DrThe world as a kind of clock mechanism, at its core is simple and predictable – that was a recent idea in the 18th century. In such a world, science has provided a method of being able to see the future on the basis of data and laws – in principle with the required accuracy, as long as the quality of the data is correct. Great thinkers were proponents of this idea, such as Isaac Newton for example or John Stuart Mill, and the idea of a predictable world is undoubtedly a completely magical idea. However, today, some 300 years later, we know that this is a mistake. Because complex systems of the kind that unfortunately dominate the world operate completely differently.
Complex systems require unpleasant things like statistics, and tedious distinctions like those between correlation and causation. Infinite influences and factors interact in it. Some of them are sensitive to the smallest changes in parameters. All opaque cannot be properly reduced to manageable parts. The beautiful world of facts and certainty turns black and white into ugly gray tones of probability and error assessments.
When Model Predictions Fail
This is disappointing. The resulting frustration is still being felt today. One can recognize them, for example, in the accusation against the designers of epidemiology that their “model predictions” did not come true. Also in the demand to make the models “better”, to incorporate more parameters, more factors. This raises the hope that being able to accurately predict is something that can be achieved if you put in a little effort. One can leave statistics, the things that cannot be measured, behind and gain security – because who wants to make decisions based on unverified data?
One might try to admit that studying complex systems can hardly produce more classic guarantees than a bankruptcy sale. One can refuse to write down scientific results until science clearly “shows” that anything is all right. A more productive position is to acknowledge that our modern understanding of our complex world is also changing the approach to scientific findings. This is where the often-invoked difference these days between scenarios and predictions comes into play.
In the space of possible futures
When we have to make decisions in situations characterized by uncertainty and incomplete knowledge, usually the option is not simply to wait until we know more. This applies to severe disasters like equity deals. Instead, you replace your ignorance with assumptions and ponder what will follow from it. What would happen if everything was in your favor? What’s the worst that could happen? Then: What are the risks I am willing to take? In short, you collect scenarios and then search for a robust strategy that results in an acceptable outcome in the largest possible number of possible situations. There are no expectations and expectations required for this procedure. You operate in a space of potential futures. Needless to say, every effort is made to prevent the worst-case scenario from occurring.
Anyone who wants to criticize political decisions can do so constructively at this point. He could complain, for example, that he himself would have understood something different from the government with an acceptable outcome. Or that the decision-making processes were not sufficiently transparent. However, the criticism that some of the predictions of the individual model did not occur, or in general: that decisions were made in a state of uncertainty, does not help here with their systematic lack of information, but only increases – either negligently or deliberately – the already high level of dissatisfaction prevalence. The year.
This also applies to the fundamentally justified criticism of the empirical data case, which remains often unsatisfactory. If such a critique, as presented to the public by prominent authors of data in the DIVI record last week, is littered with errors and statistical errors, little is gained. Instead, it creates the impression that the credibility of science is being openly undermined in favor of personal vanity. A recent study from Canada showed how dangerous this is. Accordingly, anti-intellectualism plays an important role in the public’s response to the pandemic. Not feeding them after that should be important to all of us, also for future crises.