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No, senator, science cant do away with models

Enlarge / Senator John Cornyn (center, black suit) takes a photo with the 2017 astronaut class.NASA ..

By admin , in Tech , at April 13, 2020

Enlarge / Senator John Cornyn (center, black suit) takes a photo with the 2017 astronaut class.NASA Johnson

On Friday, Texas Senator John Cornyn took to Twitter with some advice for scientists: models aren't part of the scientific method. Scientists have responded with a mix of bafflement and exasperation. And Cornyns misconception is common enough—and important enough—that its worth exploring.

Cornyns beef with models echoes a talking point often brought up by people who want to reject inconvenient conclusions of systems sciences. In reality, “you can make a model say anything you want” is about as potent an argument as “all swans are white.” The latter is either a disingenuous argument, or you have an embarrassingly limited familiarity with swans.

Models aren't perfect. They can generate inaccurate predictions. They can generate highly uncertain predictions when the science is uncertain. And some models can be genuinely bad, producing useless and poorly supported predictions. But the idea that models arent central to science is deeply and profoundly wrong. Its true that the criticism is usually centered on mathematical simulations, but these are just one type of model on a spectrum—and there is no science without models.

Whats a model to do?

There's something fundamental to scientific thinking—and indeed most of the things we navigate in daily life: the conceptual model. This is the image that exists in your head of how a thing works. Whether studying a bacterium or microwaving a burrito, you refer to your conceptual model to get what youre looking for. Conceptual models can be extremely simplistic (turn key, engine starts) or extremely detailed (working knowledge of every component in your cars ignition system), but theyre useful either way.

As science is a knowledge-seeking endeavor, it revolves around building ever-better conceptual models. While the interplay between model and data can take many forms, most of us learn a sort of laboratory-focused scientific method that consists of hypothesis, experiment, data, and revised hypothesis.

In a now-famous lecture, quantum physicist Richard Feynman similarly described to his students the process of discovering a new law of physics: “First, we guess it. Then we compute the consequences of the guess to see what… it would imply. And then we compare those computation results to nature… If it disagrees with experiment, its wrong. In that simple statement is the key to science.”

In order to “compute the consequences of the guess,” one needs a model. For some phenomena, a good conceptual model will suffice. For example, one of the bedrock principles taught to young geologists is T.C. Chamberlins “method of multiple working hypotheses." He advised all geologists in the field to keep more than one hypothesis—built out into full conceptual models—in mind when walking around making observations.

That way, instead of simply tallying up all the observations that are consistent with your favored hypothesis, the data can more objectively highlight the one that is closer to reality. The more detailed your conceptual model, the easier it is for an observation to show that it is incorrect. If you know where you expect a certain rock layer to appear and its not there, theres a problem with your hypothesis.

There is math involved

But at some point, the system being studied becomes too complex for a human to “compute the consequences” in their own head. Enter the mathematical model. This can be as simple as a single equation solved in a spreadsheet or as complex as a multi-layered global simulation requiring supercomputer time to run.

And this is where the modelers adage, coined by George E.P. Box, comes in: “All models are wrong, but some are useful.” Any mathematical model is necessarily a simplification of reality and is thus unlikely to be complete and perfect in every possible way. But perfection is not its job. Its job is to be more useful than no model.

Consider an example from a science that generates few partisan arguments: hydrogeology. Imagine that a leak has been discovered in a storage tank below a gas station. The water table is close enough to the surface here that gasoline has contaminated the groundwater. That contamination needs to be mapped out to see how far it has traveled and (ideally) to facilitate a cleanup.

If money and effort was no object, you could drill a thousand monitoring wells in a grid to find out where it went. Obviously, no one does this. Instead, you could drill three wells close to the tank, determining the characteristics of the soil or bedrock, the direction of groundwater flow, and the concentration of contaminants near the source. That information can be plugged into a groundwater model simple enough to run on your laptop, simulating likely flow rates, chemical reactions, and microbial activity breaking down the contaminants and so on, spitting out the probable location and extent of contamination. Thats simply too much math to do all in your head, but we can quantify the relevant physics and chemistry and let the computer do the heavy lifting.

A truly perfect model prediction would more or less require knowing the position of every sand grain and every rock fracture beneath the station. But a simplified model can generate a helpful hypothesis that can easily be tested with just a few more monitoring wells—certainly more effective than drilling on a hunch.

Dont shoot the modeler

Of course, Senator Cornyn probably didnt have groundwater models in mind. The tweet was prompted by work with epidemiological models projecting the effects of COVID-19 in the United States. Recent modeling incorporating the social distancing, testing, and treatment measures so far employed is projecting fewer deaths than earlier projections did. Instead of welcoming this sign of progress, some have inexplicably attacked the models, claiming these downward revisions show earlier warnings exaggerated the threat and led to excessive economic impacts.

There is a blindingly obvious fact being ignored in that argument: earlier projections showed what would happen if we didnt adopt a strong response (as well as other scenarios), while new projections show where our cRead More – Source

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