From “Algorithms to Live By – The Computer Science of Human Decisions”, by Brian Christian and Tom Griffith (Henry Holt and Company, 2016)
p 147-148 (in Chapter 6 “Bayes’s Rule” ~ the statistics of causation and prediction)
Subheading: “Priors in the Age of Mechanical Reproduction”
Introductory quotation: “As if someone were to buy several copies of the morning paper to assure himself that what it said was true.” – Ludwig Wittgenstein
“The best way to make good predictions … is to be accurately informed about the things you’re predicting. … … representing the world in the correct proportions – having good priors, appropriated calibrated. By and large, for humans and other animals this happens naturally; as a rule, when something surprises us, it ought to surprise us, and when it doesn’t, it ought not to. Even when we accumulate biases that aren’t objectively correct, they still usually do a reasonable job of reflecting the specific part of the world we live in. …
“Everything starts to break down, however, when a species gains language. [emphasis added] What we talk about isn’t what we experience – we speak chiefly of interesting things, and those tend to be things that are uncommon. More or less by definition, events are always experienced at their proper frequencies, but this isn’t at all true of language. Anyone who has experienced a snake bite or a lightning strike will tend to retell those singular stories for the rest of their lives. And those stories will be so salient that they will be picked up and retold by others.
“There’s a curious tension, then, between communicating with others and maintaining accurate priors about the world. [“Priors” is statistical jargon for experiential background relevant to the subject matter when attempting to make predictions.] When people talk about what interests them – and offer stories they think their listeners will find interesting – it skews the statistics of our experience. That makes it hard to maintain appropriate prior distributions. And the challenge has only increased with the development of the printing press, the nightly news, and social media – innovations that allow our species to spread language mechanically.
…
“Simply put, the representation of events in the media does not track their frequency in the world.
…
“If you want to be a good intuitive Bayesian – if you want to naturally make good predictions, without having to think about what kind of prediction rule is appropriate – you need to protect your priors. Counterintuitively, that might mean turning off the news.” [end of quotation]
(The next chapter is titled “Overfitting – When to Think Less”…)
Views, interpretations of the above might be meta-discussion, relative to political reporting, perhaps even to the nature of internet forums. It also might reflect aspects of dhamma – i.e. dealing with conditioned defilements plaguing the human
mind.