Tobias Sytsma
How can policymakers effectively manage automation policy to maintain or accelerate economic growth while mitigating wealth distribution inequality in an era characterized by rapidly advancing artificial intelligence (AI) capabilities? This question takes on heightened urgency within the context of global technological competition, particularly between the United States and China, in which competitive dynamics may precipitate rapid AI deployment throughout the economy before coherent policies can be formulated.
In this report, the author confronts AI's economic uncertainty through robust decisionmaking analysis of thousands of potential futures. An economic model is developed that distinguishes between horizontal automation (displacing human labor) and vertical automation (enhancing existing automated processes) while endogenizing policy choices through variable automation incentives.
The methodology simulates outcomes across thousands of scenarios for 81 unique policy packages of automation incentives and disincentives, systematically varying key uncertain parameters, including automation rates, productivity improvements, and the degree of complementarity between tasks in production. By exploring this extensive parameter space, the analysis identifies which policy approaches perform robustly across a wide variety of potential futures rather than optimizing for a single forecast.
Policy performance is evaluated through multiple complementary metrics, including compound annual growth rates of income and inequality, policy regret (measuring opportunity costs relative to optimal choices), and robustness (the probability of achieving desired outcomes across scenarios). Threshold effects and critical parameter values that determine policy success under different objectives are identified.
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