Lotteries are also known as “stupidity tax”; a nod to their improbable odds. In India, lotteries are often run by state governments – its an easy way to cover for their budget deficits. What these governments don’t realize is that they are fueling an addiction.
But what are the reasons behind this addiction? In the article, I talk about the behavioral science of lotteries.
Lotteries generate many ‘near misses’ thus making people believe that she is a winner even when she has lost, thus inducing a a dopamine fueled craving. I also talk about the incorrect application of ‘regression to the mean’ mental model, and how governments make it easy for someone to rationalize their lottery addiction.
Read more on livemint site here.
Consider this quirky data point from Uber: only 1% of trips in San Francisco gets a one-star or one-star rating according to their 2014 blog article. Even after accounting for the systemic screening of drivers and the fact that Uber drivers may not be a random sample of drivers, the 1% figure ‘feels’ too low for a population of at least 20,000. What could explain such a skew?
A few days ago, the driver (or driver-partner as Uber would like to call him) of an Uber cab I hailed belched through out the ride – disgusting, right! I had made up my mind to give him a one-star. At the end of the trip though, he did a curious thing – he rated me five-star and showed it to me, saying, “Sir, I gave you a 5”. This blog post is about the consequences of that act and how it goes a long way in explaining the skew.
Uber India drivers are master behavioral scientists, as are gold merchants and sari retailers (but that’s a subject for a different blog). The cleverness lies in their use of reciprocity to nudge an upward revision of the rating. Drivers, when ending a trip, are shown the rating screen – this happens usually when the rider is still in the car. To be a truly fair system, one would expect the driver to conceal their rating of the rider and vice-versa. Surprisingly, Indian Uber drivers rate the riders five-star and cheerfully show it to the rider. This simple act kicks in reciprocity in the rider. Suddenly, the rider is under pressure to revise the rating he had ‘decided’; northwards. From ‘this belching buffoon needs a rap on the knuckle and a one-star’ to ‘maybe I should give him a three-star, after all he gave me a 5’. Robert Cialdini, in the book ‘Influence’, says “we are obligated to the future repayment of favors, gifts, invitations, and the like” – he would be proud!
How would we design a system that doesn’t lend itself to being gamed? For a start, Uber could add a forced delay between the end of a trip and the feedback step. A one minute interval could be enough to retain the memory of the last trip (to ensure accurate feedback), while forcing distance between the rider the driver. It’s not a fool-proof system – drivers could still orally indicate the fact that they would be giving a five-star rating. But I wager that the ratings would be far more evenly spread than the 1% one-star skew Uber has presently – one that is representative of the ride!
Image Credit: http://findingthefreedom.com/using-uber-abroad/
The US presidential race is probably the most fascinating election from a behavioral science point of view. From as far back as 1920s, researchers have been studying how to get people to vote and how to get people to vote for a particular candidate. There are many accounts of how data science and behavioral science propelled Obama’s 2012 campaign. But the use of behavioral science is a two-edged sword, as Ted Cruz’ campaign just found out. Ted Cruz’ campaign was recently caught in an embarrassing position of having to defend the ‘shaming’ letters sent to potential voters in Iowa.
The letter sent to people who had not voted in recent elections showed people their ‘score’ and their neighbors’ scores based on past voting record. For added social pressure, the letter mentions that neighbors may see your score and that a follow-up letter may be issued after the election.
The letter caused an outrage on twitter with some even going on to ‘punish’ Cruz by professing support for Trump.
The interesting part was that Cruz’s campaign modeled their letters on ones drafted in a 2008 study that studied how social pressure affected voter turn out. Cruz’ letter was not far off from the most successful letter (an 8.1% lift over the baseline of 29.7% voting rate) in the study that also used voters’ and neighbors’ voting history combined with possibility of a post-election follow-up letter.
So how do we get from the nice 8.1% lift to this backlash? The answer lies in ‘context’ – insights from studies have to be contextualized for the situation or risk such failure. Though Larimer also got complaints from voters because the study, the reaction to the study may well have been muted because an independent agent with no vested interest in the election outcome was running it. Larimer, in an email to Washington Post blames the negative tone of the letter for triggering a ‘boomerang effect’. What he overlooks is that as long as the letter states “Paid for by Cruz for President”, the causal attribution for the situation would fall on Cruz (and not ‘self’, which is required for shame) and the emotion elicited may be anger rather than shame.
Image credit: Braddock Massey on Twitter