Claims

In the model, high-quality AI on hard tasks produces the highest regret despite moderate overreliance, because agents over-defer and rarely self-rely

Claim · All claims
Evidence · 1 study · theoretical r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i? High-quality AI on hard tasks yields the highest regret (0.441) but not the highest overreliance (0.119), with very low self-reliance (RSR 0.045). → Ahana Biswas 2026

Evidence

Ahana Biswas 2026

Ahana Biswas. (2026). Modeling AI Overreliance as a Complex Adaptive System. arXiv. https://arxiv.org/abs/2608.19616 doi:10.48550/arxiv.2608.19616

q2 · i? · theoretical · r3

Simulation final states across the four regime cells (Table 3, 20 seeds, CI ≤ 0.01), with regret the primary normative metric. The cell for high-quality AI on hard tasks shows "thehighest regret(0.441)" while "agents over-defer and rarely self-rely (RSR 0.045)".

"high-quality AI on hard tasks has thehighest regret(0.441) but not the highest overreliance (0.119)—agents over-defer and rarely self-rely (RSR 0.045)"

Discussion

Page metadata
Field Value
type claim
id high-regret-overdeferral-good-ai-hard-tasks
title In the model, high-quality AI on hard tasks produces the highest regret despite moderate overreliance, because agents over-defer and rarely self-rely
description In the model, high-quality AI on hard tasks produces the highest regret despite moderate overreliance, because agents over-defer and rarely self-rely
status review
evidence_strength moderate
newest_evidence 2026
generated by process:wiki-ingest on 2026-10-10
trust unverified — no one has confirmed this page's content
sources 1 — ahana-biswas-2026

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