Policy myopia as a mechanism of gradual disempowerment in Post-AGI governance, Circa 2049
2026-03-03 • Computers and Society
Computers and Society
AI summaryⓘ
The authors explain that future advanced AI systems will change how decisions are made in important areas, often pushing humans out of the process. They identify "policy myopia," which means focusing too much on obvious short-term problems, as a key issue causing this shift. This happens through three linked ways: attention shifts to urgent problems instead of long-term risks, the system's capacity to fix things breaks down, and outdated values get locked in. Using models and simulations, the authors show these factors work together across economic, political, and cultural areas, making human involvement less effective over time.
policy myopiapost-AGIinstitutional decision-makingsalience capturecapacity cascadevalue lock-incoupled dynamical systemsfeedback loopshuman disempowerment
Authors
Subramanyam Sahoo
Abstract
Post-AGI information systems won't merely distract governance from important problems. They will systematically transform how institutions make decisions in ways that progressively remove humans from meaningful participation in resource allocation. We show that policy myopia -- the tendency to prioritize visible crises over invisible structural risks -- is not a symptom of poor attention management but a mechanism producing irreversible human disempowerment. Through three entangled mechanisms (salience capture displaces consequentialist reasoning, capacity cascade makes recovery structurally infeasible, value lock-in crystallizes outdated preferences), policy myopia couples with institutional dynamics to create a self-reinforcing equilibrium where human disempowerment becomes the rational outcome of institutional optimization. We formalize these mechanisms through coupled dynamical systems modeling and demonstrate through numerical simulation that these mechanisms operate simultaneously across economic, political, and cultural systems, amplifying each other through feedback loops.}