The Dopamine Economy: Algorithmic Reward Systems and the Fragmentation of Human Attention in Digital Markets

This paper develops an interdisciplinary theoretical framework for understanding the large-scale cognitive and behavioral consequences of algorithmically mediated digital engagement systems. Modern short-form content platforms increasingly operate within an attention economy in which sustained user focus functions as a monetizable economic resource. Drawing from behavioral psychology, cognitive neuroscience, attention economics, and human-computer interaction (HCI) theory, this work proposes the Attention-Reward-Exhaustion (A-R-E) framework to model the transition from initial attention capture to cumulative cognitive fatigue and reduced executive control. The framework integrates variable-ratio reinforcement theory, reward prediction error mechanisms, and attentional fragmentation dynamics to explain how algorithmic recommendation systems sustain prolonged engagement loops. A baseline mathematical model is introduced to formalize the competing interaction between reward reinforcement and cumulative cognitive fatigue, followed by an extended continuous-time formulation incorporating temporal accumulation effects. Simulated engagement curves are presented to demonstrate qualitative system behavior under varying fatigue conditions. The paper further examines the broader societal implications of digitally mediated attentional degradation, including reduced deep-work capacity, increased impulsive consumption behavior, and structural shifts in cognitive labor environments. The proposed framework is intended as a conceptual systems-level model designed to support future empirical investigation into the long-term cognitive effects of attention-optimized digital architectures.