Phil Gyford

Contents page online here

Only one review really grabbed me this issue, which I paraphrase here. (pp 19-21.)

‘How Many Jellybeans’ by David Runciman

Reviewing Profiles, Probabilities and Stereotypes (UK, US) by Frederick Schauer. People tend to think that general rules (eg, pilots must retire at 60) are discriminatory, and that personally-applied, discretionary rules would be more fare, would take account of extenuating circumstances (eg, if applied by expert pilots or doctors). But their rules are also arbitrary and however expert they bring their own biases. Which isn’t to say an individual couldn’t perform better by exercising discretion, but how do you tell which individuals would perform better? Most people, or groups of people, will say that they are the best judge, and how do you determine if they’re right?

Of course, some general rules could be unfair (eg, pilots must retire when they go bald), but it does not make sense to argue against general rules on principle, simply because they are general. We should distinguish between these latter spurious generalisations and non-spurious ones. Deciding which non-spurious generalisations are useful in making general rules is what matters.

To argue, as the Gore Commission did when it considered the question of racial profiling, that considerations of risk must never be based on ‘stereotypes or generalisations’, is worse than useless (worse because it panders to a sloppy idealisation of individual discretion). How did they think that you could make risk assessments in the absence of stereotypes and generalisations?

Some non-spurious generalisations are worth rejecting as they can be stigmatising and counterproductive. Young Arab men pulled out of airline queues are suffering because their stereotype is already widespread and it doesn’t need institutional reinforcement; it should be compensated for. Whereas experienced old pilots might be encouraged to continue by their passengers if the law didn’t force them to retire. We should accept that stereotyping is a fact of life and look for the best stereotypes we can.

Reviewing The Wisdom of Crowds: Why the Many are Smarter than the Few (UK, US) by James Surowiecki. In a surprisingly wide variety of circumstances, even the best individual can be outperformed by the impersonal group. eg, guessing the number of jellybeans in a jar: the average guess of a large group is likely to be more accurate than the guess of an individual. And more accurate than the person in the group who is judged in advance to be the best jellybean guesser.

As a defence for democracy. What if the ignorance of the masses (as some people see it) may be a strength, not a weakness. eg Iraq war. Blair justified his decision in the face of widespread scepticism on two grounds: it was his job to take a lead and that he and his advisers had more information than the public. But perhaps the ignorant masses were better equipped to assess the nature of the risk than the experts. “…the crowd knew what the specialists did not: Saddam was not an immediate threat.”

But the crowd is not always right. Requires certain conditions for the crowd to make good decisions: members of group must be willing to think for themselves; they must be mostly independent of each other; must be reasonably decentralised; must be some means of aggregating opinions into a collective judgement. If people start second-guessing each other, or following each other, the crowd becomes a herd and herds are bad at decision making. Echoes Rousseau:

From the deliberations of a people properly informed, and provided its members do not have any communication among themselves, the great number of small differences will always produce a general will, and the decision will always be good.

Crowds do not do well the question is not a straight-forwardly cognitive one. They are not good at moral judgments. Might be good at, eg, whether terrorism or global warming is a greater threat. Three ways to answer that: ask experts, although experts rarely agree; ask politicians to exercise their judgement; ask a large group of people including members of the public to give their best guess. Suggests a futures market, which people could gamble on. But many would feel appalled at gambling on matters of life and death, hence the outcry over DARPA’s Policy Analysis Market in 2003. But it might have worked.