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>100% it can be modelled somehow in theory. The entire premise of modelling something rests on the fact that the behaviour of what you're trying to model in th

>100% it can be modelled somehow in theory. The entire premise of modelling something rests on the fact that the behaviour of what you're trying to model in the future depends on its behaviour in the past. This is not true for all systems, it's not even true for most systems. John Kay differentiates between 'resolvable uncertainty' and 'radical uncertainty'. Modelling a volcano eruption is resolvable because it's a reasonably predictable system and you can make confident stochastic claims. Complex human systems are chaotic, have 'unknown unknowns', are simply subject to chance or non-linear behaviour that completely throws any prediction off. You cannot model something that is subject to forces you cannot even know at the point of making the model, and this is true for any complex human system pretty much. Modelling Covid as if we have access to Hari Seldon's psycho-history was always a terrible idea.

pas 5y ago
What? We have to model human health. Find good proxies for it (non invasive scans, bloodwork, PCR, rapid antigen test, whole genome sequencing, anamnesis [medical history]) and there's ample to predict. Of course there are better and worse signals to use.
- Barrin92 5y ago
  >We have to model human health no, the correct behavior under uncertainty isn't modelling, it's something akin to Taleb's notion of antifragility or robustness. The correct response to a pandemic isn't some sort of Hari Seldon psycho-history which, as the article points out, is futile. The right response is building systems so responsive they can crush a pandemic before it gets to that stage.
  - pas 5y ago
    Antifragility depends on knowing how to respond so that next time you'll be less fragile for the "same" stimuli. For that we need better public health measures, those depend on modeling, vaccine development, etc. There's no escaping understanding human health. It underpins and informs our responses. (Which kind of viruses are likely to pose a real threat, monitoring those threats, evaluating them, models, predictions, data, etc.) I'm not talking about one big Seldon-esque equation, but about the public/global health system we already have, the whole of medical research, and so on. These toy classifiers trained on COVID scans are in the same class as Tesla's dangerous "autopilot", because the system is too limited, it doesn't understand context, it lacks a world model. (Tesla's lack epistemic convergence and stability for object detection, ie. it lacks the understanding that cars, roads, obstacles don't flicker in and out of existence, nor do they change from frame to frame. Similarly a "medical AI" needs to understand that medical imaging artifacts are unlikely to be good proxies for real diagnosis, so it needs to be able to abstract away from the image, process away the artifacts and recording environment differences, etc.)

pas 5y ago

What? We have to model human health. Find good proxies for it (non invasive scans, bloodwork, PCR, rapid antigen test, whole genome sequencing, anamnesis [medical history]) and there's ample to predict. Of course there are better and worse signals to use.

Barrin92 5y ago
>We have to model human health no, the correct behavior under uncertainty isn't modelling, it's something akin to Taleb's notion of antifragility or robustness. The correct response to a pandemic isn't some sort of Hari Seldon psycho-history which, as the article points out, is futile. The right response is building systems so responsive they can crush a pandemic before it gets to that stage.
- pas 5y ago
  Antifragility depends on knowing how to respond so that next time you'll be less fragile for the "same" stimuli. For that we need better public health measures, those depend on modeling, vaccine development, etc. There's no escaping understanding human health. It underpins and informs our responses. (Which kind of viruses are likely to pose a real threat, monitoring those threats, evaluating them, models, predictions, data, etc.) I'm not talking about one big Seldon-esque equation, but about the public/global health system we already have, the whole of medical research, and so on. These toy classifiers trained on COVID scans are in the same class as Tesla's dangerous "autopilot", because the system is too limited, it doesn't understand context, it lacks a world model. (Tesla's lack epistemic convergence and stability for object detection, ie. it lacks the understanding that cars, roads, obstacles don't flicker in and out of existence, nor do they change from frame to frame. Similarly a "medical AI" needs to understand that medical imaging artifacts are unlikely to be good proxies for real diagnosis, so it needs to be able to abstract away from the image, process away the artifacts and recording environment differences, etc.)