Framing effect
The same facts, described differently, lead to different choices.
What it means
The framing effect is the phenomenon whereby logically equivalent descriptions of the same information lead to systematically different choices, depending on whether outcomes are cast as gains or losses or on other features of wording. For risky choices, the standard account runs through reference dependence: a 'loss' frame shifts the reference point so options register as losses and invite risk-seeking, while a 'gain' frame makes them feel like gains and pushes toward risk-aversion. The classic Asian-disease problem shows majorities reversing their preference between identical programs purely on whether lives are described as 'saved' or 'lost.' Framing is not one effect, however: attribute and goal framing behave differently, the mechanism remains debated — with information-leakage and gist-based accounts competing with prospect theory — and its size depends on how transparent the equivalence is, on individual differences, and on whether people are prompted to weigh the alternative frame. It matters in medicine, finance, policy, and marketing, and is a strong reason to present trade-offs in multiple frames.
The original demonstration
Amos Tversky and Daniel Kahneman introduced the effect in 1981 with what became known as the Asian disease problem. Respondents imagined an unusual outbreak expected to kill 600 people and chose between two programs. One group saw the outcomes described in terms of lives saved: one program would save 200 people for certain, while the other offered a one-third chance of saving all 600 and a two-thirds chance of saving no one. Here roughly 72 percent chose the certain program. A second group saw the same outcomes described in terms of deaths: one program would leave 400 people dead for certain, while the other carried a one-third chance that nobody would die and a two-thirds chance that all 600 would. Here roughly 78 percent chose the gamble. The two problems are arithmetically identical, since 200 saved is 400 lost, yet the majority preference reversed. Only the descriptor changed while the numbers were held constant, which is what made the study the canonical demonstration that how options are worded, not merely their substance, moves choice.
Why it happens
The standard explanation comes from prospect theory, Kahneman and Tversky's 1979 account of decision under risk. Outcomes are evaluated not in absolute terms but as changes from a reference point, and the value function that maps those changes is concave for gains and convex for losses. Concavity for gains yields risk-aversion, since a sure gain is preferred to a gamble of equal expected value, while convexity for losses yields risk-seeking. A gain frame anchors the reference point so the options read as gains, inviting the safe choice; a loss frame shifts it so the same options read as losses, inviting the gamble. This is often summarised loosely as loss aversion, but the reversal is driven more precisely by the differing curvature of the value function on either side of the reference point than by the steeper slope for losses that loss aversion actually names. The mechanism is also not settled, and it applies mainly to this risky-choice family. Competing accounts hold that a speaker leaks information through the choice of frame, so the two descriptions are not truly equivalent, and that people reason over the rough gist of a problem rather than its precise quantities.
What the evidence shows
Framing is not one effect but several. Levin, Schneider, and Gaeth distinguished three kinds in 1998: risky-choice framing, as in the disease problem; attribute framing, where a single feature is cast positively or negatively, such as ground beef labelled 75 percent lean rather than 25 percent fat; and goal framing, where a message stresses either the benefit of acting or the cost of not acting. These behave differently, with attribute framing the most consistent and goal framing the weakest and least reliable. The first large synthesis, Kuhberger's 1998 meta-analysis of roughly 230 effect sizes, put the average risky-choice effect at a small-to-moderate value of about d = 0.31, with wide variation traced to features of the task rather than to a single stable magnitude. A 2018 re-appraisal by Steiger and Kuhberger reanalysed those data with a p-curve method that corrects for publication bias; it found the underlying effect reliable and somewhat larger than the raw estimate, near d = 0.52, with little sign of heavy p-hacking, while again showing that there is no single framing effect, only a family whose size depends on the manipulation. Direct replication has been kinder to the classic case than to many textbook findings: a large multi-lab project reproduced the original reversal across dozens of samples and settings, robustly though generally at a smaller magnitude than in 1981.
Limits and caveats
The effect has clear boundary conditions. When both frames are placed side by side, or the equivalence between them is made transparent, the reversal shrinks and can disappear, which is one reason presenting a decision in more than one frame is a practical corrective. It is stronger between subjects, where each person sees only one version, than within subjects, where the same person confronts both. Individual differences matter too: people higher in need for cognition, in numeracy, or in deliberate rather than intuitive thinking are less swayed, and prompting decision-makers to give reasons or to consider the other frame narrows the gap. A deeper caveat concerns the premise itself. The information-leakage critique argues that logically equivalent frames need not be informationally equivalent, since describing a glass as half full rather than half empty can hint at which way the level last moved, so part of what looks like irrational sensitivity to wording may be a reasonable reading of what the wording implies. This does not dissolve the effect, but it complicates the tidy story that people merely respond to meaningless surface form.
Where it shows up
The stakes are highest where quantities are unavoidable but their presentation is a choice. In medicine, the same treatment tends to look more attractive described by its survival rate than by its mortality rate, and this can shift both patient and clinician preferences. In finance and insurance, whether a charge is labelled a surcharge or a forgone discount, or a return is framed against one baseline rather than another, changes willingness to accept it. Public messaging on health, energy, and taxation leans on gain and loss frames to encourage uptake, and marketing routinely selects the more flattering attribute frame. Because the choice of words is never fully neutral, the durable lesson is procedural rather than clever: for consequential trade-offs, show the figures in more than one frame and check whether the preference survives the reframing.
Examples
A treatment described as '90% survival' is chosen far more often than the same one described as '10% mortality.'
A hospital describes a surgical option to two comparable groups of patients: one is told the procedure has a 90 percent survival rate, the other that it carries a 10 percent mortality rate. More patients accept the operation under the survival wording, even though the two statements report the same outcome.
A packaged food is labelled '80 percent fat-free' on one production run and 'contains 20 percent fat' on another. Shoppers rate the first version as healthier and higher quality despite the identical composition.
A payment processor lets a merchant present the cost of paying by card either as a 'card surcharge' added to the price or as a 'cash discount' subtracted from a higher list price; customers object less to the discount framing even when the final amounts match.
An energy provider tells one set of households how much they would save each winter by adding insulation and another how much they lose each winter by going without it; the loss wording produces more sign-ups even though the underlying figures are the same.
First described in Tversky & Kahneman (1981).
Key references
- Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458. doi.org/10.1126/science.7455683
- Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. doi.org/10.2307/1914185
- Levin, I. P., Schneider, S. L., & Gaeth, G. J. (1998). All frames are not created equal: A typology and critical analysis of framing effects. Organizational Behavior and Human Decision Processes, 76(2), 149-188. doi.org/10.1006/obhd.1998.2804
- Kuhberger, A. (1998). The influence of framing on risky decisions: A meta-analysis. Organizational Behavior and Human Decision Processes, 75(1), 23-55. doi.org/10.1006/obhd.1998.2781
- Steiger, A., & Kuhberger, A. (2018). A meta-analytic re-appraisal of the framing effect. Zeitschrift fur Psychologie, 226(1), 45-55. doi.org/10.1027/2151-2604/a000321
- Sher, S., & McKenzie, C. R. M. (2006). Information leakage from logically equivalent frames. Cognition, 101(3), 467-494. doi.org/10.1016/j.cognition.2005.11.001
- Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B., Jr., Bahnik, S., Bernstein, M. J., ... Nosek, B. A. (2014). Investigating variation in replicability: A "Many Labs" replication project. Social Psychology, 45(3), 142-152. doi.org/10.1027/1864-9335/a000178