## 7 Decision Support System and Decisions Strategies
If this is not possible because the state represented by the pair (S 1, E + 1) is not permitted, the second priority is given using an isoenergetic transformation with entropy reduction (direction 2); the third priority is given to a transformation that reduces the entropy of the system by cooling it (direction 3). If none of the three previous transformations are allowed, it means that in the state the system is in, it cannot immediately transition into a state with lower entropy. Therefore, the global strategy attempts to break out of such a deadlock through an energy-increasing isentropic transformation (direction 8). If direction 8 is not possible either, the decision-making strategy tries again with an isentropic option, but this time with energy reduction and relative cooling of the system (direction 4). If it is not even possible to implement an isentropic transformation, it is essential to temporarily increase the entropy. In this case, the DSS tries to implement a strategy with energy increase first (direction 7); if this is not permitted, the decision system involves an isoenergetic transformation with an increase in entropy (direction 6); finally, if none of the previous transformations are permitted, the decision system ultimately implements the strategy corresponding to direction 5. Overall, this decision-making strategy tends to minimize entropy by attempting to maximize energy. In any case, the dynamics are dominated by the minimization of entropy; in other words, the system always tries to reduce entropy first and chooses those with the highest energy among the possible meso-entropic transformations. From a formal point of view, there are three decision levels: (i) directions 1,2,3; (ii) directions 8,4; and (iii) directions 7, 6, 5. These directions ordered exactly in the indicated order create a global decision-making strategy in which the highest energy one is preferred among the meso-entropic transformations. For example, among 1, 2, and 3, 1 is preferred, then 2, and finally 3; between 8 and 4, 8 is preferred; and finally between 7, 6, and 5, on the first try 7 is  
Figure 9: Eight-connected of the current state (the central one colored in red) in the SE plane with the contiguous states (colored in purple). preferred, which is the one with the highest energy of the three, and then the meso-energetic is preferred, that is, the 6, and finally the 5 is preferred, which corresponds to that with energy reduction. Using traffic-light semantics, Figure 10 shows what has been described, where the first three choices are colored green, i.e., those corresponding to transformations with a reduction in entropy, isentropic transformations are colored yellow, while those with an increase in entropy are colored orange. The internal numbers of the state indicate the state itself, while the external numbers indicate their relative hierarchical ordering. Figure 11 shows an example of the algorithm to minimize the entropy S and to increase the energy E. Figure 11 shows how the system, among billions of billions of possible paths (interconnections in red) in a clear regime of high complexity, is able to identify the decision-making path (white curve) that optimizes the state transitions in the entropy-energy plane. Figure 10: DSS_1 algorithm and state hierarchization with traffic-light representation. A narrative approach provides an instantaneous representation of the system considered and under study. In order to make governance decisions, the decision support system presented must offer in sequence:  
* A decision strategy (DS);
* A management strategy (MS);
* An operational strategy (OS). Going into detail, the decisional strategy (DS) is a simulation of the system that allows us to move from a static to a dynamic vision.