
Open Source AI for Economic Policy Recommendations - keenmaster
https://blog.einstein.ai/the-ai-economist-moonshot/
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verdverm
[https://en.m.wikipedia.org/wiki/Preference_falsification](https://en.m.wikipedia.org/wiki/Preference_falsification)

Preference Falsification is likely the most important, under used theory in
economics. I suspect these models will fail miserably because they will always
have bad data until we stop silencing opinions we do not agree on.

Let's have constructive conversations please, please, please.

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keenmaster
Huge amounts of economic data come from inherently boring, unbiased sources
(employment records, tax records, exports, manufacturing output, etc...). What
data do you think is being falsified? You seem to be referring to survey-type
data.

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verdverm
Claiming any data as unbiased seems illogical to me, note how they are
constantly revised.

Yes, people's response to surveys are one. Their reaction to policy is
another. If this "ai" is modelling policy decisions and outcomes, what data is
being feed in?

Further, how does one explain said "ai" recommendations and rational to the
public?

(edit)

After looking over the code, I find this model incredibly simplistic and not
capable of nearing the complexity IRL.

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keenmaster
People's reactions to policy decisions/outcomes don't matter nearly as much as
the actual outcomes. I think most people will agree that simultaneously
raising GDP per capita while reducing inequality and increasing innovation
would be a good outcome. Those are all things that can be quantified and
measured with very little bias. Survey data isn't as important comparatively.

As for the model explaining itself: the model can show relationships and
patterns that humans hadn't previously detected, because there were too many
variables. We can deduce new economic strategies by observing model output,
such that we can even carry out those strategies without further use of the
model. There are also ways to make ML models explain themselves.

~~~
verdverm
I will respectfully disagree with the last two paragraphs without going point
by point. I sense too much attachment to the project and biases therein.

