The Modelers' Response
The Modelers' Response is a list of common justifications used by modelers to defend flawed statistical models, often ignoring the limits of their tools. Recognizing this pattern helps avoid the iatrogenics (harm) caused by overreliance on quantitative models.
THE IDEA
David Freedman, RIP
First, I need to pay homage to someone to whom knowledge has a large debt. The late Berkeley statistician David Freedman, who perhaps better than anyone uncovered the defects of statistical knowledge, and the inapplicability of some of the methods, sent me a farewell gift. He was supposed to be present at the meeting of the American Statistical Association, but canceled because of illness. But he prepared me for the meeting, with a message that changed the course of the Black Swan idea: be prepared; they will provide you with a certain set of self-serving arguments and you need to respond to them. The arguments were listed in his book in a section called “The Modelers’ Response.” I list most of them below.
The Modelers’ Response: We know all that. Nothing is perfect. The assumptions are reasonable. The assumptions don’t matter. The assumptions are conservative. You can’t prove the assumptions are wrong. We’re only doing what everybody else does. The decision-maker has to be better off with us than without us. The models aren’t totally useless. You have to do the best you can with the data. You have to make assumptions in order to make progress. You have to give the models the benefit of the doubt. Where’s the harm?
This gave me the idea of using the approach “This is where your tools work,” instead of the “This is wrong” approach I was using before. The change in style is what earned me the hugs and supply of Diet Coke and helped me get my message across. David’s comments also inspired me to focus more on iatrogenics, harm caused by the need to use quantitative models.
David Freedman passed away a few weeks after the meeting.* Thank you, David. You were there when the Black Swan needed you. May you and your memory rest in peace.
When to use it
A policy maker relies on economic models that assume perfect rationality, ignoring real-world complexity and dismissing criticism with 'the decision-maker has to be better off with us.'
A scientist uses a statistical model with untestable assumptions and brushes aside concerns by saying 'the assumptions are reasonable' and 'you can’t prove the assumptions are wrong.'
An investment bank uses complex risk models that ignore tail risks, and when challenged, the modelers respond with 'the models aren’t totally useless' and 'where’s the harm?' leading to catastrophic losses.
FROM THEORY TO EVERYDAY LIFE
In practice
Common Modelers' Responses
A list of justifications used by modelers to defend flawed models: 'We know all that. Nothing is perfect. The assumptions are reasonable. The assumptions don’t matter. The assumptions are conservative. You can’t prove the assumptions are wrong. We’re only doing what everybody else does. The decision-maker has to be better off with us than without us. The models aren’t totally useless. You have to do the best you can with the data. You have to make assumptions in order to make progress. You have to give the models the benefit of the doubt. Where’s the harm?'
Financial Modeler's Defense
A financial analyst uses a complex model to predict stock prices, but when questioned about its assumptions of market efficiency and normal distributions, they respond with 'You have to make assumptions to make progress' and 'The assumptions are conservative,' ignoring the model's failure during tail events.
A MOMENT TO REFLECT
Put it to work.
When have you used a model's assumptions as a justification for ignoring its limitations?
How can you identify when modelers are using the 'Modelers' Response' to defend poor models?
What harm could arise from overreliance on quantitative models in your field?
CONTINUE THE CONVERSATION
Source texts
- The Black Swan The Impact of the Highly Improbable With a new section On Robustness and Fragility (Nassim Nicholas Taleb)