But even if we would have more time to go and research and get more data and
investigate and improve the quality of our dataset, there's always unknown stuff.
So that is again, the main learning to recognize that
we have this limitation of things that we don't know.
And one we could say, we'll, we have a gap, but
this gap don't worry in ten years time.
We'll know how to cover it,
but we don't know it today.
One of the main criticism that we received were related
to some parameters that we used a input for this model.
For example, the sanction for the GDP projection or
the sanction for the price of food projection.
So, it's very important when you
try to find the credibility of the result.
Try to consider the uncertainties that you can
have in the input of your models.
So, we've talked about the known information.
We've talked about the lack of data and
there's also the limitations of our models, and
using these very rigid tools to determine what we can do what we cannot do.
There are lots of advantages and disadvantages of choosing different
models, but Brett explained what are the important parameters to choose it, and
in the end you will have to make a choice, and they will always comes with tradeoffs.
What I want to highlight here in terms of limitations
is this inability to be able to make a case or
demonstrate certain interventions are not going to ruin your economy.
Another important thing that is very
important when you try to use models to analyze
is that sometimes to use a very good model to analyze
a very complicated model is sometimes is not
necessarily the best solution to be ambitious.
Now with case, for example, we work with a very complex and
compete model to evaluate the economic
impact of the different mitigation actions.
And however, at the end of the project,
some people who didn't know did a lot of
the questions related to this that we used and
that it could be very complicated thing in a project like this.
Maybe sometimes more important to be very clear with the main
assumption that you did to model and
that could be sometimes more important than to have a very complex model.
And that is very important in a project
with a participatory process,
because all the results will probably be reviewed by a lot of people.
So also very important are just the pragmatic constraints.
The skills we have, the time constraints that we had,
the resources constraints we had.
So these processes took three, four years and
there was a huge amount of data developed.
But one would say,
especially the researchers if they would have had more time.
Of course, they would have been able to provide more textures to some of these
scenarios, to spend more time identifying all the interventions that could
have been adopted into our model.
So one can clearly say that investing more time on research,
on improving the data quality would have beneficial results.
And probably would also see the gap diminishing, but
what was important from these processes was to deliver on time.
When the policy maker had to take a decision, we had to had the results there.
There was no all the benefit in extending the research work without and
missing that policy window.
And not just that, MAPS is about co-production of knowledge.
So that means it's about research, but it's also It's about process.
Let's ask Hernan who was in charge of the process of MAPS Chile to see what were
the challenges that emerge from prolonging the work from the process perspective.