate declines. (v. Tolerance accumulation. We may now ask what combination of CA states accumulates tolerance fastest?
The surface depicts the average daily tolerance accumulation rate during steady state (homeorhesis. The overall tolerance of the present CA system rises faster than that of the previous one. The above function sets controlling the CA system generate two steady state configurations. Tolerance accumulation rates of both systems are constant. (They cruise in the tolerance space at two constant velocities). Actually the function set applied here accelerates the previous system to a new and healthier homeorhesis.