This paper introduces a mathematical and computational model elucidating human decision-making processes within complex action spaces. It explores how individuals navigate such spaces under constraints of limited time and cognitive resources, resulting in choices that may be formally sub-optimal yet resource-rational. Drawing inspiration from thermodynamics, we propose a model of the human-like mind for decisionmaking capabilities. Unlike robots, humans face challenges such as emotions, fatigue, and cognitive biases, which can be integrated into cognitive systems, enabling them to add human emotion in decision-making. A key innovation of this research is the incorporation of the monotony of life into the model, influencing decision system behavior over time. Additionally, we derive a time-dependent characteristic equation for mental states based on the second law of thermodynamics, aiming to infuse systems with more human-like behavior by introducing fluctuation dynamics and chaotic oscillations into decision-making processes. We propose that a prolonged depressive phase inherently prompts the model to seek knowledge from society, media, and other sources. This knowledge builds up mental activity, leading to decisionmaking. This computational model can emulate a collection of interacting conscious entities, offering significant potential for applications in consumer research, recommendation systems, mass opinion prediction, wayfinding, and various multimedia domains.