So let's start session A.
We will discuss of what happens,
before we crash number.
So the goal of this session is to understand
basic epistemological problems that are affecting the usefulness of scientific analysis.
Since I'm afraid that a few of you will not be familiar with the work epistemology.
Let's define what epistemology means.
Epistemology deals with how we know what we know.
That is, it's a study how we form our belief.
How we are generating knowledge,
we are validating knowledge,
we are learning about the external mode.
In particular, in this specific session
we will see the difference between narrative and storytelling.
And this difference is important,
because it is true that what we decide before start
crashing numbers is affecting the usefulness of the numbers that we will generate.
Just to thread better,
to have a better understand of this concepts,
let's have an example.
And this example, I didn't make up,
it has really happened to me because it's so bizarre that you
would not be able to make up something like that.
I was in a conference on world food security in Zurich
in Switzerland and then in this conference during the different sessions,
we had advise of very well known experts.
So when dealing with national policy we had the first expert saying,
one important strategy policy has to be keep price of food commodities low,
and of course this is important,
because for the urban poor who are not earning enough income,
low prices are the only guarantee to have food security.
Then in the same session we had another important professor saying
that the thing to do is to keep the price of food commodity high. And why is that?
Because a poor farmer cannot invest in
buying imports or to have
better technology in producing food if they cannot pay back their investment.
So the secret to have more local food production is to have food commodity price high,
guaranteeing the farmers enough revenue.
Then we had another session about international policy,
and we got one scientist saying that we should reduce the import from the South,
because in this way we are,
the North is externalizing environmental impacts and
problems to the South and we got another professor saying, that what should be done,
which will increase, the North should increase the import from the South,
because this will be more possible for poor developing countries to take advantage of
comparative advantage and develop their agricultural sector.
Finally, we had a session about social issue and we got
a person making the point that we should preserve
cultural heritage that is threatened by globalization.
We should protect cultural diversity.
But in the same session we got
another professor saying that we should fight local cultural heritage and
she was caring for an estate in India in which
the wives were burned alive together with the dead husband.
And of course she had a pretty good point.
So what can we make of all this,
is that these scientists are giving contrasting advice,
because they are incompetent?
No, they were doing a very convincing explanation what they were,
of the point they were doing.
The problem is that they were,
what we will say different storytellers in the sense that
the professor dealing with keeping food commodity
low was a scientist who came from the North,
basically the urban in developed countries,
the majority of the population is urban.
The professor suggesting that the commodity should be high
was a professor of agricultural economics so he was having the point of view,
sharing the point of view of farmers.
In the same way, those who want to reduce import from the North,
from the South into the North was from Germany.
And the one we need to explore more was from Ghana.
Again, for the last two,
we had one Swiss feminist concerned for protecting
the cultural diversity that nobody can object this.
But at the same time, the sociologist from India,
had a very good point that at times you have to fight
a lot of our cultural heritage when it is not acceptable.
So in this example you have different narratives.
They are chosen by different story types.
OK? So, in science for governance,
they are no true or false narratives,
because you can have different explanations for the same events,
but a narrative would be the explanation of causality that you are considering.
So if we accept this definition,
what makes an explanation useful,
depends on what you want to do with your analysis.
Whether or not your narrative is useful for the purpose of your analysis.
So it's why we are using the narrative.
Let's use another example of the same concept.
Let's imagine that we have an event: The possible death of a particular individual.
Then we can have different narratives: That this person is dying,
because he doesn't have enough oxygen supply in the brain.
Or, we could have another explanation,
this person is affected by lung cancer.
And of course this explanation could be useful in different timescales.
If you have a problem of oxygen in the brain,
it's an issue of minutes.
What is, if the person has lung cancer,
it's about months or years.
Then you can have another explanation,
the individual is a heavy smoker.
Then these at a large scale is about a social problem.
And then another explanation would be, humans must die.
And every time I'm presenting this to an audience,
everyone is laughing, when you explain that humans must die,
but there is nothing to laugh about.
It is a perfectly acceptable scientific explanation.
So who are the storytellers here?
The storyteller for an oxygen supply in the brain is a doctor in an emergency room.
The storyteller for the lung cancer is a pharmaceutical researcher producing drugs.
And the problem with smoking has to do with a tax expert.
And human must die, may be a philosopher.
Maybe it should be anyone of us who should consider this.
So what is the point of this slide,
is that these are four useful storytellings.
OK? Each of these narratives is matching the purpose of a story teller.
What happens if we keep the same set of narratives,
but we're scrambling the story teller.
So the oxygen supply is a narrative to be used by a tax expert.
Or they individual is heavy smoker has to be used by the doctor in emergency room.
This narrative would be completely useless,
so these would be useless storytellings.
When we cut the narrative to the storyteller,
these couplings, it doesn't work.
OK. Why is it important to get into all this discussion?
Because in reality when we are taking real decisions in life,
one thing are the narrative and explanation,
one thing are the story teller,
who is deciding what is relevant and what is not relevant.
In relation to this point in this slide, you will see something that will be discussed
in the last week of the course again.
But it could be used now just to nail down the concept.
You have in terms of stakeholders the social actors,
they are affected or affecting the decision.
The two by two matrix: There are social actors that have a very high interest,
because they are affected, and they have low interest,
because they are not affected.
And then these would be the high and low rules.
And then you have two columns that actors that do not have enough power or actors that
have enough power for affecting the outcome and their policy choice.
So you have the people that are affected, but they can not decide anything.
They are vulnerable.
The people that are not affecting and neither affected,
they are not relevant.
Then you have a group of
social actors like local governments, institutions, entrepreneurs.
They are making decisions and they're affected by what happens.
And then you have a last group.
And they are by far the most dangerous,
the ones that are deciding what happens,
affecting the outcome of a policy,
but they are not affected by the result.
So this, the governments,
the local governments, institutions, entrepreneurs,
go through a semiotic process.
Semiotic means that they are using,
applying what they say,
and then they see the consequences.
The vulnerable community, they are just affected by the policies,
so they care, but they cannot do anything, because they are powerless.
So who are the people that are affecting the things,
but they are not affected by their consequence?
The UN organizations, the scientists providing win, win, win solutions,
the media solving the problems all over the planet,
the NGO saving the world, all the international aid programs.
So all these stakeholders,
they tend to decide.
And very often they decide on wishful thinking,
they think that things can't be solved in five years just
by the doing more efficient and better solution.
And the problem is they do not suffer from that decision.
So in way, this last view of the problem of narrative story telling,
points at an ethical dimension of the integrated assessment,
and in general, of the sustainability science.
That it would be very,
very important to be aware of this two by two matrix.
In conclusion, we can say that when dealing with complex issues,
we should abandon the belief that science can provide
an uncontested and a reliable knowledge about what is the best thing to do.
Prediction and control the science,
you know, can tell you what is the best thing to do, is a dream.
And we should move to a different strategy in which science looks for
robust knowledge that is based on a proper understanding and implication of complexity.
But if this is true, we should abandon the strategy of a cartesian dream of,
of control obtained through reductionsim and go to a different type of logic,
the one for instance suggested by George Box: "All models are wrong,
but some are useful."
So the approach of reductionism is
about trying to find a better solution by doing more of the same or studying more or,
getting more in detail,
making more measurements and getting more data.
But not necessarily this works,
because if you are not semantically,
framing the problem semantically well,
adding new data will not solve your problem.
The approach of complexity is that,
you have to find a relevant and useful semantic framing and stay away
of what is called hypo-cognition that is you focus on
the wrong framing and then you
still do a lot of research and
data on something which is not addressing the real problem.
