Rough AI truth score
67/100
Kahneman's account of judgment under uncertainty and prospect theory remains foundational. The two-systems language is a useful organizing metaphor, not a literal brain partition, while the social-priming examples inherited evidence that later proved substantially less reliable.
Based on three central claims · medium confidence
Prospect theory has generated extensive experimental evidence and durable applications across economics and decision science. Parameters vary by context, stakes, experience, and elicitation, so loss aversion is a robust tendency rather than a universal constant for every choice.
Dual-process distinctions organize substantial evidence on automatic versus controlled cognition, attention, and conflict monitoring. The processes are not two discrete systems with fixed neural locations, and many tasks recruit graded, parallel, learned mechanisms that the memorable labels simplify.
Some perceptual and semantic priming effects are well established, but prominent behavioral-priming demonstrations in the book failed direct replications or showed strong publication-bias sensitivity. The broad effect-size and generalization claims should not be carried forward as settled.
What this number means. It is an AI-generated first-pass judgment of three central factual or causal claims—not a rating, exhaustive fact-check, or human peer review. Claim credits are 100% for supported, 75% for mostly supported, 50% for mixed, and 25% for weak, then averaged and rounded. Lower confidence means the score should move more as better evidence arrives.
Method three-central-claims/0.1.0 · checked 2026-09-01 · 3/3 selected claims assessed · method and source audit