women and the collective intelligence of human groups

Christopher F. Chabris, Ph.D. Assistant Professor of Psychology, Union College

Research Affiliate, MIT Center for Collective Intelligence

Thanks to: Mark Glickman, Jackie Mandart, Matthew Fontaine, Jeremy Gray, Todd Braver, Marc Hauser, James Lee, Konika Banerjee, Valen Johnson, Fritz Tsao, Anita Woolley, Nada Hashmi,

Sandy Pentland, Tom Malone, Ishani Aggarwal

Women and the Collective Intelligence of Human Groups

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© 2001 Macmillan Magazines Ltd

The importance of group performance • As work becomes more complex, groups become more crucial:

– Medicine is increasingly team-based (Linzer et al., 2006)

– Over half of the articles produced in the natural and social sciences are authored by more than one person (Wuchty et al., 2007)

– The average size of teams producing patents and scientific articles nearly doubled between 1955 and 2000

• Many groups perform a wide variety of tasks rather than repeatedly do one particular thing

• BUT: smart people can choose horrible group processes (e.g., CIA) – Suggests that the intelligence of a group as a whole may not be a simple function of individual member intelligence

Collective Intelligence • Classic examples of “the wisdom of crowds” (ask the audience)

and “swarm intelligence” (honeybee dances) • Modern examples: Google, Wikipedia, prediction markets

• Groups are sometimes smarter than individuals • But are some groups smarter than others? • More interestingly: Do groups have characteristic intelligence

levels that cannot be explained by the intelligence levels of their individual members?

• Simple-minded research strategy: Imitate researchers who discovered and characterized individual intelligence

“For each individual among the many has a share of excellence and practical wisdom, and when they meet together, just as they become in a manner one man, who has many feet, and hands, and senses, so too with regard to their character and thought.”

— Aristotle, Politics, c. 350 B.C.E.

The rest of the talk • What is individual intelligence?

• How did we go about measuring collective intelligence? • What does collective intelligence do for a group? • What makes a group more intelligent?

– among other things, having more women • What are the implications?

– evidence from a study of Wall Street firms – evidence from research on corporate boards – why there are few women in these elite groups

20

Err

or R

ate

(%)

0

40

Lure NonlureDistractor Type

36.6

3.4

Mental rotation task

Working memory task

4

Res

pons

e Ti

me

(s)

0

8

0 40Angle (deg)

80 120 160

2

6

elbow belly

snake elbow

porch belly

pencil belly

snake pencil

Evidence for intelligence in individuals

RAPM WM VF RT MR Coo Cat g Raven’s Advanced Progressive Matrices — .50

Working Memory .39 — .46

Verbal Fluency .36 .48 — .42

Response Time .41 .28 .41 — .39

Mental Rotation .41 .29 .15 .21 — .34

Coordinate Spatial Encoding .32 .30 .07 –.02 .04 — .25

Categorical Spatial Encoding .21 .12 –.02 .13 .16 .21 — .20 (N = 111, g = 36%)

Chabris (2007)

The Law of General Intelligence Measurements of cognitive ability tend to correlate positively across individuals (first discovered by Spearman, 1904).

Should we be surprised? • Gould: “The fact of pervasive intercorrelation between mental

tests must be among the most unsurprising major discoveries in the history of science.”

• Brand: “... a normal expectation is that time spent in one activity is time that is lost for another ... Thus, insofar as ‘practice makes perfect’ and time is finite, the pervasive intercorrelation between mental abilities should actually tend to be negative …”

Brain mechanisms of intelligence

Whole Brain Volume

gray matter

white matter

PFC gray matter volume

White matter organization (FA)

White matter integrity (NAA)

Simple response time

Choice response time

Variability of RT

% Variance Explained byMeasures of g or IQ

7.3

9.6

16.8

19.4

26.0

9.6

24.0

6.8

0 2010 30

10.9

Chabris (2007)

Measuring collective intelligence: Study 1 • 40 teams of 3 people each • 51% of subjects male; average age 32 (range 18–66) • Each subject completed individual IQ test (RAPM, odd items) • Groups worked together on 5-task battery:

– Brainstorming (# of uses for a brick in 10 mins) – Matrix reasoning (RAPM, even items) – Moral reasoning (“Disciplinary Action Case”) – Plan shopping trip (logistical coordination) – Typing (enter Wikipedia article into Google Doc)

• Groups completed “criterion task” (checkers vs. computer)

Woolley, Chabris, et al. (Science, 2010)

Evidence for intelligence in groups

• Average intertask correlation r = .28 (all r > 0) • First factor (c) accounts for 43% of variance • Individual intelligence of members does not explain collective

intelligence of group

Woolley, Chabris, Pentland, Hashmi, & Malone (Science, 2010)

Replication in larger followup (Study 2) • 579 subjects @ two sites • 152 teams of 2–5 people • Additional individual

measures (personality, social sensitivity) + different IQ test (WPT vs. RAPM)

• Individuals rated satisfaction, motivation, psychological safety, and group’s cohesiveness

• Sociometric badges recorded turn-taking during discussions


c versus g as predictors


2"

3"

4"

5"

6"

7"

8"

)2.00" )1.50" )1.00" )0.50" 0.00" 0.50" 1.00" 1.50" 2.00"

Collective Intelligence (c)

Obs

erve

r rat

ings

r = .59

Thin-slice study: Raven’s APM

Predictors of collective intelligence • NOT: cohesion, satisfaction, motivation, personality, safety • Turn-taking in the group

– measured by MIT Media Lab sociometric badges – the more even the distribution of # of speaking turns among the members, the smarter the group (r = –.41, p = .01)

• Proportion of women in the group (r = .23, p = .007)

– half of this effect is mediated by social ability (female > male)


% of women and collective intelligence


% of Women in Group

C (

cont

rolle

d fo

r gro

up s

ize)

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

-10 0 10 20 30 40 50 60 70 80 90 100

Social ability predicts collective intelligence • Social ability of group members

– as measured by Reading the Mind in the Eyes test – Example: terrified, upset, arrogant, or annoyed?

– Example: playful, comforting, irritated, or bored?

r = .26, p = .002


Collective Intelligence on Wall Street

• Work that seems on the surface to be done by individuals often depends critically on the efforts of a collective

• Groysberg (2010) studied Wall Street equity analysts ranked by Institutional Investor as “stars” from 1988–1996 – Stars who switched firms dropped in rankings and took 5+ years to recover their earlier rank – If entire teams moved with the star leader, no performance drop – Women switchers regained lost star status faster than men

è Financial analysis is a team activity, not an individual one!

Research Article

Sex Differences in IntellectualPerformanceAnalysis of a Large Cohort of Competitive Chess PlayersChristopher F. Chabris1 and Mark E. Glickman2

1Department of Psychology, Harvard University, and 2Department of Health Policy and Management, Boston UniversitySchool of Public Health

ABSTRACT—Only 1% of the world’s chess grandmasters arewomen. This underrepresentation is unlikely to be causedby discrimination, because chess ratings objectively reflectcompetitive results. Using data on the ratings of more than250,000 tournament players over 13 years, we investigatedseveral potential explanations for the male domination ofelite chess. We found that (a) the ratings of men are higheron average than those of women, but no more variable; (b)matched boys and girls improve and drop out at equalrates, but boys begin chess competition in greater numbersand at higher performance levels than girls; and (c) inlocales where at least 50% of the new young players aregirls, their initial ratings are not lower than those of boys.We conclude that the greater number of men at the highestlevels in chess can be explained by the greater number ofboys who enter chess at the lowest levels.

The game of chess has been studied by computer scientists andcognitive psychologists as a model arena of human intellectualperformance. Research on computer chess has culminated inprograms that can defeat the best human players (e.g., Hsu,2002), and research on chess masters has yielded seminal dis-coveries, such as the chunk structure of short-term memory(Chase & Simon, 1973), and has contributed to debates on theimportance of pattern recognition and deliberate thought inexpertise (Burns, 2004; Chabris & Hearst, 2003; Gobet & Si-mon, 1996). But one of the most striking facts about chesscompetition has received little study: the dramatic lack of wom-en among the game’s elite performers. None of the official world

champions has been a woman, no champion of a major country isa woman, and as of January 2004, only 9 of the world’s 894 chessgrandmasters—1%—were women (according to data in How-ard, 2005).

Analyzing possible explanations for the underrepresentationof women among the chess elite may help explain the under-representation of women at the highest levels in other fields,such as tenured professorships in mathematics, science, andengineering. It has been suggested (e.g., Pinker, 2005; Sum-mers, 2005) that differences between men and women in thedistribution of cognitive abilities required for success in thesefields can partly account for the disparity (the ability-distribu-tion hypothesis). In particular, men and women may differ inmean performance levels, variability of performance, or both;evidence suggests that in cognitive abilities, both types of dif-ferences are found (Halpern, 2000; Hedges & Nowell, 1995).

However, the possibility of ‘‘old boys networks’’ of men whofunction as gatekeepers to high positions in these fields, coupledwith the subjective nature of assessing achievement, makes itdifficult to distinguish between an objective lack of achievementor credentials and discrimination by the existing social systemas causes. In chess, there are neither gatekeepers nor subjectiveassessments; in particular, the rating system invented by Elo(1986) objectively measures individual skill solely on the basisof results of tournament games. The U.S. Chess Federation(USCF) applies this system to rate tens of thousands of playerswho participate in events that are open to all. Therefore, theoverrepresentation of men at the highest levels in chess is, atfirst glance, more consistent with an ability-distribution hy-pothesis than with a social-system account. (Note that a differ-ence in mean, variance, or both could explain the observeddifferences at the upper tail of the distribution.)

However, other explanations are possible. One is that men andwomen may have differential dropout rates over time. Men andwomen may start out with equal endowments of the abilities

Address correspondence to Mark E. Glickman, Center for HealthQuality, Outcomes & Economics Research, Edith Nourse RogersMemorial Hospital (152), Bldg. 70, 200 Springs Rd., Bedford, MA01730, e-mail: [email protected].

PSYCHOLOGICAL SCIENCE

1040 Volume 17—Number 12Copyright r 2006 Association for Psychological Science

Research Article

Sex Differences in IntellectualPerformanceAnalysis of a Large Cohort of Competitive Chess PlayersChristopher F. Chabris1 and Mark E. Glickman2

1Department of Psychology, Harvard University, and 2Department of Health Policy and Management, Boston UniversitySchool of Public Health

ABSTRACT—Only 1% of the world’s chess grandmasters arewomen. This underrepresentation is unlikely to be causedby discrimination, because chess ratings objectively reflectcompetitive results. Using data on the ratings of more than250,000 tournament players over 13 years, we investigatedseveral potential explanations for the male domination ofelite chess. We found that (a) the ratings of men are higheron average than those of women, but no more variable; (b)matched boys and girls improve and drop out at equalrates, but boys begin chess competition in greater numbersand at higher performance levels than girls; and (c) inlocales where at least 50% of the new young players aregirls, their initial ratings are not lower than those of boys.We conclude that the greater number of men at the highestlevels in chess can be explained by the greater number ofboys who enter chess at the lowest levels.

The game of chess has been studied by computer scientists andcognitive psychologists as a model arena of human intellectualperformance. Research on computer chess has culminated inprograms that can defeat the best human players (e.g., Hsu,2002), and research on chess masters has yielded seminal dis-coveries, such as the chunk structure of short-term memory(Chase & Simon, 1973), and has contributed to debates on theimportance of pattern recognition and deliberate thought inexpertise (Burns, 2004; Chabris & Hearst, 2003; Gobet & Si-mon, 1996). But one of the most striking facts about chesscompetition has received little study: the dramatic lack of wom-en among the game’s elite performers. None of the official world

champions has been a woman, no champion of a major country isa woman, and as of January 2004, only 9 of the world’s 894 chessgrandmasters—1%—were women (according to data in How-ard, 2005).

Analyzing possible explanations for the underrepresentationof women among the chess elite may help explain the under-representation of women at the highest levels in other fields,such as tenured professorships in mathematics, science, andengineering. It has been suggested (e.g., Pinker, 2005; Sum-mers, 2005) that differences between men and women in thedistribution of cognitive abilities required for success in thesefields can partly account for the disparity (the ability-distribu-tion hypothesis). In particular, men and women may differ inmean performance levels, variability of performance, or both;evidence suggests that in cognitive abilities, both types of dif-ferences are found (Halpern, 2000; Hedges & Nowell, 1995).

However, the possibility of ‘‘old boys networks’’ of men whofunction as gatekeepers to high positions in these fields, coupledwith the subjective nature of assessing achievement, makes itdifficult to distinguish between an objective lack of achievementor credentials and discrimination by the existing social systemas causes. In chess, there are neither gatekeepers nor subjectiveassessments; in particular, the rating system invented by Elo(1986) objectively measures individual skill solely on the basisof results of tournament games. The U.S. Chess Federation(USCF) applies this system to rate tens of thousands of playerswho participate in events that are open to all. Therefore, theoverrepresentation of men at the highest levels in chess is, atfirst glance, more consistent with an ability-distribution hy-pothesis than with a social-system account. (Note that a differ-ence in mean, variance, or both could explain the observeddifferences at the upper tail of the distribution.)

However, other explanations are possible. One is that men andwomen may have differential dropout rates over time. Men andwomen may start out with equal endowments of the abilities

Address correspondence to Mark E. Glickman, Center for HealthQuality, Outcomes & Economics Research, Edith Nourse RogersMemorial Hospital (152), Bldg. 70, 200 Springs Rd., Bedford, MA01730, e-mail: [email protected].

PSYCHOLOGICAL SCIENCE

1040 Volume 17—Number 12Copyright r 2006 Association for Psychological Science

Implications of collective intelligence • Measurability of c provides foundation for a new approach to the

science of group performance • Collective intelligence may be easier to raise than individual g

– change composition of team + cognitive diversity, social perception skills, # of women? – interventions/processes to improve group performance – tools and communications structures

• Because collective intelligence predicts a group’s success, take special care in evaluating groups and teams

• Maximizing collective intelligence could be new strategic aim for organizations of various sizes

women and the collective intelligence of human groups

Documents