**This model suggests that there is a learning algorithm of the generic neuronal network. It has been expressed in a mathematical development that is very useful for understanding human decision-making.**

It’s a model that is based on a very simple idea and is simultaneously very profound. This model is referenced by various neurological discoveries, such as that of Dr Eric Kandel, which won him the Nobel Prize in Medicine in 2000.

### Axioms

**First axiom**: "Everybody tends to conserve their affectivity, which is understood as the set of affections that comprise their identity".

**Second axiom**: "All stimulus will be counteracted with an emotion tending towards conserving its affectivity, which will create a relationship with the medium".

### Theorems

**1st Theorem of conservation**: "All human actions can be classified as dual or primal. The former is associated with neuronal reversibility and emotional learning; the latter is associated with irreversibility and unlearning. Dual human action is sustainable and primal human action is unsustainable".

**2nd Theorem of conservation**: "Any human action, whether individual or collective, complies with an open dynamic system tending toward an energetic minimum and the result of which will be a unstable balance or a trend. The systems that possess this double option are named affective ecosystems".

### Sustainability conditions

**1st Condition of sustainability or strong condition**: "The value of a decision must be equal to the capacity of producing it indefinitely." Algebraically, it can be expressed as follows:

Where E is the value of the decision (variable stock) and f the flow needed to carry it out (variable flow mechanism).

**2 ^{nd} Condition of sustainability or weak condition:** "In view of a decision, if there isn’t sufficient capacity to produce it, such sufficiency will be reachable if the person remains in a learning atmosphere or environment". Algebraically this could be expressed as follows:

Where Eo is the value of the initial decision, fo is the insufficient initial flow to carry it out and α is the rate of learning.