  The paper begins by exploring the rationality of ethical trust as a
foundational concept. This involves distinguishing between trust and
trustworthiness and delving into scenarios where trust is both rational and
moral. It lays the groundwork for understanding the complexities of trust
dynamics in decision-making scenarios. Following this theoretical groundwork,
we introduce an agent-based simulation framework that investigates these
dynamics of ethical trust, specifically in the context of a disaster response
scenario. These agents, utilizing emotional models like Plutchik's Wheel of
Emotions and memory learning mechanisms, are tasked with allocating limited
resources in disaster-affected areas. The model, which embodies the principles
discussed in the first section, integrates cognitive load management, Big Five
personality traits, and structured interactions within networked or
hierarchical settings. It also includes feedback loops and simulates external
events to evaluate their impact on the formation and evolution of trust among
agents. Through our simulations, we demonstrate the intricate interplay of
cognitive, emotional, and social factors in ethical decision-making. These
insights shed light on the behaviors and resilience of trust networks in crisis
situations, emphasizing the role of rational and moral considerations in the
development of trust among autonomous agents. This study contributes to the
field by offering an understanding of trust dynamics in socio-technical systems
and by providing a robust, adaptable framework capable of addressing ethical
dilemmas in disaster response and beyond. The implementation of the algorithms
presented in this paper is available at this GitHub repository:
\url{https://github.com/abbas-tari/ethical-trust-cognitive-modeling}.
