## 7 Discussion  
Comparing the present approach with other approaches, on the one hand, Structural Equations Modeling (SEM), time series or network models (for instance) perhaps could fit the experimental data. However, we must consider that the SRM constitutes consolidated knowledge and its variables, parameters, and equations have a biological meaning. The SEM approach hardly could respect the hydrodynamic diagram of Figure 1 with the biological sense of its variables, parameters and structure. Note, for instance, that the drug (alcohol and caffeine) pharmacokinetics fits the balance equations of the System Dynamics approach that the hydrodynamic diagram provides. Then, the subsequent SRM equationsare a natural continuation from the pharmacokinetics to the general pharmacodynamics represented by the SRM. In addition, the SRM approach considers the nonlinear dynamical equations that the SEM cannot represent. On the other hand, network models and time series are considered as input-output models and do not provide any explanation of the biological mechanisms underlying personality dynamics after drug consumption. Again, for instance, the pharmacokinetics of the drug, the homeostatic control, the immediate excitation effect or the delayed inhibition effect, which defines the general pharmacodynamics represented by the SRM, i.e., the SRM nonlinear dynamical equations that the other models cannot represent. A single-case experimental design with replication with two participants is used in this study. This fact leads us to a controversy about how to apply the statistical analysis to a single-case experimental design. In a classical book about this topic [32], the authors consider that ANOVA is not the most appropriate statistical analysis for the single-case experimental design. Nevertheless, being aware of the methodological limitations of this bold suggestion, this kind of analysis has been chosen because its results can illustrate very well the implicit dynamics in the effects of drugs, taking into account that the dynamical mathematical model can complement these results and facilitate both their humanistic and their natural scientific interpretations. In fact, the results are clearly in the same vein. Starting from the two mathematical approaches, we can conclude that the difference between both participants in the experiment is evident: P1 presents more statistical dispersion (SD) than P2, but P2 presents more residual dispersion than P1, computed by the SRM predictions. In fact, this result of the SRM represents that P1 feels in a stronger way the dynamical response to both drugs than P2. However, the results provide random residuals in both cases, and so the SRM represents the deterministic predictive part of the dynamical responses. As other studies pointed out [21; 22], the model describes and predicts how the General Factor of Personality (GFP) changes in response to a single dose of caffeine or alcohol. This mathematical model predicts that the lower the GFP trait score is, the higher the response to both caffeine and alcohol intake will be, and better the corresponding model evolution curve will fit the data. Reading the Happiness scores, the relationship between the trait and the state evolution after a single dose intake of caffeine or alcohol is not the same as regarding the GFP in the previous studies. So, the difference between both participants is 10 and 14 points, respectively, for the Euphoria Scale, and the same score (5 points) for the Face Scale, and as we expected similar results in GFP and Happiness scores because they are closely related, this discrepancy should be studied in the future. On the other hand, these results are consistent with the ones obtained for the GFP in the sense that the lower the initial score is the higher level will be achieved during the response to the drug intake. Likewise, the same thing happens regarding Happiness as this study reveals.