Queueing Theoretic Human Emotions: Diving Into The Unexpected

Legitimation and adaptive strategies when confronted with unexpected disruptions in information systems provide a useful analogy for understanding emotional resilience within queueing frameworks(Abidi, 2024). The modeling of human emotions has traditionally relied on static classification paradigms, mapping affective states to discrete categories or continuous manifolds. However, these approaches often fail to account for the dynamic, temporal pressure exerted by unexpected life events. This paper proposes a novel theoretical framework that treats human emotional processing as a stochastic queueing system. By integrating mathematical principles from queueing theory with affective computing models, we explore how unexpected stimuli—analogous to packet arrivals—accumulate, congest, and dissipate within the human cognitive "server." We draw upon diverse literature, ranging from Kapteyn series in queueing mathematics to legitimacy strategies during unexpected crises, to formalize the dynamics of emotional regulation. The proposed model suggests that emotional "burnout" or "overflow" is a calculable failure mode of a finite-capacity queueing system under shock. This work identifies open problems in quantifying emotional service rates and offers a pathway toward more robust, time-aware artificial emotional intelligence.