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.

翻译：本文研究由具有不同类型记忆（长期记忆与短期记忆）的智能体构成的智能网络中的情感决策动力学。分析基于概率情感决策理论，该理论综合考虑备选方案的理性效用与情感吸引力。本文旨在比较该智能网络的两种多步运算算法：基于离散动力学的算法与基于连续动力学的算法。通过数值分析表明，根据网络参数的不同，连续与离散运算的特征概率可能呈现出相近或截然不同的行为模式。因此，依据所采用算法（离散或连续）的不同，理论预测结果可能差异显著，无法对实际问题给出唯一确定的描述。这一发现对于理解何种算法更适合正确分析决策任务具有重要意义。讨论指出，离散运算在描述智能网络及情感人工智能时似乎更为贴近现实。