1 00:00:06,130 --> 00:00:09,436 Hi my name is Leslie John, and I am a professor of marketing at Harvard 2 00:00:09,436 --> 00:00:13,376 Business School. In my job I study why people make 3 00:00:13,376 --> 00:00:17,654 irrational decisions. And I try to use these moments of 4 00:00:17,654 --> 00:00:21,610 irrationality to help people help themselves. 5 00:00:21,610 --> 00:00:25,630 In other words, I try to take all the silly things that we do and I try to use 6 00:00:25,630 --> 00:00:30,914 them to people's own benefit. So have you ever wondered why we often 7 00:00:30,914 --> 00:00:34,970 fail to do things that we know are good for ourselves? 8 00:00:34,970 --> 00:00:40,342 For example, we buy gym club memberships only to see the membership cards collect 9 00:00:40,342 --> 00:00:44,155 dust. We regularly pledge to exercise more, but 10 00:00:44,155 --> 00:00:49,144 then day after day we come home to veg out in front of the computer. 11 00:00:49,144 --> 00:00:54,160 People try to take their prescription medications on time, but they regularly 12 00:00:54,160 --> 00:00:58,904 fail to do so. Or put differently, have you ever 13 00:00:58,904 --> 00:01:05,62 wondered why people knowingly engage in harmful behavior like risky sex? 14 00:01:05,62 --> 00:01:11,490 We'll, a lot of these behaviors can be explained by lapses in self-control. 15 00:01:11,490 --> 00:01:14,870 And today, I'm going to talk to you about a way that we can try to overcome some of 16 00:01:14,870 --> 00:01:20,112 these problems. So it should come really as no surprise 17 00:01:20,112 --> 00:01:25,263 that if you pay people to do things, they'll often do things for you. 18 00:01:25,263 --> 00:01:29,103 But it turns out that this is not the most efficient way to get people to do 19 00:01:29,103 --> 00:01:32,950 things. My colleagues and I, Kevin Volpe and 20 00:01:32,950 --> 00:01:37,834 George Lowenstein, we've been working on ways to make these expenses work even 21 00:01:37,834 --> 00:01:44,130 better as in change more behavior. So lots of employers, they will pay for 22 00:01:44,130 --> 00:01:47,586 their employees to use the gym, hoping that their employees will then use the 23 00:01:47,586 --> 00:01:52,896 gym and be healthier. But we've been finding that these simple 24 00:01:52,896 --> 00:01:58,356 direct subsidization, they're not really that efficient in getting people to go to 25 00:01:58,356 --> 00:02:02,109 gym. It turns out that there's a way that 26 00:02:02,109 --> 00:02:07,334 gives you much more bang for your buck. So what we do is we, the trick is instead 27 00:02:07,334 --> 00:02:13,610 of just paying people to do things, we capitalize on people's irrationality. 28 00:02:13,610 --> 00:02:17,563 So, in other words, we try to take these biases that people have in their 29 00:02:17,563 --> 00:02:21,610 decisions and use them to people's advantage. 30 00:02:21,610 --> 00:02:26,362 So today, I'll tell you about some work we've done where we've used two biases to 31 00:02:26,362 --> 00:02:30,754 try to help people to lose weight, that is one, the over weighting of small 32 00:02:30,754 --> 00:02:37,290 probabilities and two, loss aversion. Let me explain, so over weighting of 33 00:02:37,290 --> 00:02:41,382 small probabilities. Have you ever wondered why people play a 34 00:02:41,382 --> 00:02:47,460 lottery even though there's almost zero chance that they'll win? 35 00:02:47,460 --> 00:02:52,603 Or why people bet on underdogs? Well, it turns out that we human beings, 36 00:02:52,603 --> 00:02:56,153 we're actually really bad at understanding and estimating 37 00:02:56,153 --> 00:03:01,450 probabilities. So, we tend to overestimate, extremely 38 00:03:01,450 --> 00:03:05,548 unlikely events. So, extremely unlikely events feel to us 39 00:03:05,548 --> 00:03:10,600 like they're much more likely to happen than they actually are. 40 00:03:10,600 --> 00:03:15,712 So in other words, these low probability events exert a disproportionate influence 41 00:03:15,712 --> 00:03:21,500 on our judgments and decisions. And this can explain a lot of crazy 42 00:03:21,500 --> 00:03:26,808 beliefs that people have. For example, an ungodly number of 43 00:03:26,808 --> 00:03:31,596 Americans think that they will win the lottery and in fact some people truly 44 00:03:31,596 --> 00:03:37,772 believe that Winning the lottery. For them, winning the lottery is their 45 00:03:37,772 --> 00:03:41,150 retirement plan. So how can we use this normally 46 00:03:41,150 --> 00:03:46,620 pernicious bias to help people? Well, what let me explain how we did this 47 00:03:46,620 --> 00:03:50,713 with weight loss. So instead of just paying people to lose 48 00:03:50,713 --> 00:03:55,649 weight we took advantage of lotteries. So instead of paying someone a dollar to 49 00:03:55,649 --> 00:04:00,200 lose weight. We gave people, say, a 1% chance of 50 00:04:00,200 --> 00:04:04,380 winning $100. So importantly, the cost is the same, but 51 00:04:04,380 --> 00:04:09,700 the difference in the impact on human behavior is actually quite extraordinary. 52 00:04:09,700 --> 00:04:14,55 So what we did was we gave participants in our study a weight loss goal, so we 53 00:04:14,55 --> 00:04:19,830 told them every day what their weight loss goal was. 54 00:04:19,830 --> 00:04:24,252 And at the beginning of the study, each participant got to choose a lucky number, 55 00:04:24,252 --> 00:04:28,975 a two digit lucky number. And now, every day of the study, we put 56 00:04:28,975 --> 00:04:34,220 all the numbers into a hat and we drew out one lucky number every day. 57 00:04:34,220 --> 00:04:40,520 So if your lucky number was drawn, you could win $100, pretty great. 58 00:04:40,520 --> 00:04:45,545 But there was one catch and there was one aspect that made this lottery scheme way 59 00:04:45,545 --> 00:04:49,715 more potent. That is, you would only get the $100 if 60 00:04:49,715 --> 00:04:54,660 you had actually attained your weight loss goal that day. 61 00:04:54,660 --> 00:04:58,412 So, what we did was, suppose your number was drawn, but you hadn't lost the weight 62 00:04:58,412 --> 00:05:03,650 that you were supposed to lose that day. What we did, we sent you a really mean 63 00:05:03,650 --> 00:05:07,594 text message that said well congratulations, you're number came up, 64 00:05:07,594 --> 00:05:13,910 but unfortunately, you didn't meet your weight loss goal today. 65 00:05:13,910 --> 00:05:17,760 You would have won a $100 if you had only lost the weight. 66 00:05:17,760 --> 00:05:21,864 so this incentive scheme, not only does it leverage the power of over weighting 67 00:05:21,864 --> 00:05:25,284 of small probabilities, it feels like you're more likely to win than you 68 00:05:25,284 --> 00:05:30,490 actually are. But it also harnesses the strong 69 00:05:30,490 --> 00:05:34,910 motivations that people have to avoid regret, avoid that sense of, oh, what if 70 00:05:34,910 --> 00:05:39,650 my number came up and I hadn't lost the weight. 71 00:05:39,650 --> 00:05:44,72 so the second bias that I'll to you about before I tell you about the results of 72 00:05:44,72 --> 00:05:48,818 this study. We also in this study made use of loss 73 00:05:48,818 --> 00:05:54,220 aversion, which is a classic bias, loss aversion. 74 00:05:54,220 --> 00:06:00,0 So loss aversion, refers to how the pain of losing something is much more poignant 75 00:06:00,0 --> 00:06:05,525 to us, then is the pleasure that we get with an objectively equivalently sized 76 00:06:05,525 --> 00:06:11,333 gain. So in other words, people hate losing 77 00:06:11,333 --> 00:06:15,190 money way more than they enjoy getting money. 78 00:06:15,190 --> 00:06:20,440 And just like the over weighting of small probabilities, this bias can explain a 79 00:06:20,440 --> 00:06:25,315 lot of suboptimal behavior, for example, it explains why investors hold onto 80 00:06:25,315 --> 00:06:31,720 losing stocks for far too long, when really they should. 81 00:06:31,720 --> 00:06:34,658 They should see their losers. They should get rid of their losing 82 00:06:34,658 --> 00:06:37,153 stocks. And again just like with the over 83 00:06:37,153 --> 00:06:41,47 weighting small probabilities we use this bias in this case to help people lose 84 00:06:41,47 --> 00:06:44,500 weight. How do we do this? 85 00:06:44,500 --> 00:06:48,973 So what we did was we gave dieters the opportunity to put their own money down 86 00:06:48,973 --> 00:06:53,542 towards losing weight. So they could wager their own money 87 00:06:53,542 --> 00:06:57,141 towards losing weight. So they put their money down, and if they 88 00:06:57,141 --> 00:07:01,17 didn't lose their weight, if they didn't lose, if they didn't attain their weight 89 00:07:01,17 --> 00:07:05,256 loss goal, then they forfeited or lost their money. 90 00:07:05,256 --> 00:07:09,569 If they did attain their goal then they would get their money back, plus a match. 91 00:07:09,569 --> 00:07:12,260 So in other words, they were make, they were making a bet. 92 00:07:12,260 --> 00:07:15,476 They were begging on themselves that they would lose weight. 93 00:07:15,476 --> 00:07:20,588 Now, how do these two different incentive systems that are designed to capitalize 94 00:07:20,588 --> 00:07:25,274 on irrationality, how do these incentive systems fare when we compare them to a 95 00:07:25,274 --> 00:07:31,472 control incentive condition? Well, the results after four months, 96 00:07:31,472 --> 00:07:36,229 participants that were randomly assigned to use these incentive schemes, they lost 97 00:07:36,229 --> 00:07:42,31 way more weight than the people in the control condition, good news. 98 00:07:42,31 --> 00:07:46,855 however, the tricky thing, even trickier than losing weight in the first place, is 99 00:07:46,855 --> 00:07:51,176 keeping weight off once we remove the incentives. 100 00:07:51,176 --> 00:07:54,419 And I think I'll save that for another lecture or two altogether because that's 101 00:07:54,419 --> 00:07:57,906 another big problem that we're currently trying to solve. 102 00:07:57,906 --> 00:08:03,106 so summing up, today I've talked about how we can leverage or capitalize on 103 00:08:03,106 --> 00:08:08,306 people's irrationality to help people make better decisions and live better 104 00:08:08,306 --> 00:08:13,93 lives. Thanks.