Loss of Human Agency in the Age of Advanced AI – An Agent-Based Model

Most AI risk discussion focuses on abrupt catastrophe. This project studies a quieter failure mode: the gradual loss of human agency — people incrementally giving away their thinking to other entities, whether a human elite or advanced AI itself, each step individually rational and none ever feeling like a loss. The mechanism is self-reinforcing: overreliance on AI erodes confidence and skill, deepening reliance, while populations diverge between those AI empowers and those whose critical thinking atrophies. The core question: under what combination of individual attributes and social reinforcement does AI-mediated manipulation tip a population into self-reinforcing agency loss — and what determines the tipping point, speed, and magnitude of the spread?

We will build an agent-based model in the Schelling (1971) tradition: minimal behavioral rules, aimed at robust qualitative results rather than calibrated forecasts. Heterogeneous agents — differing in education, age, and other susceptibility-shaping attributes, following threshold models (Granovetter 1978) — repeatedly choose to rely on AI-mediated information and delegation. Three mechanisms interact: an individual atrophy ratchet, threshold-based susceptibility to manipulation, and nonlinear social reinforcement. The model does not assume AGI: capability enters as tunable parameters (delegation quality, persuasion strength, personalization), making "how capable must AI be before tipping points appear?" an experimental result, not an assumption.

This fills a specific gap: Kulveit et al.'s Gradual Disempowerment (2025) makes the case conceptually, and RAND's formal model (Moon & Boudreaux 2026, rand.org/t/RRA4817-1) captures the structure of agency loss via decisive coalitions while explicitly flagging degraded participation and dynamics as future work — exactly what this project supplies.

Concretely: formal micro-model specification, mean-field baseline results, Python implementation (Mesa/networkx, open repository), and parameter sweeps mapping tipping surfaces across attribute distributions, network topologies, and the capability dial. Outputs: a publishable computational social science paper (e.g., JASSS), an interactive demo, and a short policy translation on early-warning indicators.

Gradual disempowerment is increasingly recognized as an existential risk pathway in its own right (Kulveit et al. 2025): humans losing the capacity to shape collective outcomes not through a takeover, but through accumulated small surrenders of thinking and choosing. Its danger is precisely that no single step looks catastrophic — so policymakers currently have no way to detect it, measure it, or know when to intervene. This project makes the risk legible and actionable: a formal model with explicit tipping thresholds converts a vague civilizational worry into monitorable quantities — which populations are most susceptible, how fast agency loss spreads, what early-warning indicators precede irreversibility, and which interventions keep societies below the threshold. It directly advances RAND's recommendation (Moon & Boudreaux 2026) that the field develop agency evaluations and reversibility benchmarks. Because AI capability is a tunable parameter, the results also inform a near-term question: whether current persuasive and companion AI systems already warrant intervention under manipulation provisions like the EU AI Act's, before transformative systems arrive.

Mentees will be full intellectual contributors and co-authors: each owns a workstream (formal model, computational implementation, or theory extension), has an equal voice in our weekly discussions, and will substantially shape the model's design and the project's direction.

I have different requirements for all 3 mentees:

Role 1 — Formal Modeler (required):

Graduate-level coursework or equivalent in game theory, microeconomic theory, or formal political theory. Comfortable specifying and analyzing a model with pencil and paper (utility functions, best responses, fixed points); experience with threshold or dynamic models is a plus. Able to write mathematical results clearly for an academic paper.

Role 2 — Computational Mentee (required):

Highly proficient in Python (NumPy/pandas); able to build and maintain a clean, reproducible GitHub repository. Bonus—Built a simulation, agent-based model, or comparable computational project (coursework or toy projects following guides are fine); familiarity with Mesa or networkx is a plus. Comfortable designing and visualizing parameter-sweep experiments.

Role 3 — Theory Mentee (optional; project may proceed without this role):

Background in social choice theory or formal political theory (e.g., has read and can work with Arrow-style frameworks). Interested in potentially extending RAND's coalition-based model of collective agency (Moon & Boudreaux 2026) with degrees of effective decisiveness.

All roles: genuine interest in gradual disempowerment as a risk area, ability to commit 5-10 hours/week for 12 weeks, and willingness to work as part of a small team where model and code are developed jointly.