In a field experiment among US university students, incentives for sleep increase both sleep and academic performance. Our primary intervention pairs bedtime reminders with morning feedback and immediate rewards for sleeping at least 7 hours on weeknights. The intervention raises the share of weeknights with 7+ hours of sleep by 26% and average sleep by 19 minutes during a 4-week treatment period, with posttreatment effects of 8 minutes. Immediate incentives outperform both delayed incentives and reminders/feedback alone during treatment but have no distinguishable impacts posttreatment. The primary intervention improves average semester course performance by 0.075–0.089 grade points, a 0.10–0.11 standard deviation increase. Our results demonstrate that incentives targeting sleep can be a cost-effective approach to improve educational outcomes.
The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework
We review 74 experiments with no, low, or high performance-based financial incentives. The modal result has no effect on mean performance (though variance is usually reduced by higher payment). Higher incentive does improve performance often, typically judgment tasks that are responsive to better effort. Incentives also reduce “presentation” effects (e.g., generosity and risk-seeking). Incentive effects are comparable to effects of other variables, particularly “cognitive capital” and task “production” demands, and interact with those variables, so a narrow-minded focus on incentives alone is misguided. We also note that no replicated study has made rationality violations disappear purely by raising incentives.
Getting to the Top of Mind: How Reminders Increase Saving
We provide evidence from field experiments with three different banks that reminder messages increase commitment attainment for clients who recently opened commitment savings accounts. Messages that mention both savings goals and financial incentives are particularly effective, whereas other content variations such as gain versus loss framing do not have significantly different effects. Nor do we find evidence that receiving additional late reminders has an additive effect. These empirical results do not map neatly into existing models, so we provide a simple model where limited attention to exceptional expenses can generate undersaving that is in turn mitigated by reminders. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2296 . This paper was accepted by Teck-Hua Ho, behavioral economics.
Overcoming Salience Bias: How Real-Time Feedback Fosters Resource Conservation
Inattention and imperfect information bias behavior toward the salient and immediately visible. This distortion creates costs for individuals, the organizations in which they work, and society at large. We show that an effective way to overcome this bias is by making the implications of one’s behavior salient in real time, while individuals can directly adapt. In a large-scale field experiment, we gave participants real-time feedback on the resource consumption of a daily, energy-intensive activity (showering). We find that real-time feedback reduced resource consumption for the target behavior by 22%. At the household level, this led to much larger conservation gains in absolute terms than conventional policy interventions that provide aggregate feedback on resource use. High baseline users displayed a larger conservation effect, in line with the notion that real-time feedback helps eliminate “slack” in resource use. The approach is cost effective, is technically applicable to the vast majority of households, and generated savings of 1.2 kWh per day and household, which exceeds the average energy use for lighting. The intervention also shows how digitalization in our everyday lives makes information available that can help individuals overcome salience bias and act more in line with their preferences. This paper was accepted by Uri Gneezy, behavioral economics.
The Mythical Agent-Month – Wes McKinney
Like a lot of people, I’ve found that AI is terrible for my sleep schedule. In the past I’d wake up briefly at 4 or 4:30 in the morning to have a sip of water or use the bathroom; now I have trouble going back to sleep. I could be doing things. Before I would get a solid 7-8 hours a night; now I’m lucky when I get 6. I’ve largely stopped fighting it: now when I’m rolling around restlessly in bed at 5:07am with ideas to feed my AI coding agents, I just get up and start my day.
How Cycling Solved Sleep | Defector
AURILLAC, France — Is the Tour de France the most competitive sleep environment in sports? “Probably, yeah,” answered Dr. Jon Greenwell, EF-Education EasyPost’s Head Doctor when I asked him. Every athlete needs to recover, though the demands on professional cyclists at the biggest race in the sport are unique. Riders have to be fresh every…
I couldn’t sleep, then I tried cognitive shuffling | Psyche Notes to Self
The usual advice didn’t help my sleep problems, but then I tried an exercise that kickstarts the sleep-onset control system
Spaced mathematics practice improves test scores and reduces overconfidence
Abstract The practice assignments in a mathematics textbook or course can be arranged so that most of the problems relating to any particular concept are massed together in a single assignment, or these related problems can be distributed across many assignments–a format known as spaced practice. Here we report the results of two classroom experiments that assessed the effects of mathematics spacing on both test scores and students' predictions of their test scores. In each experiment, students in Year 7 (11–12 years of age) either massed their practice into a single session or divided their practice across three sessions spaced 1 week apart, followed 1 month later by a test. In both experiments, spaced practice produced higher test scores than did massed practice, and test score predictions were relatively accurate after spaced practice yet grossly overconfident after massed practice.
What Did Ancient Humans Do at Night?
Misunderstanding Savings Growth: Implications for Retirement Savings Behavior
People systematically underestimate exponential growth. This article illustrates this phenomenon, its implications, and some potential interventions in the context of saving for retirement, where savings grow exponentially over long periods of time. Experiment 1 shows that a majority of participants expect savings over 40 years to grow linearly rather than exponentially, leading them to grossly underestimate their account balance at retirement. Experiment 2 demonstrates that this misunderstanding leads to underestimates of the cost of waiting to save, which makes putting off saving more attractive than it should be. Finally, Experiments 3–5 show that highlighting the exponential growth of savings motivates both college students and employees to save more for retirement. Making clear to employees the exponential growth of savings before they make crucial decisions about how much to save may be a simple and effective means of increasing retirement savings.
Can Self-Control Explain Avoiding Free Money? Evidence from Interest-Free Student Loans
Abstract This paper uses insights from behavioral economics to explain a particularly surprising borrowing phenomenon: one in six undergraduate students offered interest-free loans turns them down. Models of impulse control predict that students may optimally reject subsidized loans to avoid excessive consumption during school. Using the National Postsecondary Student Aid Study, we investigate students' take-up decisions and identify a group of students for whom the loans create an especially tempting liquidity increase. Students who would receive the loan in cash are significantly more likely to turn it down, suggesting that consumers choose to limit their liquidity in economically meaningful situations.
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53,000 college students, where the goal was to use "nudges" to encourage students to renew their financial-aid applications before a non-binding deadline. We begin with baseline approaches to targeting. First, we target based on a causal forest that estimates heterogeneous treatment effects and then assigns students to treatment according to those estimated to have the highest treatment effects. Next, we evaluate two alternative targeting policies, one targeting students with low predicted probability of renewing financial aid in the absence of the treatment, the other targeting those with high probability. The predicted baseline outcome is not the ideal criterion for targeting, nor is it a priori clear whether to prioritize low, high, or intermediate predicted probability. Nonetheless, targeting on low baseline outcomes is common in practice, for example because the relationship between individual characteristics and treatment effects is often difficult or impossible to estimate with historical data. We propose hybrid approaches that incorporate the strengths of both predictive approaches (accurate estimation) and causal approaches (correct criterion); we show that targeting intermediate baseline outcomes is most effective in our specific application, while targeting based on low baseline outcomes is detrimental. In one year of the experiment, nudging all students improved early filing by an average of 6.4 percentage points over a baseline average of 37% filing, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.
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Nicolas Mattia – Keep your Mac awake with caffeinate
This explains how to keep a Mac from going to sleep using a simple command
Dylan Wiliam on Twitter / X
The best journal article titles tell you what the study actually found, rather than being a teaser to make you read the paper. Here's a good example: "Spaced mathematics practice improves test scores and reduces overconfidence" https://t.co/iXxdkAgYPM ($)— Dylan Wiliam (@dylanwiliam) December 31, 2024