**Tolerance acceleration**

**two CA
proliferon**. Despite delivering some of its resources into the environment,
CA-1 succeeded accumulating some tolerance. With aid of the **delivery
activation:** [1, 0, set point = p[1]-0.1,state[0,43]] and **change state**: [state[1,
i+1] = state[0. i], state[0, 43]] it
was possible to find the coordinates of a CA-1 with the **fastest tolerance
accumulation**. Its coordinates are {43,42}.

First let’s explain the functions involved in the computation:

**augment state**: [state[j, i+1] += state[j-1, i], state[0,
k]]; {k,1,46}

First argument: CA-j state augmentation.

Second argument: CA-0 state which activates the augmentation.

**delivery activation**: [state[j , set
point = p[j] - 0.1, state[0,43]]

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.

**delivery**: [j, j-1, While[p[j-1] >
set point], 2]

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.

**change state**: [state[j, i+1] = state[0.
i], state[0, k]] {k, 1, 46}

First argument: state transfer from CA-0 to CA-j

Second argument: CA-0 state which activates the change.

**three CA proliferon:** CA-0 controls CA-1 and CA-2.
CA-1 delivers resources to CA-2, which does not deliver resources. CA-0
and CA-1 accumulate tolerance at a constant rate. We shall therefore
focus on CA-3. The function settings are:

**delivery activation**: [state[1, set
point = p[1] - 0.1, state[0, 43]]; Only CA-1 delivers. CA-2 is not activated
and does not deliver.

**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 activation**: [state[1, set
point = p[1] - 0.1, 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}

**Tolerance accumulation
rates **

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?

**Tolerance acceleration**

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.