Managing the Transition to an Economy of Freely Chosen Activity under Pervasive General-Purpose Robotic Automation: A Mathematical and Agent-Based Policy Model with a Critical Review of the Literature
This research preprint develops a national-level decision-support framework and agent-based policy model for managing the long-run transition to an economy shaped by artificial intelligence and general-purpose robotic automation. It combines a mathematical system with agent-based simulations to evaluate policy packages involving automation rent taxes, statutory public capital ownership, social dividends, and staged work-time reduction. The study provides a calibration protocol, a roadmap with monitoring indicators, and a replication package to address deep uncertainty in labor markets and inequality.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, policymakers, and modelers studying the long-term economic impacts of artificial intelligence and robotic automation.
What the document contains
The document addresses the long-run economic and social transition under pervasive robotic automation, modeling policy mechanisms such as taxation of automation rents, public capital ownership, social dividends, and working-time reductions across six sectors.
The text does not provide binding legal regulations, actual governmental enactments, or real-world economic estimates, as the numerical levels are purely illustrative.
- A national-level decision-support framework models the transition to an economy with pervasive general-purpose robotic automation.
- The proposed policy vector includes automation rent taxes, public capital participation, social dividends, and work-time reduction.
- An operational index measures the capacity for free activity based on voluntarily usable time and material security.
- Simulations show that managed policy packages significantly reduce income inequality and below-threshold income shares.
- A complete replication package is included to ensure reproducibility of the agent-based model and experiments.
Not stated
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
Description
screening score · 2026-10-24
Fairness
2/5Non-discrimination and equitable treatment
in the illustrative central experiment, the managed package reduces the below-threshold income share from 23.3% to 7.9% and the income Gini from 0.383 to 0.183
Description
screening score · 2026-10-24
Human Dignity
1/5Respect for the inherent worth of persons
An operational index measures the capacity for free activity
Description
screening score · 2026-10-24
Human Oversight
0/5Humans remain in control of consequential decisions
Description
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
Description
screening score · 2026-10-24
Transparency
4/5Openness about how AI systems work and are used
Code, random seeds, tables, and figures are fully reproducible. The deposit includes the full replication package
Description
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
Description
screening score · 2026-10-24
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
An operational index measures the capacity for free activity: voluntarily usable time conditional on material security, skills, motivation, and trust.
Description
screening score · 2026-10-24