---
id: "quote-bayesian-agents"
type: "quote"
source_timestamps: ["¶8"]
tags: ["behavioral-economics", "human-nature"]
related: ["concept-willful-ignorance-in-ai", "prereq-bayesian-agent-theory"]
speaker: "Alex Chan"
speakers: ["Alex Chan"]
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-edu-37-employees-not-questioning-ai"
sourceUrl: "https://hbr.org/2026/06/employees-arent-questioning-ai-advice-enough"
sourceTitle: "Employees Aren’t Questioning AI Advice Enough"
---
# Humans are not perfectly rational Bayesian agents

> "Humans interacting with AI are not perfectly rational Bayesian agents. They are strategic, motivated, and sometimes willfully ignorant."
> — [[entity-alex-chan|Alex Chan]]

This quote encapsulates the core behavioral-economics finding of the study: humans do not simply absorb all available information to make optimal mathematical choices. Their interactions with AI are heavily influenced by strategy, motivation, and the desire to avoid discomfort. It is the verbal anchor for [[concept-willful-ignorance-in-ai]] and requires understanding [[prereq-bayesian-agent-theory]] to fully land.

**Enrichment note:** The contrast with a "perfectly rational Bayesian agent" is grounded in standard Bayesian decision theory; the empirical literature documents systematic deviations from optimal evidence acquisition via status quo bias, selective exposure, and motivated reasoning — aligning with Chan's finding that people undervalue explanations even when they complement private information and improve accuracy.