Useful Art

On March 16 he came up with an extremely simple model to calculate the number of COVID-19 infected worldwide at moment t : (I_ {t}) is the number of infected at the end of a specific infection period. That number is equal to the number of infected people in the last period ((L_ {t}) = (I_ {t-1})) plus the number of infections that the newcomers ((N_ { t}) = (I_ {t-1} -I_ {t-2})) originate. That number is (c * N_ {t}), where for the contamination number (c) a constant (c = 2) has been assumed and for the length of the infection period also a constant (p) has been assumed ( p = 5 days), both taken from worldometers.info. He summarized this in a formula:

I_{t} = L_{t} + c*N_{t}.

Why, he thought? What is the purpose of such a formula? What’s in it for us and what can we do with it? That formula is art, he knew, and art can become knowledge if the story the art tells is sufficiently consistent and sufficiently reliable. What a translation in mathematical terms can help with is judging the story. He believed that mathematical formulas translate stories. Not only can they help assess their coherence, but they can also – when complicated enough – hide serious flaws for those who blindly rely on ‘difficult’ expertise. The latter, he believed, is too often unwittingly concealed in disregarding ‘silent’ assumptions. This is a hobby horse of his. He will probably come back to it.

Anyway, that was not an answer to the question — more of a side effect. The why of translating stories back and forth to mathematics that have the ambition to become knowledge is in that ambition. Why do we sometimes want knowledge stories? Two things.

One: to describe a situation faithfully, both now, in the future and in the past. Here coherence and ‘truth’ and balance play a role and reality prevails, he thought.

Two: to have a story that can change the world, for example when you want to bend the COVID-19 infection number down. Convincing stories and reliable mechanisms play the main role here and the story aims to be a guide for policy change.

If you look at the COVID-19 pandemic, we need both types of models.