## 7 Decision Support System and Decisions Strategies  
Thanks to the analysis described in the previous paragraphs, it is possible to associate each state, understood as a micro-state characterized by the seven CSFs, with a macro-state, i.e., a state characterized by the pair of state variables (S,E). With this completed, it is possible to either fix the variables of the physical hyper-dimension, i.e., latitude, longitude, and temporal location, or you can aggregate the data (the states) spatially, temporally, or spatio-temporally through the theory of moments with the creation of four indexes: (i) a position index (such as the arithmetic mean, the median or the mode); (ii) a dispersion index (such as the range of variation, the variance, the standard deviation); (iii) an asymmetry index; (iv) a kurtosis index. To better understand the usefulness of aggregation, think about having to make a decision which for a system does not concern a specific place corresponding to the latitude, longitude, and time coordinates, but which concerns the system in a complex system such as a neighborhood, city, work or social context, etc. We then understand the need for a greater level of synthesis. Once the macro-state, considered representative of the system, has been identified, immersed in the wider system in space and time, the purpose of the decision support system (DSS) will be to suggest a management strategy, i.e., an operational or governance strategy, which indicates how to manage the considered macro-state, i.e., the system state for a particular pair (S,E), to a more advantageous one. It should also be noted that transitions between micro-states within a macro-state are not the subject of this model. In fact, they would be equi-entropic and equi-energetic transformations, while the aim of the DSS is to suggest strategies that minimize (or reduce) entropy and maximize (or increase) internal energy. The result is achieved through successive steps functional to the final aim. The final goal is the capture of the current state using one of the five fundamental attractors. This aim can be achieved using different algorithms corresponding to different strategies. In other words, the system creates the following conceptual stack at three hierarchical levels:  
* Identification of the seven CSFs (Critical Success Factor);
* Choice of decision strategy for the DSS;
* DSS computation. Subsequently, the results of the DSS, i.e., those in a symbolic form (and in SE space), will be transformed via management strategies, i.e., codified in natural language accessible to all and simple to interpret, in order to implement the strategies of interest, i.e., in seven-dimensional original space (see Figure 8). Figure 8: DSS and related input/output. The strategy employed using the different algorithms has the objective of reaching one of the five attractors, i.e., minimizing entropy. This can be achieved through different decision-making strategies corresponding to different paths to follow in the SE plan. The objective is achieved by imposing the minimization of entropy until it is equal to zero. Since there are five fundamental attractors, there will be five privileged directions (global dominance). In fact, once points of coordinates (S,E) are fixed, we will have eight possible directions (local dominance) in relation to the three-by-three matrix centered on the state of interest and called an eight-connected (see Figure 9). Let us consider a specific algorithm for minimizing the entropy. For each single step, this algorithm implements the following decision-making strategy through the hierarchization of priorities among the eight transformations (i.e., possible directions): 1, 2, 3, 8, 4, 7, 6, 5. Therefore, the first priority is a transformation that leads to a state with lower entropy and greater energy (direction 1).