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Entrepreneurship: Building Rewarding Ventures

The Diversity Bonus

In our Leading Diverse Teams and Organizations MOOC, Professor Scott Page explains why research shows that diverse organizations achieve more than homogenous counterparts.


Excerpt From


0:06 hi 0:07 my name is scott paige and i'm a 0:08 professor at the raw school of business 0:10 at the university of michigan 0:11 what i want to talk to you today about 0:12 is how is it when is it 0:15 that diverse groups outperform 0:16 homogeneous groups 0:18 and the logic that i'm going to present 0:20 is something that i call diversity 0:21 bonuses 0:22 it explains why is it that diverse 0:24 groups gives you that extra 5 0:26 10 over homogeneous groups 0:29 and the idea is going to be this is the 0:31 diversity ends up being really 0:33 beneficial 0:33 when the task before you the problem at 0:35 hand is really complex 0:37 if it's easy you don't need a team and 0:39 you don't need a diverse team 0:41 it's when the problems are hard so what 0:43 do i mean by complex 0:44 by complex i mean something that's 0:46 difficult to explain or 0:48 engineer or evolve or predict but to 0:50 think of the global economy 0:52 that's complex if you think of middle 0:53 school that's complex you think of the 0:55 brain 0:56 that's complex if i think about 0:58 something like you know cutting a 2x4 to 1:00 a specific length 1:01 that's not complex and i can just have 1:03 one competent person do that job 1:05 so if i look at trying to predict or 1:07 forecast but in the economy 1:08 some things are going to be complex like 1:10 oil prices 1:12 and other things like oil production are 1:14 going to be less complex 1:15 and so where we need to put teams 1:16 together where we get these bonuses and 1:18 diversity 1:19 are on the more complex tasks now to 1:21 walk you through the logic 1:23 it's not going to be some sort of like 1:25 fits all and now we sprinkle on some 1:27 diversity 1:27 and we do a lot better we actually have 1:29 to have a mechanistic understanding of 1:31 what is the nature of the task 1:33 what is the type of diversity and how 1:35 does the type of diversity make us 1:36 better at that task 1:38 so let's start with prediction or 1:40 forecasting so now we've got it is we've 1:41 got to figure out should we hire this 1:43 person 1:43 what's going to happen in this market 1:45 what's possibly going to happen in this 1:46 region of the world 1:48 and what we want to do is we want to see 1:49 what are the advantages of putting a 1:50 diverse group of people 1:51 together in a room over just having a 1:53 whole bunch of like homogeneous people 1:55 or maybe just one really smart person 1:58 so let me start with some data google 2:00 which gets about four million job 2:02 applicants a year 2:03 has to figure out a way to sort of sort 2:04 through those so what they do at first 2:06 pass is use a computer algorithm so what 2:08 you see in this graph at the 50 percent 2:09 line 2:10 is sort of if you just took someone from 2:12 the algorithm half would be above 2:13 average half would be below average 2:15 if they just have one person interview 2:18 they get a bonus up to 76 so they're 26 2:21 percent better than the algorithm 2:23 now the point isn't that the person is 2:24 smarter than the algorithm the point is 2:26 that the person is different from the 2:27 algorithm 2:28 now they had a second interviewer it 2:30 goes from 76 to 81 2:32 a third from 81 to 84 a fourth from 84 2:35 to 86. 2:36 so what you get is by adding 2:38 interviewers adding this diversity 2:40 you end up having a 36 chance of hiring 2:43 someone better than average 2:44 whereas if you had one person you might 2:46 be 26 better than average 2:48 now the point here is when they're 2:49 adding more interviewers they're not 2:51 adding on people that are smarter 2:53 they're just adding on people that are 2:54 different each one of us when we 2:56 interview someone we see that person 2:57 through a different lens 2:59 and what you see is the crowd is more 3:01 accurate 3:02 than an individual now this phenomenon 3:04 of groups being better than individuals 3:06 is sometimes called the wisdom of crowds 3:08 that comes from a book by jim sirwiki 3:09 where he talks about this west of 3:11 england fat stock and poultry exhibition 3:13 held in the 19th century where 787 3:16 people guessed the weight of a steer 3:17 average guess was 197 pounds 3:20 actual weight of this steer was 198 3:22 pounds so this is a great anecdote 3:24 they're within a pound but we want to 3:26 move from anecdote to science 3:28 because i understand when do we need the 3:30 team when don't we 3:31 so what does the science say what the 3:33 science says is we can write down a 3:35 theorem a formal mathematical identity 3:37 that says the following 3:38 the crowd's error equals the average 3:40 error of the people in it 3:42 minus their diversity what does that 3:44 mean so the crowds there that trimming 3:46 standoff sees the crowd's prediction and 3:48 that greek 3:48 term theta that little you know circle 3:50 with a line there 3:51 that's the true value so the crowd's 3:54 error you might think 3:55 is the average of the individual errors 3:57 like your stocks in a portfolio but it 3:59 turns out it's not 4:00 the crowd is more accurate than the 4:02 average person and the amount 4:03 by which it's more accurate is the 4:05 diversity of their predictions because 4:07 if some predictions are high and some 4:08 predictions are low 4:09 you end up the crowd being more accurate 4:11 than the average person in it 4:13 now this isn't just some sort of like 4:14 feel-good idea this is a mathematical 4:17 fact 4:18 so if we look at the data from galton 4:19 sphere the crowd remember i said was up 4:22 by a pound well we 4:23 square the errors here because it's 4:24 statistics we do squared errors they're 4:26 actually up by 1.4 pounds 4:28 so when we square it we get two the 4:30 average air was over 70 pounds so when 4:32 we square that we get something a little 4:33 over 5 000. 4:35 the reason the crowd was correct is the 4:38 crowd was also diverse 4:39 so what you get is the crowd air is 4:41 really small because you're pretty smart 4:42 people 4:43 but those people happen to be diverse 4:45 this is the key 4:46 and this is also why complexity is so 4:48 important why do we need diversity on 4:50 complex problems 4:52 so if the pump is complex nobody's going 4:54 to be right right the average air is 4:55 going to be pretty big 4:57 we want the crowds here to be small so 4:59 the only way we can make the crowds air 5:01 small the average air is big 5:02 is by making the crowd diverse now this 5:06 happens and this plays out 5:08 everywhere right it's a mathematical 5:09 fact so let me show you some data from 5:10 economic forecasts 5:11 this is worked by professors at duke 5:13 university what they have is 28 000 5:16 forecasts by professional economist 5:18 six economic indicators the crowd is 21 5:22 better than the average economist now 5:23 it's not 28 000 economists on each 5:25 prediction so typically it's like 40 or 5:26 50 economists making predictions 5:28 the crowd was 21 better than the average 5:30 economist 5:31 now even more impressive than that if 5:33 you look at this red line 5:35 the red line starts at one that's the 5:36 year for an average economist 5:38 if you just add in a second economist 5:40 you do nine percent better 5:42 even more amazing than that take the 5:44 purple line that purple is the very best 5:47 economy so the best economist in this 5:48 data set 5:49 is 10 better than average economist when 5:52 you average her 5:53 and a lot of state it wasn't her was 5:54 abby cohen from goldman sachs 5:56 when you average her with the second 5:58 best economist 5:59 who by definition is not 10 better you 6:02 get 17 6:03 better that's what i mean by a bonus ten 6:06 percent plus nine percent gives you 6:07 seventeen percent 6:09 the reason why is because they're 6:10 diverse so this is the key logic 6:14 when you think about why you have 6:16 diversity for a portfolio 6:18 you do so because you want to minimize 6:19 risk right so if one stock gets eight 6:21 percent one gets 12 and one loses eight 6:23 right on average you get about five 6:25 percent but with predictors and this is 6:27 the data from the economist 6:28 the best is nine percent better the next 6:30 best nine percent better the next best 6:31 nine percent better the next best eight 6:32 percent better when you average them 6:34 22 percent better bonus so whatever 6:38 logic you have whatever reason you have 6:40 for diverse stocks 6:41 which is risk mitigation you know which 6:43 diversity you have in your stock 6:44 portfolio 6:45 you should have more diversity within 6:47 your employees because employees don't 6:48 just mitigate risk 6:50 they do that also but they give you a 6:52 bonus and that bonus in this case is 6:54 enormous right it's 6:55 the value add from the bonus is larger 6:57 than the value out from having the best 6:58 predictor in the market 7:00 all right that's prediction let's look 7:03 at problem solving the logic here's 7:05 entirely different but again we're going 7:06 to see a bonus 7:07 so let's think about how problem solving 7:08 works you've got some sort of difficult 7:10 problem you've got a bunch of you know 7:11 problem solvers that set loose on it 7:13 like they represent it in some way 7:14 they think about how they're going to 7:15 find better solutions i find an 7:17 improvement that lindy builds on my 7:18 improvement 7:19 what you can show here lu hong and i 7:21 showed this in a mathematical paper in 7:22 2001 7:24 that i think a whole bunch of really 7:25 competent problem solvers these are 7:26 represented as kind of mathematical 7:28 algorithms 7:29 i'm actually better off taking a random 7:31 group of competent ones 7:33 than taking a collection of the very 7:34 best and the reason why 7:36 is the collections very best aren't 7:37 diverse so when one of them gets stuck 7:39 at a point 7:39 since they're all representing the 7:41 problem in similar ways they all get 7:43 stuck at that same 7:44 point when i have a diverse group of 7:45 problem solvers they get stuck at 7:47 different points 7:48 now this was a somewhat controversial 7:49 paper so there's a bunch of follow-ons 7:52 and everybody who's done a follow-on 7:54 has found pretty much the exact same 7:55 thing and here's one by john kleinberg 7:57 and michael ragu from cornell 7:59 they said okay let's think of problem 8:00 solvers as people who sort of like come 8:02 in with a set of ideas 8:04 right and then what happens in the room 8:06 we combine those ideas an interesting 8:07 way we build up those ideas 8:09 what they show is that if you do 8:11 something interesting in the group as 8:12 long as it's like it's kind of like 8:13 adding up 8:14 and taking an average if you do 8:15 something interesting with these 8:16 solutions 8:17 then there's no test that you could 8:20 apply to individuals 8:21 such that the best team consists of 8:24 the people who scored highest on that 8:26 test so in other words if you're putting 8:27 together a team 8:29 you can't put together a team thinking 8:30 in terms of individual performance you 8:32 want to think of that team in terms of 8:33 diversity 8:35 now these are theoretical results what 8:36 about empirically well this was so 8:38 sort of surprising to people that people 8:40 then tested this in a lot of cases and 8:42 one of the most amazing tests is 8:44 using iq tests so this is raven's 8:45 progressive iq test right here which is 8:47 the next pattern in the sequence 8:50 so this is a wonderful work where people 8:51 wanted to see would an iq test 8:54 work to put together a team to take an 8:57 iq test 8:58 and the answer is no if you want to put 9:01 together a team to take an iq test 9:02 you actually don't want the people with 9:04 the highest iq now why is that 9:06 but what you want is you want people who 9:08 have high contextual iq 9:10 relative to the other people in the 9:11 group what is contextual like 9:13 contextual iq is this suppose that like 9:15 i don't score that well in an iq test 9:17 but i happen to get the questions right 9:20 that that really smart people 9:22 get wrong so i get questions right that 9:24 like other people are sort of confused 9:25 on 9:26 then you want me in the group now yeah i 9:28 miss a lot of easy questions but that 9:30 doesn't matter because the rest of the 9:31 groups get it right 9:32 so you want sort of people whose correct 9:35 answers are correlated differently 9:37 right who get different correct answers 9:39 than the p than most of the people 9:41 so if what you're trying to do is take 9:43 an iq test as a team you actually don't 9:45 want the people who score highest 9:46 individually 9:47 you want people who think differently 9:50 all right third type of task creative 9:52 tasks suppose you're trying to come with 9:54 just creative solutions just generate 9:56 ideas 9:57 what about diversity here well let me 9:59 give an example this is something that 10:00 sometimes knows the brick test or the 10:01 alternative uses test we have to think 10:03 of 10:03 how many ideas can i think of you know 10:06 using a brick for 10:08 so i did a version of this with a 10:09 corporate client where i said okay 10:10 suppose you've got 10 000 plastic straws 10:12 sitting in the basement 10:13 what can you do with it right how many 10:15 ideas can you think of 10:17 this is known as the alternative uses 10:18 test the individual ability of a person 10:21 is how many ideas they can think of on 10:22 their own 10:23 the ability of the group is how many is 10:24 the group can think of so i did this 10:26 with the corporate client here's the 10:28 results so one person person a the 10:30 highest performing person thought of 31 10:32 ideas the lowest performing person 10:34 person j thought of five ideas you could 10:36 say okay a is the most creative b is the 10:38 second most creative and so on 10:39 but when i combine these people i get 10:41 103 10:43 ideas because what i can do is i can 10:44 sort of take the union of all their 10:45 ideas if there's a duplicate copy i 10:47 throw it out 10:48 and what you see is the group is far far 10:50 more creative 10:51 than any one person in the room and in 10:53 this case if a person be in person c 10:56 even though they have the same 10:57 individual diversity person c 10:59 actually thought of a lot more unique 11:01 ideas so relative to the group for since 11:03 he had more value than person b 11:05 even though on their own they were 11:07 equally valuable 11:09 so what we see in all these cases is the 11:12 sum 11:12 is better than the parts and it happens 11:14 in the form of bonuses 11:16 but let's think about this carefully 11:17 what is the form of diversity in 11:19 prediction it's different 11:20 categorizations of world different 11:22 models 11:22 and problem solving it's different 11:24 representations of the problems 11:26 different tools different analogies 11:27 different ways of kind of climbing the 11:28 hill thinking of little improvements 11:30 and at creative tasks it's different 11:32 sets of possibilities it's just what can 11:34 you think of 11:35 and then what is the logic in prediction 11:38 the logic is by averaging these diverse 11:40 models 11:40 you kind of eradicate biases right some 11:43 of the estimates that are too high and 11:44 some that are too low those kind of 11:45 cancel out 11:46 in problem solving it's this notion of 11:48 iteratively improving 11:49 so i'm stuck somewhere somebody else 11:51 thinks of something new 11:53 stuart kaufman calls this the adjacent 11:55 possible 11:56 i'm stuck i can't think of anything 11:57 better lindy can think of something new 11:59 her adjacent possible is different than 12:01 mine 12:02 making the group smarter on a creative 12:04 task 12:05 it's about the union of ideas right so 12:07 it's my ideas 12:08 lindy's ideas but somebody else's ideas 12:10 and the union of those sets 12:12 is more than any one person knows in 12:14 each case 12:15 diversity bonus in each case a different 12:18 logic 12:20 okay what about identity diversity 12:21 because this is a course on 12:23 diversity equity and inclusion what 12:25 about identity 12:26 because i've been talking about 12:27 different ways that people think 12:30 so you can think about identity 12:31 diversity who we are 12:33 affects how we think and how we think 12:36 leads to better outcomes but we can 12:38 unpack that a little bit more because 12:39 what do we mean by identity diversity we 12:41 mean things like gender race age sexual 12:43 orientation ethnicity social class where 12:45 we grew up religion 12:47 those things map into the information we 12:50 have the knowledge the heuristics we 12:51 develop how we represent the world the 12:53 causal models in our head our analogies 12:55 the books we read 12:56 the places we visit what's inside our 12:59 head 13:00 is this kind of messy bundle of who we 13:02 are so if you want 13:04 cognitive diversity you have to have 13:07 identity diversity and the mechanisms 13:09 which that play out are actually 13:10 four-fold so first as we just mentioned 13:12 identity diversity correlates with 13:13 cognitive diversity so if you want a 13:14 diverse room 13:15 in terms of how that people think you 13:17 need diversity and how people look 13:19 second people think differently and 13:21 harder 13:22 when they're around identity diverse 13:23 people so this has worked from catherine 13:25 phillips 13:26 if i'm in a room with people just like 13:27 myself i kind of take it easy 13:29 when i'm in a room with people who are 13:30 different from me i just think harder i 13:32 generate new ideas 13:34 third identity diversity influences what 13:37 people are experts in 13:38 so it's not just how we filter the world 13:41 in terms of through our identities 13:42 but also what really interests people is 13:45 strongly a function of who they are 13:47 and so our expertise is going to 13:48 correlate with what we know 13:51 and then last when you're drawing from a 13:53 diverse pool yes it's larger but it's 13:55 also 13:56 more diverse so one reason you want even 13:58 if there were no differences 14:00 in people based on identity diversity by 14:03 hiring from an identity diverse pool 14:05 yes you're getting more talent but 14:06 you're also getting more diverse talent 14:09 all right sum it up diversity 14:13 improves performance when the task is 14:15 hard when it's easy 14:17 someone can just handle it it improves 14:19 performance when the task is hard 14:21 and it's cognitive and identity 14:23 diversity that really drive this 14:25 because who we are affects how we think 14:30 last when you think about what's going 14:31 to give you sustainable competitive 14:32 advantage 14:33 it's going to be your performance on 14:35 these complex tasks 14:37 thank you very much 14:45 you