Anchoring Bias Simulator | mysimulator.uk

Anchoring Experiment

Anchor value A 10

Adjustment sensitivity k 0.35

Prior uncertainty σ 12

Mean estimate

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True value

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Anchoring index

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Std. deviation

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How it works

Each of the 180 agents first forms its own private, noisy belief about the answer — a guess drawn from a normal distribution centred on the real value. But before answering, every agent is shown the same anchor number A. Instead of reporting their private guess, people adjust away from the anchor toward it — and that adjustment is systematically too small (Tversky & Kahneman, 1974). The result is the anchoring-and-adjustment heuristic:

guess ~ Normal(true, σ)
estimate = A + k · (guess − A)
anchoring index = (1 − k) × 100%

When k = 1, agents fully correct for the anchor and the histogram centres on the true value. When k is small, the crowd barely moves away from A at all — the whole distribution collapses toward the anchor regardless of what people actually believe.

Anchor value A — the arbitrary number shown before the question (in the classic experiment: a rigged spinning wheel landing on 10 or 65).
Adjustment sensitivity k — how far each person moves from the anchor toward their own belief; low k = strong anchoring bias.
Prior uncertainty σ — how spread out people's private beliefs are before the anchor is shown; more uncertainty gives the anchor more room to dominate.
Anchoring index — the share of the anchor-to-truth gap that survives in the crowd's final answers; 100% = fully anchored, 0% = anchor had no effect.

Real-world relevance: the same mechanism drives first-offer effects in salary negotiation, "was $199 / now $99" pricing, and initial damage-claim figures in legal settlements — whoever states a number first shifts every later estimate toward it.

Anchor A True value Mean estimate Agent estimate

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