Why Public Health & Civics Lotteries Are So Highly Effective: Gamification
This year I got a pretty amazing birthday present: a public example of highly effective health gamification.
Namely, Ohio’s Vax a Million campaign, which is giving away $1M per week to residents that get vaccinated. Vaccinations jumped 28% total, with weekly vaccinations increasing by over 50% week over week, according to the state. Maryland and New York have followed suit, and several other states (and perhaps even the Federal government) are poised to follow suit.
Large scale social good gamification is not new, per se. And we’ve been talking about the importance of lotteries to incentivize good behavior for years, including in the fields of prize-linked savings and rescuing journalism. But with the COVID-19 pandemic looming large, and a sinking vaccination rate in the US, the idea has received some major new attention. So why do behavioral lotteries work so well, and how can we expand their use?
Behavioral Lotteries take advantage of several cognitive biases and psychological processes that are relevant for public health and social good:
Optimism Bias holds that people will underestimate their odds of avoiding negative outcomes, and overestimate their odds of encountering positive outcomes. This is important because the risk of the underlying problem (getting sick from COVID) is being underestimated in the target population. Tying that to an unreasonable belief in your odds of winning a lottery replaces the negative optimism with positive optimism towards the potential of winning.
This is particularly important in public health settings where optimism bias drives a large part of the “bad” behavior. For example, smokers famously underestimate their chances of getting lung cancer when asked. Distracted drivers also underestimate their chances of getting into an accident and executives lowball their risks of stress-related disorders.
Sometimes called the Monte Carlo fallacy, this bias causes people to believe that — after witnessing several draws in a row — the next instance of that draw will be the opposite of those seen previously. That is, if you see heads 4 times in a…