'Mathematical Truth' Leads To Wrong Scientific Predictions

Many scholars assume that more detailed models produce better estimates and predictions because they are closer to reality. But our new research, published in Science Advance,s suggests they may have the opposite effect, Prof. Arnald Puy wrote for The Conversation.

Photo Insert: Puy said that uncertainties increase with every model upgrade, making the output fuzzier at every step of the way even if the model itself hews closer to reality.

The assumption that “more detail is better” cuts across disciplinary fields. The ramifications are enormous. Universities get more and more powerful computers because they want to run bigger and bigger models, requiring an increasing amount of computing power.

Recently, the European Commission invested €8 billion euros (£6.9 billion) to create a very detailed simulation of the Earth (with humans), dubbed a “digital twin,” hoping to better address current social and ecological challenges.

“In our latest research, we show that the pursuit of ever more complex models as tools to produce more accurate estimates and predictions may not work. Based on statistical theory and mathematical experiments, we ran hundreds of thousands of models with different configurations and measured how uncertain their estimations are. We discovered that more complex models tended to produce more uncertain estimates. This is because new parameters and mechanisms are added,” Puy, an associate professor in social and environmental uncertainties at the University of Birmingham, argued.

Puy said that uncertainties increase with every model upgrade, making the output fuzzier at every step of the way even if the model itself hews closer to reality.

This affects all models that do not have ample validation or training data against which to check the accuracy of their output, like global models of climate change, hydrology (water flow), food production and epidemiology alike, as well as all models predicting future impacts.

One model in 2009, called the Google Flu Trends based on 50 million queries wasn’t able to predict the 2009 swine flu outbreak. Another example is global hydrological models, which track how and where water moves and is stored.

We have shown that estimates of the amount of water used in irrigation produced by eight global hydrological models can be calculated with a single parameter only - the extent of the irrigated area.

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