Discrete Versus Continuous Algorithms in Dynamics of Affective Decision Making

TL;DR

This paper compares discrete and continuous algorithms in affective decision-making networks, showing that their behaviors can differ significantly depending on parameters, which impacts the choice of modeling approach for intelligent systems.

Contribution

It introduces a comparison of discrete and continuous dynamics algorithms in affective decision-making, highlighting their differing behaviors and implications for modeling intelligent networks.

Findings

Discrete and continuous algorithms can exhibit drastically different behaviors.

The choice of algorithm affects the accuracy of decision-making predictions.

Discrete operation may be more realistic for modeling affective artificial intelligence.

Abstract

The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes into account the rational utility of alternatives as well as the emotional alternative attractiveness. The objective of this paper is the comparison of two multistep operational algorithms of the intelligent network: one based on discrete dynamics and the other on continuous dynamics. By means of numerical analysis, it is shown that, depending on the network parameters, the characteristic probabilities for continuous and discrete operations can exhibit either close or drastically different behavior. Thus, depending on which algorithm is employed, either discrete or continuous, theoretical predictions can be rather different, which does not allow for a uniquely defined description of practical problems. This finding is important for understanding which of the algorithms is more appropriate for the correct analysis of decision-making tasks. A discussion is given, revealing that the discrete operation seems to be more realistic for describing intelligent networks as well as affective artificial intelligence.