# Discrete versus Continuous Algorithms in Dynamics of Affective Decision Making
## 7 Conclusions  
We have considered affective dynamic decision making, where there are several groups of agents choosing between several alternatives. A multistep process of decision making takes into account the utility of the alternatives, their attractiveness, and the inclination of the society members to mimic the actions of others (herding effect). Two possible algorithms are compared, one algorithm treating multistep decision making as a sequence of discrete decisions, while the other algorithm studies the overall process as one continuous action. Dynamic regimes of both algorithms are thoroughly investigated for the case of two alternatives and two groups of agents. One group consists of agents with long-term memory and the other, of agents with short-term memory.  
It is worth stressing that our aim has been not a study of some specific cases, but the general understanding of which of the possible algorithms is more appropriate for the description in a wide range of parameters corresponding to different situations.  
It turns out that the discrete algorithm exhibits much richer behavior that includes the tendency to a stable node, or to stable focus, or to chaotic behavior. Contrary to this, the continuous algorithm always results in the convergence to a stable node. In real life, as empirical studies show, chaotic decision making can occur in the presence of risk and uncertainty. Therefore, it appears that the discrete algorithm is more general, while the continuous algorithm can be treated as an approximation that in some cases gives a reasonable description, while in many other cases it is not applicable. Anyway, from the physiological point of view, multistep decision making better corresponds to a sequence of separate decisions than to a single continuous action.  
For clarity, above, we kept in mind the frequentist interpretation of probability as a fraction of group members. As far as the decision making of a single agent is also a probabilistic process, the theory can also be applied to separate agents possessing different types of memory.  
Instead of separate agents, it is possible to consider the nodes of an intelligent network. For instance, one can keep in mind a neural network, where neurons exchange information in order to come to a state represented by a fixed point. The chaotic performance of an intelligent network can be interpreted as due to some uncertainty in the process of choice. For humans, uncertainty can be caused by the complexity of the studied problem or by defects in a neural network. Overall, affective intelligence, whether artificial or natural, seems to be better described by discrete algorithms than by their continuous approximations. The results of this paper can be useful for the creation of affective artificial intelligence.  
The probabilistic model of affective decision making, considered in this paper, can be extended in several aspects. It is possible to include into consideration more than two groups, for instance differing from each other by memory longevity or by the strength of mutual interactions in the process of exchanging information. It is also possible to take into account time discounting diminishing the utility factors with time. These extensions are planned for future research.  
**Author Contributions:** [PERSON] and E.P.Y. equally contributed to the paper. All have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We appreciate very much discussions with [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.