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What this article misses is that simple models of complex systems in science are most useful for understanding the dynamics of phenomena, not for making accurat
by Thrymr 7y ago
What this article misses is that simple models of complex systems in science are most useful for understanding the dynamics of phenomena, not for making accurate quantitative predictions. This is not a model of a mass accelerating in a vacuum where Newton's laws are sufficient to a high degree of accuracy, or even a numerical model of the aerodynamics of an airplane, where the physics are well understood but there are far too many particles to solve analytically. This is a parameterized model for the behavior of millions of people, each one's reaction to exposure to viral particles, legal and social norms, personal and economic situations, and so on. Of course we are not going to be able to predict the future of an unprecedented event like this quantitatively. It's not just that data are hard to obtain accurately; the models themselves are so simplified that they can't capture much of the important dynamics going on.
- jhayward 7y agoAgree. I think a better exposition on COVID-19 models is Zenep Tufecki's article in The Atlantic [1]. TLDR: They're more directional roadmaps, showing you which possible outcome paths you need to prune off the tree of outcomes, not exact numerical predictions. [1] https://www.theatlantic.com/technology/archive/2020/04/coronavirus-models-arent-supposed-be-right/609271/ https://www.theatlantic.com/technology/archive/2020/04/coron...
- dekhn 7y agoIn a sense, much of this work is similar to A* search and reinforcement learning. You have a limited view of the world, the responses to your actions are not directly causal, but if you can use the data you have to make reasoned decisions on what parts of the (extremely large) action space are unlikely to pay off, you can avoid wasting resources on them.
- sesuximo 7y agoIt's hard to use a roadmap with this much variance. Is there anything that can be said with certainty about covid
- jhayward 7y agoIt's not the variance, it's the worst-case analysis that is helpful. You take decisions that lower the worst-case outcome. Sure, there is lots that can be said about COVID-19. Epidemiology is a fairly well-studied field, and we have experience with aerosol-transmitted respiratory viruses. We have a pretty good feel for the transmissibility, getting better all the time. Pretty good feel for the disease course, and getting more specific about the different phases of treatment all the time. Pretty good feel for the R0 in many conditions, and the serial interval, and so on. "Is there anything that can be said with certainty" is a dangerous position to be pushing on the public. It is the very thing that autocrats and dictators try to induce in populations through propaganda and disinformation, so that reason and self-help are surrendered. When there is no way to know truth, truth is what the Leader says it is. And we don't want to go there.
- sesuximo 7y agoI don't think it makes sense to shoot for the least bad worst case. How about the best average case.
- DonHopkins 7y agoIt's like trying to calculate the Butterfly Effect during winter at the Reserva de la Biosfera Mariposa Monarca (Monarch Butterfly Biosphere Reserve). https://en.wikipedia.org/wiki/Butterfly_effect https://en.wikipedia.org/wiki/Butterfly_effect https://en.wikipedia.org/wiki/Monarch_Butterfly_Biosphere_Reserve https://en.wikipedia.org/wiki/Monarch_Butterfly_Biosphere_Re...
- sgt101 7y agoI don't think it is - I think you can write "x is like y" but you have to understand and explain why. Otherwise I can say "a hippo is like a bright blue sky" and then claim that I am right! I don't think that this is like modelling the impact of a butterfly on storms - I think this is more like modelling the diffusion of a bottle of dye in a swimming pool.
- deadmetheny 7y agoGrandparent is somewhat nonsensical, but I think what they were trying to say is that since human society is a highly dynamical system, it's not really possible to have a highly accurate model to represent how COVID-19 is going to spread. The best we can do is use naive models and plan around worst-case scenarios.
- vrinstan 7y agoWe did use naive models and turned off half the planet. We should learn not to do that anymore. And beside that, we should hold our politicians accountable for these decisions.
- sgt101 7y agoBut we know really quite accurately how people en-mass move and associate prior to intervention. The proper models are using this information.
- sgt101 7y ago>the models themselves are so simplified that they can't capture much of the important dynamics going on That isn't my (outsider) understanding, could you provide a reference? By reference I mean a recent survey that demonstrates the specific limitations? My understanding of the Imperial College model is that it uses a 30m*30m grid of the world and the expected people in the world and various additions to simulate schools and workplaces. Maybe you know better?
- guscost 7y ago30m is quite a lot bigger than the Planck length, and you can’t predict human behavior by playing The Sims.
- dekhn 7y agoyou can predict human behavior with big data and linear models/embeddings with a few billion parameters. This works so unbelievably well, I expect that eventually health modelling folks will do this, provided they have enough high quality data.
- used_noise 7y agoAlice gives Bob a number, x, and tells Bob to calculate f(x), where f(x) is defined as the partial function with goedel index x applied to input x. She says that if or when Bob finishes, he should come bring her his answer and they'll get drinks together. Given parameter x, do Bob and Alice ever get drinks together?
- sgt101 7y agoWell, shit yus, because Bob has a dominant option to go and get Alice to have a drink if he has an answer or not. If Bob gets the answer Alice will be impressed. If he doesn't Alice like him anyway - so what's the odds? It's undecided what happens next.
- dfsegoat 7y agoActually, it seems to me that more or less, that is exactly how some of the network based contact tracing models work: "Therefore, characteristics of mixing networks—and how these deviate from the random-mixing norm—have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures." [1] And as a follow-on, that is why there is so much discussion about using mobile apps for contact tracing - it builds the network for you, passively. 1 - https://royalsocietypublishing.org/doi/10.1098/rsif.2005.0051 https://royalsocietypublishing.org/doi/10.1098/rsif.2005.005...
- deleted 7y ago[deleted]
- mikorym 7y agoThere is another point too, which also to some extend goes against your point—that we model epidemiology in standard ways, and that in order to use those standard ways we need the parameters in the set of differential equations. And these do make predictions based on the equilibria. I am not saying they solve everything, but they are routinely used to for example calculate how many people need to be vaccinated to stop outbreaks of measles. The article does mention most of the parameters; but we don't know what values to assign to those parameters. The last scientific writing that I was reading speculated about the R_0, but I believe we are still not sure about that. In any case, the point of the parameters is to find R_0, so we cannot expect to have an accurate R_0 without them. We also don't know mortality rates in a general population (look at Italy vs. China, when specifying to age groups). But anyway, all I wanted to say is that our models are simplistic, but surprisingly useful.
- thereisnospork 7y agoThe point of the parent is you can't actually find R_0 because R_0 depends on the behaviors of millions of people which cumulatively are based on billions of factors. R_0 isn't a constant, it's an equation. Mathematically, unless you get really lucky or have a very idealized system, the standard models underfit the actual system. The models can still be useful though, e.g. 'we must reduce r0 below r_critical to eliminate spontaneous spreading given herd immunity %'s' is worth knowing.
- jvanderbot 7y agoThis is not as absolute as is being presented. We can speak about spreads and aggregate behavioral patterns just fine if we take times to infinity and zoom out far enough. The next few weeks or localized transmisson (which is "impossible" to model) are problematic, agreed.
- jacobr1 7y agoActuaries do a pretty good job predicting things at a population level, where many factors average.
- watwut 7y ago
- m463 7y agoWhat you said reminded me of Richard Feynman when he worked on the connection machine: http://longnow.org/essays/richard-feynman-connection-machine/ http://longnow.org/essays/richard-feynman-connection-machine...
- IAmEveryone 7y agoThe weather has far more moving parts than people (in aggregate). It’s the canonical example of chaos (theory). Until about a decade ago, assuming today’s weather is also tomorrow’s was still outperforming our prediction models. But recently, we have become quite good at weather predictions 7 days out, and even for 14 days are now significantly better than chance. Disease models can be useful even in the absence of predictive power. Getting any one of a few dozens assumption wrong can throw your prediction off by orders of magnitude. But it still allows you to, for example, explore how sensitive the epidemic is to different policy alternatives. Besides: what are the alternatives? As long as you do anything, that action is based on some “model” of how the world works, how people behave, what value you assign to competing objectives. Writing that model-in-your-Head down or implementing it in software is strictly better than not doing so: it forces you to be explicit about the assumptions you make, it allows people to cooperate, it forces them to be specific in any criticism, it is a far better tool to communicate your reasoning to people affected by it, it deals in real numbers and will quickly expose any significant oversights you might otherwise miss, it’s accuracy can be measured and thereby improved...
- wahern 7y agoWell said, but it should be noted that making quantitative predictions based on simple models (and scattershot data) of complex systems is FiveThirtyEight's bailiwick and Nate Silver's claim to fame. The subtext of the article is that the problem is so difficult even they are stumped.
- tomrod 7y agoThey also would be limited from privileged data, like how many ICU beds are occupied by Covid-positive patients. The data requirements to pull off are high.