by Matthew Syed
39 Joseph Henrich and Michael Muthukrishna argue that differences in individual IQ are an emergent property of the collective brain. See ‘Innovation in the Collective Brain’, Philosophical Transactions of the Royal Society, 19 March 2016.
40 Joseph Henrich, The Secret of Our Success.
41 AnnaLee Saxenian, Regional Advantage: Culture and Competition in Silicon Valley and Route 128 (Harvard University Press, 1994).
42 AnnaLee Saxenian, Regional Advantage.
43 Glenn Rifkin and George Harrar, The Ultimate Entrepreneur: The Story of Ken Olsen and Digital Equipment Corporation (Contemporary Books, 1988).
44 AnnaLee Saxenian, Regional Advantage.
45 AnnaLee Saxenian, Regional Advantage.
46 Tom Wolfe, ‘The Tinkerings of Robert Noyce: How the Sun Rose on the Silicon Valley’, Esquire, December 1983.
47 Walter Isaacson, Innovators: How a Group of Inventors, Hackers, Geniuses and Geeks Created the Digital Revolution (Simon & Schuster, 2014).
48 https://www.cnet.com/news/steve-wozniak-on-homebrew-computer-club/
49 AnnaLee Saxenian, Regional Advantage.
50 https://www.vox.com/2014/12/9/11633606/techs-lost-chapter-an-oral-history-of-bostons-rise-and-fall-part-one
51 AnnaLee Saxenian, Regional Advantage.
52 http://djcoregon.com/news/2012/06/19/building-20-what-made-it-so-special-and-why-it-will-probably-never-exist-again/
53 Another line of research is conducted by network theorists themselves. One famous study by Sandy Pentland of MIT analysed eToro, a platform for financial traders. Users can look up each other’s trades, portfolios and past performance, and can copy trading ideas if they think it will increase their own profits. Pentland and his colleagues collected data from 1.6 million users, tracking almost everything about the exchanges between them, and financial return.
They found that traders who were isolated in the network performed poorly. They had ‘impoverished opportunities for social learning because they had too few links to others’. But the researchers also found that people who were highly interconnected also performed poorly. Why? Because they were embedded in a web of feedback loops, so that they were hearing the same ideas over and over again. They were caught up in echo chambers.
It was traders whose networks exposed them to new ideas, but not merely recycled stale ideas, who performed the best. In fact, by subtly reshaping the structure of the network, and by offering small incentives to nudge people out of the echo chambers, Pentland was able to raise the financial return of the entire network. ‘By reducing idea flow to allow greater diversity, we moved the social network back into its sweet spot and raised average performance,’ he said.
54 These points about recombinant innovation in football were also made in my column for The Times: https://www.thetimes.co.uk/article/why-english-footballs-reluctance-to-embrace-idea-sex-is-stopping-the-game-from-evolving-gs75vb30v
55 Owen Slot, The Talent Lab: How to Turn Potential Into World-Beating Success (Ebury, 2017).
56 Owen Slot, The Talent Lab.
57 https://www.open.edu/openlearn/history-the-arts/history/history-science-technology-and-medicine/science-the-scottish-enlightenment/content-section-3.1
58 https://www.open.edu/openlearn/ocw/mod/oucontent/view.php?id=1944&printable=1
5: Echo Chambers
1 https://usatoday30.usatoday.com/life/2001-07-16-kid-hate-sites.htm
2 https://www.splcenter.org/20140331/white-homicide-worldwide
3 http://nymag.com/intelligencer/2019/04/ex-white-nationalist-says-they-get-tips-from-tucker-carlson.html
4 See Eli Saslow, Rising Out of Hatred: The Awakening of a Former White Nationalist (Doubleday, 2018). Also see: https://iop.harvard.edu/forum/im-not-racist-examining-white-nationalist-efforts-normalize-hate https://www.youtube.com/watch?v=LMEG9jgNj5M
5 Data provided by the academic Angela Bahns, personal correspondence.
6 https://www.ncbi.nlm.nih.gov/pubmed/26828831
7 Data provided by Bahns, measured in 2009.
8 Conversation with the author.
9 http://www.columbia.edu/~pi17/mixer.pdf
10 Eli Pariser, The Filter Bubble: What the Internet is Hiding from You (Viking, 2011).
11 https://qz.com/302616/see-how-red-tweeters-and-blue-tweeters-ignore-each-other-on-ferguson/
12 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140520/
13 https://www.tandfonline.com/doi/pdf/10.1080/1369118X.2018.1428656
14 Kathleen Hall Jamieson and Joseph N. Cappella, Echo Chamber: Rush Limbaugh and the Conservative Media Establishment (Oxford University Press Inc., 2010).
15 https://aeon.co/essays/why-its-as-hard-to-escape-an-echo-chamber-as-it-is-to-flee-a-cult
16 https://aeon.co/essays/why-its-as-hard-to-escape-an-echo-chamber-as-it-is-to-flee-a-cult
17 https://aeon.co/essays/why-its-as-hard-to-escape-an-echo-chamber-as-it-is-to-flee-a-cult
18 https://aeon.co/essays/why-its-as-hard-to-escape-an-echo-chamber-as-it-is-to-flee-a-cult
19 Eli Saslow, Rising Out of Hatred.
20 For much of the biographical detail in this section, see Eli Saslow, Rising Out of Hatred.
21 https://www.splcenter.org/sites/default/files/derek-black-letter-to-mark-potok-hatewtach.pdf
22 https://philpapers.org/rec/HINTFO-3
23 John Locke, An Essay Concerning Human Understanding (Clarendon Press, 1975).
6: Beyond Average
1 Material on Eran and Keren Segal taken from a personal interview; also Eran Segal and Eran Elinav, The Personalized Diet: The Revolutionary Plan to Help You Lose Weight, Prevent Disease and Feel Incredible (Vermilion, 2017).
2 Todd Rose, The End of Average: How to Succeed in a World that Values Sameness (Penguin, 2017).
3 http://www.accident-report.com/Yearly/1950/5002.html
4 Todd Rose, The End of Average.
5 Todd Rose, The End of Average.
6 Todd Rose, The End of Average.
7 A. Wrzesniewski, Berg, J. M., Grant, A. M., Kurkoski, J., and Welle, B., ‘Dual mindsets at work: Achieving long-term gains in happiness’. Working paper 2017.
8 Adam Grant, Originals.
9 Conversation with the author.
10 Conversation with the author.
11 https://www.ncbi.nlm.nih.gov/pubmed/26590418
12 Detail from this chapter taken from interviews with Segal and others, plus Eran Segal and Eran Elinav, The Personalized Diet.
13 Author interview.
14 Todd Rose and Ogi Ogas, Dark Horse: Achieving Success Through the Pursuit of Fulfillment (HarperOne, 2018).
15 Ellwood Cuberley, Public School Administration: A Statement of the Fundamental Principles Underlying the Organization and Administration of Public Education (1916).
16 https://www.edsurge.com/news/2018-07-31-6-key-principles-that-make-finnish-education-a-success
17 Caroline Criado Perez, Invisible Women: Exposing Data Bias in a World Designed for Men (Kindle edition, 2019).
18 https://www.ncbi.nlm.nih.gov/pubmed/12495526
19 Todd Rose, The End of Average.
20 Author interview.
21 https://adobe99u.files.wordpress.com/2013/07/2010+jep+space+experiments.pdf
22 Some of the most recent is led by Tim Spector, an epidemiologist at King’s College, London.
7: The Big Picture
1 Kevin N. Laland, Darwin’s Unfinished Symphony: How Culture Made the Human Mind (Princeton University Press, 2017).
2 Author interview. See also Joseph Henrich, The Secret of Our Success.
3 Kevin N. Laland, Darwin’s Unfinished Symphony.
4 I discussed this aspect of racism in this Times column: https://www.thetimes.co.uk/article/black-players-helped-to-fight-racism-now-game-needs-them-in-positions-of-power-592jgc078
5 Another way of removing bias is by using algorithms to make hiring decisions or, at the very least, to whittle down the list of candidates. After all, machines are not subject to the stereo
typing that often influences human judgement. At least, that is the theory.
In truth, as the author Cathy O’Neil has shown in her book Weapons of Math Destruction (Penguin, 2017), algorithms can themselves reflect the biases that exist within societies. She relates the case of Gild, an American start-up that looks at millions of data points to assess the suitability of candidates for jobs, mainly in the tech industry. One predictor of job success is how well integrated a coder is with the coding community. Those with larger followings score higher, as do those connected to influential coders.
But while seeking correlations, the Gild algorithm finds other patterns, too. It turns out, for example, that frequenting a Japanese manga site is a ‘solid predictor of strong coding’. On the surface, this sounds like a useful piece of information for any company hoping to recruit top coders.
Yet now consider the effects on gender. Women, on average, perform 75 per cent of the world’s unpaid care work. A talented female coder might therefore be expected, on average, to have less time to spend hours on sites like manga. And if the content of the website is not women-friendly, they are even less likely to visit it. As O’Neil puts it: ‘if, like most of techdom, that manga site is dominated by males and has a sexist tone, a good number of women in the industry will probably avoid it.’
This means that an algorithm that lowers the relative score of people not visiting such sites will entrench an unfair bias against talented female coders. ‘Gild undoubtedly did not intend to create an algorithm that discriminated against women,’ Caroline Criado Perez has written. ‘They were intended to remove human biases. But if you aren’t aware of how those biases operate, if you aren’t collecting data and taking a little time to produce evidence-based processes, you will continue to blindly perpetuate old injustices. And so by not considering ways in which women’s lives differ from men’s, both on and offline, Gild’s coders inadvertently created an algorithm with a hidden bias against women.’
6 https://hbr.org/2019/06/why-you-should-create-a-shadow-board-of-younger-employees
7 https://www.npr.org/2015/09/14/440215976/journalist-says-the-drone-strike-that-killed-awlaki-did-not-silence-him
Footnotes
1 Collective Blindness
FN1 This was partly about the fear that gay staff, particularly those who had not come out, might be subject to blackmail.
FN2 These studies are suggestive but not yet conclusive. It might not be diversity driving success, but the other way around. The successful firms may be able to afford more diversity. Later, we will bolster the argument that the relationship is causal.
2 Rebels Versus Clones
FN1 Why wasn’t the Poll Tax quashed at cabinet? According to King and Crewe, the checks and balances failed: ‘The policy was gestated and born almost wholly in-house, within one corner of the already secretive and secluded world of Whitehall.’ It was ultimately waved through during a meeting at Chequers with only half the cabinet in attendance, where few were aware of what was being discussed in advance, and where no papers had been circulated.
FN2 It is, of course, possible to come up with historical examples where a narrow demographic (say, aristocrats or peasants) have come up with enlightened policies, but it is mistaken to infer that narrow demographics make for better decision-making groups. The problem is that we don’t see the counterfactual: would a more diverse group have made a better decision? This is why diversity science is so important. Randomised trials show that diverse teams systematically come up with superior judgements, better predictions and wiser strategies.
FN3 Predictions also create rich data sets, and are wonderfully amenable to mathematical analysis.
FN4 This is a point that has been made by the economist Friedrich Hayek, who showed how prices emerge as a consequence of independent judgements by lots of different people, acting upon their own information and preferences. Often, market prices do an astonishingly effective job at combining diffuse information.
FN5 The security of the naval Enigma had been strengthened in early 1942, making it particularly difficult to crack.
3 Constructive Dissent
FN1 It was Mallory who responded to a journalist badgering him for the reason he wished to risk life and limb to climb Everest with the immortal line: ‘Because it’s there!’
FN2 I refer to this incident in my book Black Box Thinking in the context of safety investigations.
4 Innovation
FN1 The answer to the first triad is ‘table’ (i.e. table manners, round table, table tennis). For the second triad, the solution is ‘card’ (i.e. playing card, credit card, report card).
FN2 Tim Wigmore, a British sports journalist, argues that many technical innovations in sport are, when you take a closer look, recombinant in nature. Indian cricketers refined the reverse sweep by drawing on insights from tennis. Novak Djokovic learned his famous slide by incorporating ideas from his love of skiing. The same pattern applies to the ‘flop’ of high jumper Dick Fosbury, the ‘tomahawk’ serve of female table-tennis player Ding Ning, and the unusual eye-tracking techniques of rugby player Danny Cipriani.
5 Echo Chambers
FN1 A 2019 study led by Ana Lucía Schmidt, a computational social scientist based in Italy, came to broadly similar conclusions. She analysed 376 million Facebook users’ interactions with 900 news outlets and concluded that ‘selective exposure drives news consumption . . . we find a distinct community structure and strong user polarization’. A different study concluded that ‘segregation of users in echo chambers might be an emerging effect of users’ activity on social media’.
https://pdfs.semanticscholar.org/e05f/05f773c9fc3626fa20f9270e6cefd89950db.pdf and https://arxiv.org/abs/1903.00699
FN2 In her excellent book Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many (Princeton University Press, 2012), the Yale political scientist Hélène Landemore provides a powerful defence of democracy from the perspective of collective intelligence. Under most conditions, she shows that many minds will reach better decisions than oligarchies, dictatorships and military juntas. Classic examples include Condorcet’s jury theorem, developed by the Marquis de Condorcet and published in his 1785 work Essay on the Application of Analysis to the Probability of Majority Decisions.
6 Beyond Average
FN1 Formally, they often take point averages as representative of a class of people while overlooking (or completely ignoring) the distribution from which it was calculated.
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