Decision context

Comparing plans & pricing

Use these evidence-reviewed lenses when comparing subscription tiers, product plans, vendor offers or recommendation menus where the way options are arranged may change what looks attractive.

Use this context when

Start from the situation, not a label.

Evidence-reviewed lenses

4 patterns worth testing, not diagnosing.

well documented, context-dependentEvidence

Decoy Effect – when an inferior option changes the comparison between better options

Does removing the inferior or strategically positioned option change my preference between the remaining plans?

The decoy effect is a well-documented family of context effects in which adding an inferior or otherwise strategically positioned alternative can change choice shares among the original options. The classic attraction effect uses an asymmetrically dominated decoy that is worse than the target but not necessarily worse than the competitor. The effect is not guaranteed: its size depends on the geometry of the options, prior preferences, task design and other moderators, and preregistered replications have found both successful and weak or null results.

well supported, highly context-dependentEvidence

Default Effect – when a pre-selected option changes what people choose

Which plan applies if I do nothing, and is that automatic outcome being mistaken for preference?

Pre-selecting an option often increases the chance that people choose it, but the size of the default effect varies greatly across domains and designs. A 2019 meta-analysis of 58 studies found a substantial average effect together with strong heterogeneity, including some studies with little or even reversed effects. Staying with a default should therefore not be treated as a direct measure of strong preference or as proof that the default improved welfare.

well-supported for numerical judgments; strength depends on anchor type and contextEvidence

Anchoring Effect – when a starting number pulls later estimates toward it

Which displayed price, budget or recommendation became the starting point for my value judgment?

Anchoring is a well-supported effect in which an initial numerical value can pull a later estimate toward it. A 2026 meta-analysis covering 2,601 effect sizes found a large overall effect, but also substantial variation across studies. The effect should not be treated as a rule that every number changes every judgment: incidental anchors, anchors from a different dimension, clearly random values, incentives, and some debiasing conditions were associated with smaller or null effects.

risky-choice framing is robust; broader framing types have different evidenceEvidence

Framing Effect – when equivalent descriptions lead to different choices

Are equivalent features, gains, losses or rates described differently across the options?

Framing research covers several distinct paradigms. Risky-choice framing, where equivalent outcomes are presented as gains or losses, is a well-established effect with substantial evidence across many studies. A classic meta-analysis found a reliable small-to-moderate average effect with strong variation across designs, and later reviews and metastudies support broad generalizability. Attribute framing also has supporting evidence, while goal or message framing is less consistently established. These should not be treated as one identical effect caused by any change in wording.

Decision workflow

Turn the context into observable questions.

  1. 1

    List the serious options and the attributes that matter to your goal before using badges, highlights or recommended labels.

  2. 2

    Remove any suspected decoy and compare the remaining options again. If your preference changes, inspect what contrast the extra option created.

  3. 3

    Identify the no-action outcome separately. A default can influence choice even when no decoy is present.

  4. 4

    Hide or replace the starting price, target or recommendation when practical and make an independent estimate of value before reconciling it with the displayed number.

  5. 5

    Rewrite important attributes in matched units and neutral language so equivalent differences are easy to compare.

  6. 6

    Choose on expected value and fit to the actual need. Conversion, popularity and a 'recommended' badge are not substitutes for the decision criteria.

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Practice this context

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