Present bias

What can be done

Actions rated on the strength-rating scale (A strong and replicated, B solid but limited, C weak or debated, D contested or failed), applied to the specific claim made.

• Commit future pay raises to savings, with automatic increases (B). In Save More Tomorrow, 78% of those offered joined, and average saving rates rose from 3.5% to 13.6% over 40 months (Thaler and Benartzi, 2004, Benartzi and Thaler, 2013). The designers have an interest in the program.
• Prompt specific "if-then" plans (B). A meta-analysis of 94 tests found a medium-to-large effect (d = .65) (Gollwitzer and Sheeran, 2006), confirmed in an update of 642 tests by the same authors (Sheeran, Listrom, and Gollwitzer, 2025). In the field, asking employees to write down a date and time for a flu shot raised vaccination by 4.2 percentage points (Milkman et al., 2011).
• Timely reminders that make the next step concrete (A at the measured provider, B for overall uptake). Reminder texts raised flu vaccination by an average of 5% across 19 messages (Milkman et al., 2021b) and by 2.0 percentage points (6.8%) among 689,693 pharmacy patients (Milkman et al., 2022). COVID-19 vaccine texts to 142,428 Rhode Island residents had no detectable effect, so timing and context matter (Rabb et al., 2022).
• Not shown to help, especially for low-income people: voluntary commitment savings accounts (C). In the Philippines, 28% of people offered a locked savings account took it up, and their balances rose (Ashraf, Karlan, and Yin, 2006), but in another trial 55% of low-income users defaulted on their own commitments and lost money (John, 2020). Poverty raises the cost of small decision errors (Bertrand, Mullainathan, and Shafir, 2004), so automatic policies are more equitable than products people must sign up for.
• Not lasting: short programs to build exercise habits (C for lasting change). Of 54 interventions tested on 61,293 gym members, 45% raised visits during the program, but only 8% had a measurable effect after it ended (Milkman et al., 2021a).

Proposed and experimental methods

Methods that are proposed, under trial, approved in some places, or tried and then failed. Each shows a stage label and an evidence rating. A stage label shows how far a method has progressed, not whether it works. The stage labels are explained on the psychological factor page.

• Commitment contracts for health goals (Large trial, B). People put their own money at stake on a future goal and forfeit it if they fail. This builds on the voluntary commitment savings item above. In the Philippines, 11% of smokers offered a deposit account that was forfeited on a failed urine test took it up, and those offered were 3 percentage points more likely to pass the 6-month test, an effect that persisted in surprise tests at 12 months (Giné et al., 2010). In a U.S. trial with 2,538 participants, only 13.7% accepted deposit programs versus 90.0% for reward programs, so rewards produced more 6-month abstinence overall (15.7% versus 10.2%), but among people who would accept either, deposits raised abstinence by 13.2 percentage points more (Halpern et al., 2015). Data from 397,456 commitments on the stickK platform show referees and money stakes were used less often but strongly predicted success (observational) (Jangra et al., 2024).
• Prize-linked savings (Approved but not scaled, contested, C). Savings accounts pay returns partly or wholly as lottery-style prizes while protecting the money saved. Using South African bank data, people who used such an account raised their total savings by 1% of annual income on average, uptake was highest among financially constrained people and those without other deposit accounts, and random prize draws showed the accounts replaced lottery gambling (Cole et al., 2022). An online experiment found they increased savings and reduced lottery spending, most for people with the lowest savings and income (Atalay et al., 2014). South Africa's Supreme Court of Appeal ruled on March 28, 2008 that the product was an unlawful lottery (Supreme Court of Appeal of South Africa, 2008), while the U.S. credit union program Save to Win, started in Michigan in 2009, reports 70,000 account holders and over $165 million saved in 12 states (self-reported, no comparison group) (Commonwealth, n.d.).
• Just-in-time adaptive prompts (Early trial, C). Mobile systems sense a person's state and context and send tailored support at the moment it is most likely to help (Nahum-Shani et al., 2018). This builds on the reminders item above. In a 6-week trial that randomized prompts many times a day for 44 adults, a walking suggestion raised steps in the next 30 minutes by 24% on average, but the effect started at 107% and faded over time (Klasnja et al., 2019). A meta-analysis found a medium effect (g = 0.77) but pooled only 21 effect sizes from 592 participants, all from before and after designs (Xu and Smit, 2023).
• Wales's Well-being of Future Generations Act (Approved but not scaled, C). Public bodies in Wales must pursue sustainable development toward seven well-being goals, monitored by a Future Generations Commissioner and the Auditor General (Future Generations Commissioner for Wales, n.d.). Ten years on, the Auditor General found that "the Act is not driving the system-wide change that was intended", with variation in practice and incomplete use in financial planning (Audit Wales, 2025).
• Future Design (Early trial, C). Some citizens role-play residents of a future year and negotiate with groups speaking for the present. In Yahaba Town, Japan, about 20 citizens worked toward a vision for 2060, and in the final consensus 7 of 10 and 6 of 10 chosen measures came from the "future" groups (Hara et al., 2019). It is a single small practice without a comparison group.
• Generational accounting (Theoretical, C). Budget reports estimate the net tax burden current policy places on each generation, including those not yet born, instead of only the annual deficit (Auerbach et al., 1994). A critique in the same journal issue found the accounts rest on many debatable assumptions, such as the discount rate and population projections, and that equally reasonable assumptions give very different results (Haveman, 1994). No study was found showing that publishing such accounts changes budget decisions.
• Reversed: Israel's Knesset Commission for Future Generations (Failed or reversed, B). The commission ran from 2001 to 2006 and could examine bills that risked harming future generations, with its main power being the right to enough time to prepare an opinion on a bill. It ended after one term amid criticism of its cost and members' view that it interfered too much in their work (FDSD, 2017).

Sources cited on this page

1. Laibson, D. (1997). Golden eggs and hyperbolic discounting. Quarterly Journal of Economics, 112(2), 443-477. DOI B Moderate
2. DellaVigna, S., & Malmendier, U. (2006). Paying not to go to the gym. American Economic Review, 96(3), 694-719. DOI B Moderate: for the behavior, C for present bias as the cause
3. Acland, D., & Levy, M. R. (2015). Naiveté, projection bias, and habit formation in gym attendance. Management Science, 61(1), 146-160. DOI B Moderate: for overprediction
4. Ashraf, N., Karlan, D., & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. The Quarterly Journal of Economics, 121(2), 635-672. link B Moderate
5. Benartzi, S., & Thaler, R. H. (2013). Behavioral economics and the retirement savings crisis. Science, 339(6124), 1152-1153. link B Moderate
6. Bertrand, M., Mullainathan, S., & Shafir, E. (2004). A behavioral-economics view of poverty. American Economic Review, 94(2), 419-423. link B Moderate
7. DellaVigna, S., & Linos, E. (2022). RCTs to scale: Comprehensive evidence from two nudge units. Econometrica, 90(1), 81-116. link A Strong: for small average at-scale effects, B for attributing most of the gap to publication bias
8. Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69-119. link B Moderate
9. John, A. (2020). When commitment fails: Evidence from a field experiment. Management Science, 66(2), 503-529. link B Moderate
10. Li, Y., & Bates, T. C. (2019). You can't change your basic ability, but you work at things, and that's how we get hard things done: Testing the role of growth mindset on response to setbacks, educational attainment, and cognitive ability. Journal of Experimental Psychology: General, 148(9), 1640-1655. link B Moderate
11. Milkman, K. L., Beshears, J., Choi, J. J., Laibson, D., & Madrian, B. C. (2011). Using implementation intentions prompts to enhance influenza vaccination rates. Proceedings of the National Academy of Sciences, 108(26), 10415-10420. link B Moderate
12. Milkman, K. L., Gromet, D., Ho, H., Kay, J. S., Lee, T. W., Pandiloski, P., et al. (2021a). Megastudies improve the impact of applied behavioural science. Nature, 600(7889), 478-483. link B Moderate
13. Milkman, K. L., Gandhi, L., Patel, M. S., Graci, H. N., Gromet, D. M., Ho, H., et al. (2022). A 680,000-person megastudy of nudges to encourage vaccination in pharmacies. Proceedings of the National Academy of Sciences, 119(6), e2115126119. link A Strong: for raising vaccination at the measured provider, B for net population uptake
14. Rabb, N., Swindal, M., Glick, D., Bowers, J., Tomasulo, A., Oyelami, Z., et al. (2022). Evidence from a statewide vaccination RCT shows the limits of nudges. Nature, 604(7904), E1-E7. link B Moderate
15. Sheeran, P., Listrom, O., & Gollwitzer, P. M. (2025). The when and how of planning: Meta-analysis of the scope and components of implementation intentions in 642 tests. European Review of Social Psychology, 36(1), 162-194. link B Moderate
16. Thaler, R. H., & Benartzi, S. (2004). Save More Tomorrow™: Using behavioral economics to increase employee saving. Journal of Political Economy, 112(S1), S164-S187. link B Moderate
17. Atalay, K., Bakhtiar, F., Cheung, S., & Slonim, R. (2014). Savings and prize-linked savings accounts. Journal of Economic Behavior & Organization, 107(Part A), 86-106. link C Limited: (B for its own setting)
18. Auerbach, A. J., Gokhale, J., & Kotlikoff, L. J. (1994). Generational accounting: A meaningful way to evaluate fiscal policy. Journal of Economic Perspectives, 8(1), 73-94. link C Limited
19. Cole, S., Iverson, B., & Tufano, P. (2022). Can gambling increase savings? Empirical evidence on prize-linked savings accounts. Management Science, 68(5), 3282-3308. link B Moderate
20. Giné, X., Karlan, D., & Zinman, J. (2010). Put your money where your butt is: A commitment contract for smoking cessation. American Economic Journal: Applied Economics, 2(4), 213-235. link B Moderate
21. Halpern, S. D., French, B., Small, D. S., Saulsgiver, K., Harhay, M. O., Audrain-McGovern, J., et al. (2015). Randomized trial of four financial-incentive programs for smoking cessation. The New England Journal of Medicine, 372(22), 2108-2117. link B Moderate
22. Hara, K., Yoshioka, R., Kuroda, M., Kurimoto, S., & Saijo, T. (2019). Reconciling intergenerational conflicts with imaginary future generations: Evidence from a participatory deliberation practice in a municipality in Japan. Sustainability Science, 14(6), 1605-1619. link C Limited
23. Haveman, R. (1994). Should generational accounts replace public budgets and deficits? Journal of Economic Perspectives, 8(1), 95-111. link B Moderate
24. Jangra, H., Shah, R. R., & Kumaraguru, P. (2024). Put your money where your mouth is: Dataset and analysis of real world habit building attempts. Proceedings of the International AAAI Conference on Web and Social Media, 18(1), 1967-1978. link C Limited
25. Klasnja, P., Smith, S., Seewald, N. J., Lee, A., Hall, K., Luers, B., et al. (2019). Efficacy of contextually tailored suggestions for physical activity: A micro-randomized optimization trial of HeartSteps. Annals of Behavioral Medicine, 53(6), 573-582. link C Limited
26. Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., et al. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52(6), 446-462. link B Moderate
27. Xu, Z., & Smit, E. (2023). Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis. Digital Health, 9, 20552076231183543. link C Limited
28. Ash, E., Galletta, S., & Giommoni, T. (2025). A machine learning approach to analyze and support anticorruption policy. American Economic Journal: Economic Policy, 17(2), 162-193. link B Moderate

Every source for this factor is listed on the psychological factor page.