Last month I described how computational models were used as more than fables in many areas of applied science, including engineering and precision medicine [‘Models as fables’ on March 16th, 2016]. When people need to make decisions with socioeconomic and, or personal costs, based on the predictions from these models, then the models need to be credible. Credibility is like beauty, it is in the eye of the beholder. It is a challenging problem to convince decision-makers, who are often not expert in the technology or modelling techniques, that the predictions are reliable and accurate. After all, a model that is reliable and accurate but in which decision-makers have no confidence is almost useless. In my research we are interested in the credibility of computational mechanics models that are used to optimise the design of load-bearing structures, whether it is the frame of a building, the wing of an aircraft or a hip prosthesis. We have techniques that allow us to characterise maps of strain using feature vectors [see my post entitled ‘Recognising strain‘ on October 28th, 2015] and then to compare the ‘distances’ between the vectors representing the predictions and measurements. If the predicted map of strain is an perfect representation of the map measured in a physical prototype, then this ‘distance’ will be zero. Of course, this never happens because there is noise in the measured data and our models are never perfect because they contain simplifying assumptions that make the modelling viable. The difficult question is how much difference is acceptable between the predictions and measurements . The public expect certainty with respect to the performance of an engineering structure whereas engineers know that there is always some uncertainty – we can reduce it but that costs money. Money for more sophisticated models, for more computational resources to execute the models, and for more and better quality measurements.
Thank you for this interesting post. I agree that the “eye of the beholder” has a much greater influence on decision making than one might think or admit, despite the engineer’s striving for laying a quantitative basis (such as the distance of feature vectors). In fact, decisions are often taken DESPITE the quantitative basis. Or to quote a Swiss National Councillor: “Politicians should have the freedom of decision”.
The degree to which decision makers or policy makers use scientific or social science research in their decisions is a field of study in and of itself. Having a willingness, desire, or propensity to even consider research precedes the ability to have confidence or trust in any particular model at hand. So, the problem is more complex, at least in the U.S. where so many people are out spokenly anti-science, incuding members of Congress and persons currently seeking to be President. Also, in the U.S., even among scientifically open persons, engineering itself can get a bad rap, particularly among environmentalists. The U.S. Army Corps of Engineers, for example, ruined many streams, rivers and natural habits by over-zealous and often un-necessary channelization and use of chemicals. Using biased reserach the Corps supported the massive blunder of continuing to pursue the “Cross Florida Barge Canal” project. Many projects they have recommended and completed over time are considered to be based on faulty review, faulty models, and faulty evidence, resulting in various harms. Some have even faulted the, with the devastation of Katrina in New Orleans.
faulted them with
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