Interesting article, especially because I’m currently rereading some decision making material in the light of some LLM projects.
I think a very interesting part of your discussed setups is how the world model is defined in detail.
I see it as something that is already involved in our perception with some major restrictions, i.e., incomplete observability and some kind of biological hard-coded guidance system (“feelings”). This view still allows for a factored model, but it comes with different dependencies between the building blocks, i.e., between the values, beliefs, and the decision theory, that have a great impact on the system.
Do you mean with “locally consistent beliefs and values” in your last paragraph not necessarily consistent to every other belief and value the individual has?
The situation you describe in your first paragraph also nicely fits this framework of human decision making outlined in https://www.worldbank.org/en/publication/wdr2015 (highly recommend):> First, people make most judgments and most choices automatically, not deliberatively: we call this “thinking automatically.”> Second, how people act and think often depends on what others around them do and think: we call this “thinking socially.”> Third, individuals in a given society share a common perspective on making sense of the world around them and understanding themselves: we call this “thinking with mental models.”So two other strong „processes“ are at play before a mental model / decision theory can be leveraged (if we share the same definition here that these are the same). So this is much more complex to resolve and we maybe need those corrective actions (due to the restrictions mentioned above)?
This is a very interesting topic and I’m looking forward to more discussions.
Very interesting article! A big part of the outlined techniques I also kind of discovered by trial and error.
I recently also started to work more on my math skills and putting in the mathjax was getting very time consuming. To reduce that bottleneck I can recommend this small app: https://mathpix.com
PS: I stumbled once over this collection of proofs without words and they make great Anki cards to build up intuition: https://mathoverflow.net/questions/8846/proofs-without-words
A book that goes very much in that direction with small but impactful chapters is:
“Chop Wood Carry Water”
I really enjoyed (and as I’m now reminded of it, I will have a look at it again) and I guess you could like it too.
(I found it via https://fs.blog/reading-2019/ and the very good review got me interested in it.)
Out of curiosity I had to look at the FT chart with relative numbers (= seven-day rolling average of new cases (per million), by number of days since 0.1 average daily cases (per million) first recorded):
It looks like the next weeks will show where the situation will go. The curve for some countries looks quite bumpy on the log scale.
I guess a deficit in quantitative reasoning is just one of the contributing factors.
Another contributing part, I keep thinking about a lot, is the role of social media during the pandemic. Social media is making money by engaging people. The longer people are on your platform, the more data you can harvest and the more advertisements you can show them, resulting in more revenues. And the more data you have, the better you can target the ads, and so on. The best way to drive up engagement is to promote controversial posts (the more extreme the better, you like it and share it or you don't like it and talk about it). This leads to filter bubbles. By knowing the main orientation of those filter bubbles it is easy to drive up engagement by showing each filter bubble some posts that are aligned to their views (maybe even increasing to more extreme topics and standpoints).
Of course this is not beneficial for the society as a whole and it drives division and is not improving a culture of open discussion, but it is currently a great and more or less unregulated money making machine.
Pair that with a very capitalistic society without a lot of social security nets and a situation that brings people to the edge (i.e., pandemic) and the outlined mechanics from above is running even faster/better (isolated people, increase of fear of the unknown, mental health issues, etc.).
(And hey, you can even use this technology (unofficially) to harm other parties and cause a lot of damage with a fraction of the cost of traditional operations.)
And in the end, the outlined aspect comes down to misaligned incentives.
(Note: Maybe I was reading recently too much about misinformation using natural language processing.)
A nice graphical guide on COVID-19 vaccines: https://www.nature.com/articles/d41586-020-01221-y
To grow the virus and inactivate them by chemical reagents is the standard setup for (older) influenza vaccine systems that are egg- or cell-culture-based. From such a setup whole-virus- or subunit vaccines can be derived.
Interesting COVID-19 vaccine development landscape publication in Nature.
To produce a vaccine, you will need at least:
In biopharmaceutical production you have two kind of extremes in the facility design:
Of course, everything between those two designs is possible. Both types can be designed to produce multiple products which is referred to as a "multi-product facility". Depending on the automation grade you will need more or less staff with more or less training and experience. For the ramp-up of the facility and for ongoing troubleshooting you will need (highly) educated stuff.
The vaccine production systems can be roughly classified into these variants:
These outlined systems are quite different, and, therefore, the facilities will look different. However, a well-designed multi-product facility should be able to cover a wide range of the possible vaccine types because basic fluid handling and a lot of other steps are similar.
The most similar historical equivalent I can think of is the penicillin production, although there the circumstance where quite different.
Vaccines are one of the most cost-effective medical preventive measures but usually the margins are thin. This is why the investments in such products has not seen the levels of treatments of civilization diseases, e.g., cancer or diabetes.
Edit: Added large scale process to the points at the beginning.
No direct prediction from my side but a link to a report:
The full PDF report (linked on the website) has on page 15 a overview of possible outcomes that could be a basis for discussion.