Projection bias

Projection bias is assuming that future preferences will resemble the preferences we feel right now. Hunger, excitement, loneliness, motivation, and fear masquerade as stable information about what our future selves will want.

Mechanism

How it works

Current states are vivid and immediately accessible, while a different future state is abstract. We therefore use today's desire as a shortcut for tomorrow's choice, forgetting how quickly visceral and emotional states change. The result is a decision optimized for the buyer we are now, not the user we will become.

Examples

Where it shows up

  • A hungry shopper buys food for the whole week as if every future meal will be chosen while hungry.
  • A highly motivated January self buys a year-long gym plan while underestimating how different a tired November self will feel.
  • A vacationing customer chooses a dramatic home feature they will rarely value during ordinary weekdays.
Consequences

What it can distort

  • People overbuy, overcommit, and pay for options that fit a temporary state rather than their ordinary lives.
  • Forecasts of satisfaction look persuasive at the moment of choice but fail when the emotional state that created them passes.
Countermeasures

How to work around it

  • Delay decisions made in a hot state until you can revisit them in a neutral state.
  • Design for the ordinary use case: imagine a typical Tuesday, not the exceptional moment in which you are choosing.
  • Use past behavior and the experience of people already living the choice as stronger evidence than current enthusiasm.
Caveats

Critiques and limits

Some preferences are stable and some current states contain useful information. The bias is strongest when a temporary visceral state is treated as a durable preference without checking how similar choices have felt before.

Taxonomy

Fields of impact

Evidence

How solid is the research?

Mixed — real but conditional

Hot–cold state gaps and state-dependent preference errors are well supported, though 'projection bias' covers several mechanisms and varies substantially by domain.

Research

Relevant papers

Projection Bias in Predicting Future Utility

Loewenstein, G., O'Donoghue, T., & Rabin, M. (2003)

Quarterly Journal of Economics, 118(4), 1209-1248

The Effect of Purchase Quantity and Timing on Variety-Seeking Behavior

Simonson, I. (1990)

Journal of Marketing Research, 27(2), 150-162

Prospection: Experiencing the Future

Gilbert, D. T., & Wilson, T. D. (2007)

Science, 317(5843), 1351-1354

Case studies

Real-world patterns.

Real-world examples showing how Projection bias manifests in practice

Case study

When a Week of Cravings Became a Year of Missed Targets

A real-world example of Projection bias in action

Context

A mid-stage food-tech startup built a meal-planning subscription that lets users pick weekly menus and receive pre-portioned ingredients. Early testers loved the convenience during a seven-day pilot, so the product team used that enthusiasm to set aggressive long-term retention and supply commitments.

Situation

After a successful seven-day trial with 3,200 users, the product and growth teams extrapolated weekly usage into an annual retention forecast and negotiated six‑month supply contracts with ingredient vendors. Pricing, marketing messaging, and hiring plans were all aligned to the projected subscription base they assumed would keep choosing the same meals month after month.

The bias in action

Team members assumed that the preferences and routines demonstrated during the short pilot would persist, failing to account for fluctuating factors like weekends, holidays, social plans, seasonal tastes, and menu fatigue. Product managers used the pilot's 42% weekly reorder rate as a proxy for long-term customer lifetime value, and finance baked that into revenue and hiring models. Because the pilot period captured a temporary state (users motivated to try something new and keen to reduce cooking effort), planners overprojected steady demand and ignored evidence that appetite and schedules change. Decisions were made under the implicit belief that users' current tastes and circumstances would remain stable over months.

Outcome

Within three months of launch active subscribers dropped to 18% of the original cohort, far below the forecasted 42% retention. The company was left with excess inventory orders and monthly vendor minimums they could not meet, leading to $120,000 in perishable waste in the first quarter and renegotiation penalties. Revenue projections missed targets by $1.2 million for the first year and hiring plans had to be frozen, slowing product development and eroding investor confidence.

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Further reading

Recommended books

Entry last reviewed 2026-07-16 · sources verified against the published literature — methodology

Projection bias - The Bias Codex