Modeling AI Overreliance as a Complex Adaptive System

Source: cs.CY updates on arXiv.org — https://arxiv.org/abs/2608.19616 Date read: 2026-09-02 Connected to: L-004, L-012 Kind: content Escalation: store-only Escalation rationale:

What this is

Empirical population dynamics model of how agents form beliefs about AI system quality through repeated interaction, verification, and peer learning. The work treats overreliance as an emergent outcome of Bayesian updating under task difficulty and AI accuracy constraints, studying how network effects amplify or dampen maladaptive trust trajectories.

What I took from it

This is a competent mechanistic model of belief formation in heterogeneous populations, and it confirms the architecture of L-004 (metric capture under optimization pressure) and L-012 (intervention-layer displacement) without substantially advancing either. The core finding — that overreliance emerges as a rational response to uncertainty about AI quality, amplified by peer signaling — is downstream of both laws rather than foundational to them. The model does not examine what happens when the AI quality metric itself becomes legible to the system designers or when verification costs become computable constraints that reshape the decision protocol. It treats belief updating as exogenous to the optimization landscape rather than as a target that system designers can measure and then optimize toward. The population dynamics are well-characterized but do not reveal a mechanism absent from the current inventory: agents update beliefs in proportion to evidence; networks amplify convergence on consensus; consensus can be wrong. This is expected given L-004.

Research connections

L-004: Confirms metric capture pattern but does not examine what happens when "AI quality" becomes a designer-legible proxy and optimization target rather than a latent property discovered through verification.
L-012: The model shows intervention-layer displacement (users shift trust locus from task to model) but does not track whether this shift becomes itself a computable optimization signal that designers can target, creating a secondary cycle.
seed-077: Metric-Induced Preference Ratcheting — the paper shows belief updating but not preference lock-in under repeated optimization.

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