A model world

[n2doc]

In economics, climate science and public health, computer models help us decide how to act. But can we trust them?

by Jon Turney


Here’s a simple recipe for doing science. Find a plausible theory for how some bits of the world behave, make predictions, test them experimentally. If the results fit the predictions, then the theory might describe what’s really going on. If not, you need to think again. Scientific work is vastly diverse and full of fascinating complexities. Still, the recipe captures crucial features of how most of it has been done for the past few hundred years.

Now, however, there is a new ingredient. Computer simulation, only a few decades old, is transforming scientific projects as mind-bending as plotting the evolution of the cosmos, and as mundane as predicting traffic snarl-ups. What should we make of this scientific nouvelle cuisine? While it is related to experiment, all the action is in silico — not in the world, or even the lab. It might involve theory, transformed into equations, then computer code. Or it might just incorporate some rough approximations, which are good enough to get by with. Made digestible, the results affect us all.

As computer modelling has become essential to more and more areas of science, it has also become at least a partial guide to headline-grabbing policy issues, from flood control and the conserving of fish stocks, to climate change and — heaven help us — the economy. But do politicians and officials understand the limits of what these models can do? Are they all as good, or as bad, as each other? If not, how can we tell which is which?

Modelling is an old word in science, and the old uses remain. It can mean a way of thinking grounded in analogy — electricity as a fluid that flows, an atom as a miniature solar system. Or it can be more like the child’s toy sense of model: an actual physical model of something that serves as an aid to thought. Recall James Watson in 1953 using first cardboard, then brass templates cut in the shape of the four bases in DNA so that he could shuffle them around and consider how they might fit together in what emerged as the double-helix model of the genetic material.

more

http://aeon.co/magazine/world-views/should-we-trust-scientific-models-to-tell-us-what-to-do/

[jakeXT]

1. They just found their air resistence error in FIFA 14

When the soccer video game FIFA 14 went on sale this past fall, it boasted a ball that, at long last, could sail smartly through the air. In earlier versions of the popular game, the ball sometimes became a bit “floaty,” soaring along an unrealistically linear path.

Last year a team of engineers and animators vowed to get to the bottom of the problem. After an intense audit of all the projectile physics code in the game, they found the problem: their drag coefficient was wrong.

Engineers use the drag coefficient to model air resistance, which affects the speed and trajectory of an object in flight. “The ball moves at its fastest velocity when it comes right off the foot, and air resistance immediately slows it down until it reaches its maximum height,” says John Eric Goff, a physicist at Lynchburg College and author of Gold Medal Physics: The Science of Sports. “The ball should then pick up speed on its way down.”

In previous FIFA versions the ball violated the laws of physics, accelerating and decelerating at a set rate unaffected by its initial velocity. “So if the ball was moving at 30 or 50 miles per hour, it was going to slow at the same rate as if it were moving at five miles per hour,” says Aaron McHardy, a senior gameplay producer at EA Sports, the company that produces the FIFA franchise.

http://www.scientificamerican.com/article.cfm?id=fifa-physics-how-a-video-game-figured-out-air-resistance

[longship]

2. The final decider (sorry Dubya) is Mother Nature.

The universe is the decider. All models that do not abide by that, need correction.

And on it goes, ad infinitum.

[caraher]

3. I love the parts about models in economics

Tony Lawson, an economic theorist at the University of Cambridge, argues more forcefully that the whole enterprise of mathematical description is so flawed that economics would be improved if it found other ways of analysing the system. In other words, no models at all might be better than the current models.