"Ciência" e certezas

Para quem confunde ciência com certezas absolutas: 

"Taking a model literally is not taking a model seriously.

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Models can be both right, in the sense of expressing a way of thinking about a situation that can generate insight, and at the same time wrong - factually incorrect. Atoms do not consist of little balls orbiting a nucleus, and yet it can be helpful to imagine that they do.

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Nobel Prize-winning economist Peter Diamond said in his Nobel lecture that to me, taking a model literally is not taking a model seriously'. There are different ways to avoid taking models literally. We do not take either wave or particle theories of light literally, but we do take them both seriously. In economics, some use is made of what are called stylised facts: general principles that are known not to be true in detail but that describe some underlying observed or expected regularity. Examples of stylised fact are 'per-capita economic output (generally) grows over time', or people who go to university (generally) earn more', or in the UK it is (generally) warmer in May than in November'. These stylised facts do not purport to be explanations or to suggest causation, only correlation.

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We might think of models as being caricatures in the same sense. Inevitably, they emphasise the importance of certain kinds of feature - perhaps those that are the most superficially recognisable - and ignore others entirely.

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Thinking of models as caricatures helps us to understand how they both generate and help to illustrate, communicate and share insights. Thinking of models as stereotypes hints at the more negative aspects of this dynamic: in constructing this stereotype, what implicit value judgements are being made?

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Since each model represents only one perspective, there are infinitely many different models for any given situation.

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When the models fail, you will say that it was a special situation that couldn't have been predicted, but force equals mass times acceleration itself is only correct in a very special set of circumstances. Nancy Cartwright, an American philosopher of science, says that the laws of physics, when they apply, apply only ceteris paribus - with all other things remaining equal. In Model Land, we can make sure that happens. In the real world, though, the ceteris very rarely stay paribus."

Trechos retirados de "Escape from Model Land" de Erica Thompson.

"but some are useful"

"Distinguishing between Model Land and real world would reduce the sensationalism of some headlines and would also encourage scientific results to clarify more clearly where or whether they are expected to apply to the real world as well.

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As statistician George Box famously said, 'All models are wrong.' In other words, we will always be able to find ways in which models differ from reality, precisely because they are not reality. We can invalidate, disconfirm or falsify any model by looking for these differences. Because of this, models cannot act as simple hypotheses about the way in which the true system works, to be accepted or rejected.

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Box's aphorism has a second part: "All models are wrong, but some are useful.' Even if we take away any philosophical or mathematical justification, we can of course still observe that many models make useful predictions, which can be used to inform actions in the real world with positive outcomes. Rather than claiming, however, that this gives them some truth value, it may be more appropriate to make the lesser claim that a model has been consistent with observation or adequate for a given purpose. Within the range of the available data, we can assess the substance of this claim and estimate the likelihood of further data points also being consistent. 

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The extrapolatory question, of the extent to which it will continue to be consistent with observation outside the range of the available data, is entirely reliant on the subjective judgement of the modeller."

Trechos retirados de "Escape from Model Land" de Erica Thompson.

"following the science"

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"As I will show, reliance on models for information tends to lead to a kind of accountability gap. Who is responsible if a model makes harmful predictions? The notion of 'following the science becomes a screen behind which both decision-makers and scientists can hide, saying the science says we must do X' in some situations and 'it's only a model' in others. The public are right to be suspicious of the political and social motives behind this kind of dissimulation. Scientists and other authorities must do better at developing and being worthy of trust by making the role of expert judgement much clearer, being transparent about their own backgrounds and interests, and encouraging wider representation of different backgrounds and interests.

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Taking models literally and failing to account for the gap between Model Land and the real world is a recipe for underestimating risk and suffering the consequences of hubris. Yet throwing models away completely would lose us a lot of clearly valuable information. So how do we navigate the space between these extremes more effectively?"

Trechos retirados de "Escape from Model Land" de Erica Thompson. 

"Though Model Land is easy to enter, it is not so easy to leave"

"The frameworks we use to interpret data take many different forms, and I am going to refer to all of them as models.

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The contribution of the model is to add relationships between data.

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the purpose of modelling relationships between data is to try to predict how we can take more effective actions in future in support of some overall goal. These are real-world questions informed by real-world data; and when they have answers at all, they have real-world answers. To find those answers, we have to go to Model Land.

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Within Model Land, the assumptions that you make in your model are literally true. The model is the Model Land reality.

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Model Land is a wonderful place. In Model Land, because all of our assumptions are true, we can really make progress on understanding our models and how they work. We can make predictions. 

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Model Land is not necessarily a deterministic place where everything is known. In Model Land, there can still be uncertainty about model outcomes.

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One premise of this book is that unquantifiable uncertainties are important, are ubiquitous, are potentially accessible to us and should figure in our decision-making - but that to make use of them we must understand the limitations of our models, acknowledge their political context, escape from Model Land and construct predictive statements that are about the real world.

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You cannot avoid Model Land by working only with data. Data, that is, measured quantities, do not speak for themselves: they are given meaning only through the context and framing provided by models. Nor can you avoid Model Land by working with purely conceptual models, theorising about dice rolls or string theories without reference to real data. Good data and good conceptual understanding can, though, help us to escape from Model Land and make our results relevant once more in the real world.

Escaping from Model Land

Though Model Land is easy to enter, it is not so easy to leave. Having constructed a beautiful, internally consistent model and a set of analysis methods that describe the model in detail, it can be emotionally difficult to acknowledge that the initial assumptions on which the whole thing is built are not literally true."

Trechos retirados de "Escape from Model Land" de Erica Thompson.