How do mathematical models help predict the future? - with Erica Thompson
ฝัง
- เผยแพร่เมื่อ 16 มิ.ย. 2024
- How do mathematical models help us make predictions about what will happen? And what happens when those models are biased towards a particular view of the world?
Buy Erica's book 'Escape from Model Land' here: geni.us/8XxJ
Watch the Q&A for this event here: • Q&A: How do mathematic...
This talk was recorded at the Ri on 20 June 2023.
Mathematical models have shaped our world and continue to be at the centre of everything we do. They became headline news as we tackled the COVID-19 pandemic, and are helping us to regulate an increasingly volatile economy and navigate the uncertainties of climate change.
In this talk, policy and models expert Erica Thompson explains the validity of the models we use: what they are, how they work, and the disastrous consequences when the makers and interpreters of models get things wrong.
Erica Thompson is a senior policy fellow at the London School of Economics Data Science Institute, a fellow of the London Mathematical Laboratory and an honorary senior research fellow at UCL's Department of Science, Technology, Engineering and Public Policy. With a PhD from Imperial College, Erica works on the use of mathematical modelling to support real-world decisions, specifically on the ethics of modelling and simulation. She has recently worked on the limitations of models of COVID-19 spread, humanitarian crises, and climate change. Erica's previous work includes the UK Department of for Energy and Climate Change's Global Calculator project, where she provided the climate science information. Erica lives in West Wales, and is reducing her own ecological footprint to a "One Planet" level by not travelling to conferences and finding other ways to reach people.
--
A very special thank you to our Patreon supporters who help make these videos happen, especially:
modsiw, Anton Ragin, Edward Unthank, Robert L Winer, Andy Carpenter, William Hudson
Don McLaughlin, efkinel lo, Martin Paull, Ben Wynne-Simmons, Ivo Danihelka, Kevin Winoto, Jonathan Killin, Stephan Giersche, William Billy Robillard, Jeffrey Schweitzer, Frances Dunne, jonas.app, Tim Karr, Alan Latteri, David Crowner, Matt Townsend, THOMAS N TAMADA, Andrew McGhee, Paul Brown, David Schick, Dave Ostler, Osian Gwyn Williams, David Lindo, Roger Baker, Rebecca Pan
--
The Ri is on Patreon: / theroyalinstitution
and Twitter: / ri_science
and Facebook: / royalinstitution
and TikTok: / ri_science
Listen to the Ri podcast: anchor.fm/ri-science-podcast
Our editorial policy: www.rigb.org/editing-ri-talks...
Subscribe for the latest science videos: bit.ly/RiNewsletter
Product links on this page may be affiliate links which means it won't cost you any extra but we may earn a small commission if you decide to purchase through the link. - วิทยาศาสตร์และเทคโนโลยี
![[Full Episode] MasterChef Junior Thailand มาสเตอร์เชฟ จูเนียร์ ประเทศไทย Season 3 Episode 3](http://i.ytimg.com/vi/A1XXYbV048k/mqdefault.jpg)
Thinking of Model as a Compass, giving us a sense of direction, what a beautiful insight! Thanks for sharing 🙏
I work as an operational researcher and you have captured very succinctly the challenges around modelling and simulation. Data science is becoming more and more powerful, but modelling chaotic systems will always need assumptions and appropriate simplification that may not be clear from the data alone
I have never seen any evidence of anybody or thing predicting the future.
@@jhonadam1420Like Einstein predicting Starlight gravitational lensing blackhole. Darwin prediction of transitional fossils. Newton predicting the third planet
@@jhonadam1420Even TH-cam algorithm predicting what you would click next
She clearly is not the most engaging speaker but as a scientist myself I found her use of the term ModelLand very insightful. We are living in reality not in Model Land where assumptions dominate your models behaviour. Far too often I see scientists in my field that just do modeling and trust their results without doubting their assumptions. Great talk I will get a book as well
seeing few of the people doing these talks are professional teachers, it's not surprising she's not as engaging as a professional speaker.
I found the speaker engaging - clear, precise and no fluffy bits (ignoring the catty pun) put in to distract from the message. One of the few that I felt I wanted to listen to the end. :)
Blimey, I thought she was great!
How is she not engaging. She was direct, which is a good thing.
Obviously I scientist only able to talk to other scientists lol. She was very accessible
Profoundly compelling and insightful presentation by Dr. Thompson! Thank You. Since everything humans know is ultimately a model, representation, or map and not reality itself, the implications of this highly engaging presentation go far beyond any specific model or category of models. Perhaps one of the implications is that as data scale so must also error scale? Speaks to the humility of acknowledging limits.
good and useful talk
YT put a climate change context on this, but not a covid warning. Of course the speaker doesn't deny that climate change is real, but this is too analytical and confuses the censors.
Thank you - very useful analogies (I'm a meteorologist).
That's cool. I'm vegan.
The graph at about 40 mins says it all. It is cost-based, so it gives the financial solution. This might involve biomass burning of forests for example. Truly a counter-productive strategy taking us back to the middle ages, but cost-effective apparently. Environmental accounting would not allow this type of modelling.
When I hear biomass, I understand it to mean you burn what you grow, or at least what otherwise you would throw away for other life forms to ingest. So it is either net zero (lower CO2 production) or it doesn't increase the CO2 you are already producing (CO2 production stable and not growing.
How could they expend so much effort on making this video, but get the deinterlacing so badly wrong on the computer graphics? It's such an integral part of the talk.
what a gifted speaker, i literally realized it was probably a dog the moment before the reveal and not a second sooner
The right goals are the ones that we don't regret asking the evil genie for.
T00 important its a prediction nearer to accuracy but to remove uncertainty the even & odds of squire root of 2;; _ 1 is the random of opposit to be as errors to be removed
🎉
Interlaced video is hard to watch. Please pick a non-interlaced encoding next time.
Great talk. Lots of effort went into this.
Fire models in Australia are based on expertise of fire scientists and not ecologists who understand the ecosystem to some extent- potentlally a disaster waiting to happen, but you can't get through the sociopolitical modelling wall. Another area people like Dr Thompson could spend some time on (other than the damn financial system) is dark energy. Listened to a very interesting talk by the Perimeter Institute and how dark energy moddelling is done and I'm sure they could use extra input.
9:00 -·- 23:15 This is what Π is all about
👀ツ
So many plan examples can you just explain the topic of modeling in detail with a point
she did. She's not here to explain the basics of what models are, but how they are used for this function.
I wish she had spent a bit more time in the lecture explaining how if your model doesn’t predict doomsday from climate change, you don't get funding for future models.
Look, look, another conspiracy theorist.
@carl7674 Feel free to prove me wrong, I guess.
@@APbbb
Conspiracy theorists have too much of a peremptory attitude to be proven wrong.
Studies say that it's IMPOSSIBLE, that a model can predict the future, or even the weather. As also been seen on this channel in a lecture. I wish you all a nice day.
what studies? Links plz
On the face of it, what you are saying is complete rubbish: of course models predict the future reasonably well! She says herself that all models are wrong (ie not perfect) but good ones are good at predicting the future. 33:40
Hell, I always use the models for hurricanes coming my way, here in PR. They give good, actionable advanced warning. Few if any deaths here from the storms themselves up to now. It's the slow response/repair after the storms that kill us here.
Did you listen to the lecture? A model is created to serve a specific purpose. Its value is determined by how well it achieves that purpose. There are plenty of models that are adding value in the prediction of future events within the scope defined by their purpose.
36 min in and I don’t understand if anything has been said. Look at data take model take date don’t use old date use new date then more date then use it. Very bad her not well done
She is not the most articulate or engaging speaker but I deeply enjoyed and understood her presentation.
Thank you, so I didnt waste time. And to your question, if it wasn’t rhetorical. Mathematical modeling is quantifying the qualitative understanding of a “system”. For example, we know what a thrown ball is going to do, but the model, aka Newton’s laws of motion, gives a numerical value for its behavior. It’s speed, direction, hight, distance, and so on. I hope that’s clear.
Time is gone. It's not mine or yours. Sitting and waiting now
Data not date. I don’t know how to spell lol just was a little frustrated with the lecture. Felt some one need to say. It would be interesting to model the points of this lecture then model them. What are the data points of this lecture. I don’t normally make negative comments fill bad doing so in-fact. I suspect this is a lecture from a subject mater expert a well put together presentation except for the lack of data points that are significant to the subject
@@iteerrex8166 I’d like to here more technical plotting and of points. Maybe example of real input and out put.
It is totally unacceptable that maths dosent predict future but their is randomisation we have to predict randomnes then we pre dict future
Just is case: the actual definition of randomness is the intrinsic property of not being predictable for single events, although the average for those events is predictable.
Well....Mathematics will not predict the future. Mathematics is already entangled in the future. Future will also be shaped according to acts now. Cause and effects. Surely, if used well,Mathematics will tell you all .not just the future... Future is a very important component but a part when the whole existence is considered.!
incorrect.
@@thekaxmax 😂😂😂😂😂😂
@@gayan369 "Mathematics is already entangled in the future."--incorrect. Mathematics is 'entangled' in what it is being used for, that's all--it is a method of description and explanation.
@thekaxmax you haven't got a clue what it is, my friend. Unless one is entangled with another as a matter of direct or inversely proportional , you would not be able to predict anything. They must have a correlation within one another. Einstein said Mathematics is the queen's of all science.
Don't take mathematics, it is too much for you. Just take a simple example.
To predict someone’s characters in Psychology( ask a professor as you definitely don't know this too), they will also have mathematical models, including the behaviour. When they see your behaviour, they will say who you are. Because it is entangled with in one another. This is a very simple explanation.
Ask Einstein if you met him again and look for Sathara sathipattana.. Einstein got everything from that book.
"Sathara Sathipattana" 📖.
@@gayan369 Show your evidence and working.
Your argument is mistaken, weak, and includes at least two fallacies. And at least two personal attacks, which are an indication that your argument is weak and you know it.
She does not understand that math is not a science but an instrument. The discipline of mathematical modelling is a toolset. A well developed one, with ARMAX, Kalman, observability, controllability, etc - but yet it is only a toolset. If you apply a wrong tool, not based on the underlying science, you'll get "results" which may be arbitrarily far from reality. These "results" will look "scientific" and convincing to uninitialized - but their Science-wise value is zero. There is no (and can not be) "generalized" modelling, and any talks about it have no scientific value. Moreover, any pretenses of modelling non-linear loop-backed unstable stochastic systems are fake.