With so many people freaking out about computer models, this seems like a good time for some perspective!
Here is JG’s Comment of the Day on the post, “Saturday Ethics Warm-Up, 4/4/2020: Letting The Perfect Be The Enemy Of The Good, And Other Blunders,” (Item #2):
I’m a math major from the late 60s so I do love numbers but wasn’t subjected to subsequent generations of educators preaching the genius of computer generated models (CGM).
My first job after college was in the research department of a well-known consulting company. One of the whiz kid consultants from Stanford was all about computer models (this is when we were still using teletypes to communicate with the mainframe).
I’ll never forget how impressed I was with his modeling and he, very wisely, cautioned me that it was difficult to remove personal bias from a computer model. He told me that our brains work more efficiently than any computer and, when faced with a problem that may require analyzing huge data series (i.e. perfect project for a computer), we, with our human brains, already have a “gut” conclusion which creeps into the modeling and very well may influence its neutrality. That piece of advice has stayed with me lo these 50 years and makes me skeptical about the reliability of any computer-generated model.The discrepancy that we are observing in the virus models is remarkable because the medical field has had for years the benefit of the most advanced statisticians who are specifically trained in recognizing the nature of different types of medical data and how differently they behave which allows for generally pretty accurate results. This is one case where the data has not been available and, thus, garbage-in, garbage-out.
Now, to my main point: climate science models are much less reliable because there is not an army of statisticians, thus far, trained specifically in the analysis of climate-associated data. My understanding about many of the models that support the IPCC reports is that there are numerous variables for which there is not an adequate time series available, so they’re left out of the model all together. Thus, we don’t even have garbage-in to criticize; rather, we have half-baked results kind of like baking a cake and leaving out half of the ingredients.