The paper proposes a value based UBI funded by a four percent annual tax on company equity and evaluates it with an agent based model inspired by recursive alignment ideas. The concept of viewing policy design through adaptability, rigidity, and failure modes is creative and fits the sprint’s call for resilient governance tools. The author outlines a clear modular framework with heterogeneous households, strategic firms, and government rules, and implements five scenarios in Mesa, reporting time series for inequality, investment, and fiscal balance on page 6 and the triplet of panels in Figure 1 on page 8
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Nonetheless the contribution remains exploratory. Key parameters that drive the simulated paths, such as the elasticity of investment to after tax returns or the stochastic process for firm value manipulation, are only described qualitatively. No calibration to empirical data, robustness checks, or confidence bands accompany the headline Figure 1 results. The decision to average just five runs for each scenario limits statistical power, and the model horizon of thirty years may not capture longer term capital accumulation or demographic feedbacks. The literature engagement focuses on recursive alignment and functional models of intelligence but omits the extensive empirical and theoretical work on UBI finance, wealth taxes, and adaptive fiscal rules.
Links to AI safety are present but indirect. The paper claims that adaptive fiscal policy can mimic recursive alignment principles and avoid stability failures, yet it does not show how the tax design interacts with advanced AI incentives, ownership concentration of compute, or catastrophic misuse channels. Adding explicit pathways from tax funded redistribution to safety outcomes, for example reduced race dynamics or funding for alignment research, would raise the safety relevance.
Technical documentation is thin. The Mesa code, parameter file, and raw outputs are not shared, preventing replication. The framework is verbally specified but lacks formal equations or a flowchart. Tables summarising baseline values, shock sizes, and adaptive rule algorithms would help readers evaluate validity. A sensitivity sweep over tax rates and adaptive triggers, together with comparisons to simpler lump sum or payroll financed UBI baselines, would illustrate the marginal value of the proposed policy.
Suggested next steps
1. Release the model repository with a reproducible environment and add Monte Carlo sensitivity around growth, gaming probability, and tax elasticity.
2. Calibrate initial distributions and TAI productivity shocks to stylised facts from AI macro projections.
3. Expand the literature review to include recent papers on wealth taxation feasibility and dynamic fiscal rules.
4. Map the policy’s impact on safety relevant metrics such as concentration of AI profits or funding for public goods that reduce existential risk.
5. Provide formal notation for production, household, and government behaviour to allow integration with general equilibrium or optimal control extensions.