Author here: happy to take comments or criticism
If any of the scenarios from the famous Imperial College model turn out to have been based on just-as-bad assumptions, would you be willing to write a follow up about that?
All models are wrong, but some are completely wrong
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Re: All models are wrong, but some are completely wrong
#32This is a good time for schools to go back to teaching about Ignorance:
https://www.nytimes.com/2015/08/24/opinion/the-case-for-teac...
Re: All models are wrong, but some are completely wrong
#33Author here: happy to take comments or criticism
The final point seems like a bunk attack. A lot of epidemiologists were upset at Silicon Valley data scientists and hedge fund quants who were putting together prediction models that disputed what the government was saying. Well, so far these models have been more accurate than the official ones.
As we should expect, because data scientists and quants are EXACTLY the people with the set of skills necessary to make such predictive models. At best, it's an ancillary skill for epidemiologist, and we've seen many cases in this pandemic where they have wielded these tools incorrectly.
Re: All models are wrong, but some are completely wrong
#34I fail to understand how you can reasonably model an unprecedented event in modern history. We have no data on how people will act under a weeks, even months long lockdown. Will they stay indoors and follow guidelines? Maybe. Will they watch their livelihoods get affected, their mental health deteriorate, or get careless over time and break quarantine? Maybe. We just don't know because we just don't have the data or…
On the contrary, epidemics are reasonably easy to model (at least at a basic level), and modelling them has a long academic tradition. As with all modelling, it all depends on what kind of detail you want to get out of your analysis/predictions. If you‘re looking at a simple population infection model, lockdown efficacy is just a factor affecting the contact rate among uninfected actors. You run multiple scenarios fo…
So many of the models I've seen focus on the efficacy of lockdowns in controlling the spread of the disease. Most treat the length of the lockdown as a mathematical variable - X week lockdown leads to Y infection rate.
My point is that we can't reasonably predict how people will behave in lockdown past a certain point. We've never had similar lockdowns in a world that was as globalized, as hyper connected as ours. You could go from 2 to 8 weeks of lockdowns if everyone was living in isolated villages a la 1918 Spanish Flu, but that's not our present world.
How do you model a situation where after 4 weeks of lockdowns, a social media post about food shortages goes viral, causing mass panic and breaking of quarantine?
We can't because we've never had a situation like this, or the tools for spreading (mis) information as we currently do.
Re: All models are wrong, but some are completely wrong
#35What I've noticed about models, or at least when people are talking about them or trying to prove a point about them, is that people forget models are a simplified version of a specific part of reality, much the same as models of airplanes or something. No matter how many variables you include, you can never capture the utterly massive and unpredictable amount of variables that exist in reality. But they're not suppo…
It’s funny, but when you point out that a bad analogy is actually pretty accurate if you actually know anything about the other concept , people don’t want to talk about it any more. We all know that hill climbing algorithms are often naive and sometimes hilariously wrong. Nobody will disagree with you about this, until you start talking about prioritizing work, and then everyone vigorously defends their favorite hil…
Can you elaborate on your preferred strategy?
Re: All models are wrong, but some are completely wrong
#36Author here: happy to take comments or criticism
Your entire post seems to be about #6. And therefore it's entirely wrong.
Given that "people with background in infectious diseases" have largely failed, as a group, to warn us in January-February about this pandemic (see this Twitter thread for a slew of concrete examples https://twitter.com/RokoMijicUK/status/1246509433145917443), my conclusion is that unless someone has a background in hard quantitative field (regardless of what that field is), that person should not be let anywhere near quantitative models.
Re: All models are wrong, but some are completely wrong
#37What I've noticed about models, or at least when people are talking about them or trying to prove a point about them, is that people forget models are a simplified version of a specific part of reality, much the same as models of airplanes or something. No matter how many variables you include, you can never capture the utterly massive and unpredictable amount of variables that exist in reality. But they're not suppo…
Humans have the tendency to think they understand something if they have a label on it. It helps them take decisions faster, whether it's good or bad.
If you tell people you don't know what causes their respiratory illness, they may think you are incompetent, and feel helpless.
If you tell them they have acute bronchitis, suddenly they feel empowered. It just means they have an inflammation of the bronchi. It says nothing of the direct cause, nor the indirect cause: a virus, tobacco, dust, air pollution or asthma. It says nothing about how bad it is. They don't even know really what it implies for their body or their treatment.
But now they think they know.
Call something a democracy, and nobody will check if it is.
Call something a privilege, and people will start to want it, working hard for it.
Models can be used to help your interact with the world. But they are also used to avoid interacting with the world: if you use them as labels, instead of comparing the model with reality, you get a shortcut to take decisions. It becomes a mere name to justify a decision process instead of a tool for the decision process.
Re: All models are wrong, but some are completely wrong
#38Regarding the "all models are wrong" maxim... Is that statement 100% true for the low-level models that physicists use and develop? In particular, I'm curious if quantum-physics models are 100% right, just not 100% precise.
I keep this distinction in mind: - Models are deliberate simplifications of reality, in order to guide thinking and otherwise pull in only important information - Formulations (formalizations) are encapsulation of principles into a mathematical framework While there is significant overlap, the two categories do not overlap 100%. I see formalizations of physics as the latter, and we use the former to help keep our und…
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Re: All models are wrong, but some are completely wrong
#39Reading the examples, where people jumped on a single mistake to discredit an entire report points to some sad conclusions: 1. Scientific literacy is super low in the general population. 2. Motivated reasoning is rampant. People will believe anything that enables them to do what they wanted to do anyway.
I think the author oversimplifies the problems with climate models. They've had numerous mistakes and extremely critical ones. Not least of which is that they haven't factored for Multi-decadal changes in cloud cover albedo. The climate system is incredibly complex and our models do not have good models for all the subsystems that make it up, including the oceanic oscillations like Atlantic Multi-decadal Oscillation, PDO, etc. We can't even predict many of these subsystems with any degree of accuracy so we are hopelessly inaccurate at the higher level.
Re: All models are wrong, but some are completely wrong
#40Regarding the "all models are wrong" maxim... Is that statement 100% true for the low-level models that physicists use and develop? In particular, I'm curious if quantum-physics models are 100% right, just not 100% precise.
Some predictions of Quantum Electrodynamics have been verified to within 1 part in 100,000,000.
https://en.wikipedia.org/wiki/Precision_tests_of_QED
However, we know it cannot be completely accurate, as it has no way of explaining gravity.