**Output and efficiency**

Resources generated at one instant (step) are called
**product (input)**. One part
of the product becomes permanent CA
structure (CA core). The other called output (CA rim) is delivered to
the environment.

**Product (input) = permanent structure
(core) + output (rim) **

**delivery**** activation**:
[state[1, set point = p[1] - 0.1, state[0, k]]; {k,1,46}

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

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

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

**change**** state**: [state[2, i+1]
= state[0, k], state[2, k]];

**augment**** state**: [state[1, i+1]
+= state[2, i], While[p[2] > p[1]]

**augment**** state**: [state[2, i+1]
+= state[1, i], While[p[1] > p[2]]

**change state** function. The healthiest CA-1 was triggered at CA-0 state = 22.**
**CA-0 state = 27 did not trigger a mutual stimulation and the CA
remained isolated. At CA-0 state
= 31 the CA had the largest output and
was least healthy.

The graphs depict health and **output**.
Most CA aligned along the centeral line were isolated (like CA-2).
CA-1 is the healthiest with a moderate output.

**Efficiency** is defined as Max[velocity]
/ Min[velocity]. CA-3 is the most efficient since its core is very
narrow.

Output and efficiency are negatively correlated with health. Roughly, the health ordinate is driven by velocity, and the output or efficiency ordinates, by the m-factor. However the m-factor may also accelerate velocity and improve health.

**In summary**:

**Velocity**: Accumulations of resources
in the core.

**Input or product**: resources generated in one step.
(core + rim).

**Output **(rim): Max[velocity] - Min[velocity] / Max[velocity]

**m **: Min[velocity]/Max[velocity]

**Health**: velocity * Min[velocity] / Max[velocity]
= velocity * m

**Efficiency**: Max[velocity] / Min[velocity]

**Differentiation**

The isolated CA is the least differentiated. As CA interact their structure changes and they differentiate. Differentiation is controled by the m-factor, which actually controls Min[velocity] and Max[velocity]. Both may applied to measure differentiation. First let's define a CA state vector:

State[CA] = {velocity, output, efficiency).

State[CA-0] = {velocity0, output0, efficiency0}. The isolated CA-0
(stem process) serves as reference.

Differentiation[CA] = { (velocity-velocity0)^2 + (output-output0)^2
+ (efficiency - efficiency0)^2} ^0.5

Differentiation[CA] normalized = Differentiation[CA] / Norm[Differentiation[CA]].