Forecast models
have been used for
government decision
making for decades.
Forecast models created
great public awareness
when used to over-predict
COVID-19 deaths.
COVID-19 exposed
the problem with
computer models.
Computer models of
a complex system
rarely explain
something in
the real world
well enough to
make predictions.
But they get abused
by policymakers
anyway.
Computer models
are actually a gross
simplification of reality
( an abstraction ).
The more
complex
the model,
the greater
uncertainty
becomes.
Trying to model complex
things goes well beyond
looking at variables that
we can actually measure.
Assumptions have
to be made, and they
are inherently subjective,
rendering model outputs
relatively useless
as forecasting tools.
That’s why most
computer modelers
talk about “projections”
rather than “predictions.”
Estimated mortality
from COVID-19 shifted
massively over time,
as new data came in.
A new public insight
into the limitations
of computer modeling
will hopefully spread
from COVID-19 science,
to climate change science.