Why does timing shape how much value we assign to rewards?

The 

Present Bias

, explained.
Bias

What is the Present Bias?

Present bias is the tendency to give disproportionate weight to rewards that arrive sooner, even when a larger payoff is available later. This can lead us to prioritize immediate rewards over future payoffs, even if that decision benefits us less overall.

Where this Bias Occurs

Ever promised yourself that you’d go to the gym right after work, only to end up on the couch watching the newest episode of your favorite guilty pleasure show instead? You arrive home after a day full of back-to-back meetings, shoes kicked off, phone in hand, and the episode’s theme song starts playing before you’ve even sat down. That workout was penciled in earlier, when energy was higher and exercising felt realistic, maybe even exciting. Now, though, lifting weights or running on the treadmill for half an hour sounds wildly optimistic, while staying on the couch feels well-earned after a long day at work.

Present bias helps explain why this happens. The episode delivers comfort immediately, and it’s right there in front of you, while the benefits of exercising arrive later. Improved sleep, better mood, and long-term health may be developing with consistent exercise over time, yet they don’t register at the moment the workout begins. When choices are evaluated this way, rewards that are closer to the present may receive more weight than those that are delayed, even when that trade-off isn’t optimal.

This bias belongs to a broader group of time-related decision biases that affect how future outcomes are valued. It is closely related to hyperbolic discounting, a mathematical model of intertemporal choice where subjective value drops sharply over short delays, such as from now to one week, and then declines more gradually across longer delays, such as from six months to seven months. Although the terms are often used interchangeably, hyperbolic discounting represents one of several non-linear discounting models that captures present-biased preferences. In practice, present bias makes it harder to follow through on goals whose benefits take time to materialize, especially after a long day when attention may narrow toward what feels good in the moment and is easy to justify.

Sources

  1. Dorward, P., Osbahr, H., Sutcliffe, C., & Mbeche, R. (2020). Supporting climate change adaptation using historical climate analysis. Climate and Development, 12(5), 469–480. https://doi.org/10.1080/17565529.2019.1642177 
  2. U.S. Chamber of Commerce, Allstate, & U.S. Chamber of Commerce Foundation. (2024). The preparedness payoff: The economic benefits of investing in climate resilience (2024 Climate Resiliency Report). https://www.uschamber.com/assets/documents/USCC_2024_Allstate_Climate_Resiliency_Report.pdf
  3. Valentelyte, G., Sheridan, A., Kavanagh, P., Doyle, F., & Sørensen, J. (2025). Health and societal burden of tobacco smoking in Ireland: A life table modelling study. Public Health, 247, 105880. https://doi.org/10.1016/j.puhe.2025.105880 
  4. Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40(2), 351–401. https://doi.org/10.1257/002205102320161311 
  5. Green, L., Fristoe, N., & Myerson, J. (1994). Temporal discounting and preference reversals in choice between delayed outcomes. Psychonomic Bulletin & Review, 1(4), 383–389. https://doi.org/10.3758/BF03213979 
  6. Dasgupta, P., & Maskin, E. (2005). Uncertainty and hyperbolic discounting. American Economic Review, 95(4), 1290–1299. https://doi.org/10.1257/0002828054825637 
  7. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
  8. McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306(5695), 503–507. https://doi.org/10.1126/science.1100907 
  9. Loewenstein, G. (2000). Emotions in economic theory and economic behavior. American Economic Review, 90(2), 426–432. https://doi.org/10.1257/aer.90.2.426 
  10. Skrynka, J., & Vincent, B. T. (2019). Hunger increases delay discounting of food and non-food rewards. Psychonomic Bulletin & Review, 26(5), 1729–1737. https://doi.org/10.3758/s13423-019-01655-0 
  11. Kannisto, K. A., Koivunen, M. H., & Välimäki, M. A. (2014). Use of mobile phone text message reminders in health care services: A narrative literature review. Journal of Medical Internet Research, 16(10), e222. https://doi.org/10.2196/jmir.3442 
  12. Aguilera, A., Bruehlman-Senecal, E., Demasi, O., & Avila, P. (2017). Automated text messaging as an adjunct to cognitive behavioral therapy for depression: A clinical trial. Journal of Medical Internet Research, 19(5), e148. https://doi.org/10.2196/jmir.6914 
  13. Kraft, S., Wolf, M., Klein, T., Becker, T., Bauer, S., & Puschner, B. (2017). Text message feedback to support mindfulness practice in people with depressive symptoms: A pilot randomized controlled trial. JMIR mHealth and uHealth, 5(5), e7095. https://doi.org/10.2196/mhealth.7095 
  14. Van Blarigan, E. L., Chan, H., Van Loon, K., Kenfield, S. A., Chan, J. M., Mitchell, E., Zhang, L., Paciorek, A., Joseph, G., Laffan, A., Atreya, C. E., Fukuoka, Y., Miaskowski, C., Meyerhardt, J. A., & Venook, A. P. (2019). Self-monitoring and reminder text messages to increase physical activity in colorectal cancer survivors (Smart Pace): A pilot randomized controlled trial. BMC Cancer, 19(1), 218. https://doi.org/10.1186/s12885-019-5427-5 
  15. Head, K. J., Noar, S. M., Iannarino, N. T., & Harrington, N. G. (2013). Efficacy of text messaging-based interventions for health promotion: A meta-analysis. Social Science & Medicine, 97, 41–48. https://doi.org/10.1016/j.socscimed.2013.08.003 
  16. Samuelson, P. A. (1937). A note on measurement of utility. The Review of Economic Studies, 4(2), 155–161. https://doi.org/10.2307/2967612 
  17. Herrnstein, R. J. (1961). Relative and absolute strength of response as a function of frequency of reinforcement. Journal of the Experimental Analysis of Behavior, 4(3), 267–272. https://doi.org/10.1901/jeab.1961.4-267 
  18. Ainslie, G. (2012). Pure hyperbolic discount curves predict “eyes open” self-control. Theory and Decision. https://doi.org/10.1007/s11238-011-9272-5 
  19. Cervellati, E. M., Filotto, U., Sgrulletti, D., & Stella, G. P. (2026). Buy now, pay later consumer credit behavior: Impacts on financing decisions. Qualitative Research in Financial Markets, 18(1), 287–299. https://doi.org/10.1108/QRFM-07-2024-0185 
  20. Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006 
  21. Watson, C. E., & Rainie, L. (2026). The AI challenge: How college faculty assess the present and future of higher education in the age of AI. Association of American Colleges and Universities; Elon University Imagining the Digital Future Center. https://imaginingthedigitalfuture.org/wp-content/uploads/2026/01/Elon-AACU-faculty-AI-survey-full-report-1-21-26.pdf
  22. Gillespie, N., Lockey, S., Ward, T., Macdade, A., & Hassed, G. (2025). Trust, attitudes and use of artificial intelligence: A global study 2025. The University of Melbourne & KPMG. https://assets.kpmg.com/content/dam/kpmgsites/xx/pdf/2025/05/trust-attitudes-and-use-of-ai-global-report.pdf 
  23. Vitale, F., McGrenere, J., Tabard, A., Beaudouin-Lafon, M., & Mackay, W. E. (2017). High costs and small benefits: A field study of how users experience operating system upgrades. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 4242–4253). https://doi.org/10.1145/3025453.3025509 
  24. Dissanayake, N., Zahedi, M., Jayatilaka, A., & Babar, M. A. (2022). Why, how and where of delays in software security patch management: An empirical investigation in the healthcare sector. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1–29. https://doi.org/10.1145/3555087 
  25. Mohurle, S., & Patil, M. (2017). A brief study of WannaCry threat: Ransomware attack 2017. International Journal of Advanced Research in Computer Science, 8(5).
  26. Frik, A., Malkin, N., Harbach, M., Peer, E., & Egelman, S. (2019). A promise is a promise: The effect of commitment devices on computer security intentions. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–12). https://doi.org/10.1145/3290605.3300834
  27. Werthschulte, M., & Löschel, A. (2019). Cost misperceptions and energy consumption: Experimental evidence for present bias and biased price beliefs (CAWM Discussion Paper No. 111). https://hdl.handle.net/10419/200166 
  28. Gans, W., Alberini, A., & Longo, A. (2013). Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland. Energy Economics, 36, 729–743. https://doi.org/10.1016/j.eneco.2012.11.022

About the Author

Maryam Sorkhou

Maryam holds an Honours BSc in Psychology from the University of Toronto and is currently completing her PhD in Medical Science at the same institution. She studies how sex and gender interact with mental health and substance use, using neurobiological and behavioural approaches. Passionate about blending neuroscience, psychology, and public health, she works toward solutions that center marginalized populations and elevate voices that are often left out of mainstream science.

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