Dan Ariely · 2008

Predictably Irrational

The Hidden Forces That Shape Our Decisions

Rough AI truth score

50/100

The book vividly communicates genuine context effects in valuation and choice. Its strongest promise—that a suite of striking experiments reveals stable, predictable laws—has aged poorly amid failed replications, disputed analyses, and retractions in Ariely's wider research program.

Based on three central claims · medium-low confidence

The three claims

01Mostly supported

Relative comparisons, anchors, defaults, and the price of zero can systematically change what people choose and value.

Behavioral economics contains robust evidence that valuations depend on reference points and choice sets rather than only stable intrinsic preferences. Effect sizes, mechanisms, and boundary conditions vary, and memorable demonstrations should not be treated as interchangeable proof of one law.

Nobel scientific background on behavioral economics Nobel scientific background on decision under uncertainty

02Mixed

Irrational behavior is sufficiently stable and predictable that simple experiments reveal general rules for everyday decisions.

Many biases recur across studies, making systematic departures from textbook rationality a sound conclusion. Replication, cultural variation, learning, incentives, selection, and experimental demand often shrink or reverse particular effects, limiting rule-like generalization from small demonstrations.

Nobel scientific background on behavioral economics Nature report on psychology replication results

03Weak

The book's striking experimental examples form a dependable evidence base for its broad conclusions.

Some examples rest on established behavioral effects, but Ariely's research record includes retracted and contested studies, including falsified data in later dishonesty work. Problems in adjacent studies do not automatically refute every chapter, yet they materially lower confidence in unreplicated demonstrations.

Psychological Science retraction record Data Colada forensic analysis of the dishonesty dataset

Other claims worth checking

Social and market norms can crowd one another out.
Ownership can raise subjective valuation.

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