Assuming your primary aim is to dissuade well-qualified immigrants who will contribute to our scientific advancement and cultural and economic growth, that is.
Hooray!
Tuesday, December 31, 2019
Monday, December 23, 2019
Outcomes
At the start of the year I made some predictions. It's now time to see how I did.
In reverse order....
6. The level of CO2 in the atmosphere will increase (p=0.999).
Yup. I don't really need to wait for 1 Jan for that.
5. 2019 will be warmer than most years this century so far (p=0.75 - not the result of any real analysis).
As above, I know we've a few days to go but no need to wait for this one, which has been very clear for a good while now.
4. We will also submit a highly impactful paper in collaboration with many others (p=0.85).
Done, reviews back which look broadly ok, revision planned for early next year when we all have a bit of time (31 Dec is a stupid IPCC submission deadline for lots of other stuff).
3. Jules and I will finish off the rather delayed work with Thorsten and Bjorn (p=0.95).
Yes, the project is done, through the write-up continues. Actually we hope to submit a paper by 31 Dec but there will be more to do next year too.
2. I will run a time (just!) under 2:45 at Manchester marathon (p=0.6).
Nope, 2:47:15 this time. The prediction was made just a few days after I'd run a big PB in a 10k but even then I thought it was barely more likely than not, and it got less likely as the date approached.
1. Brexit won't happen (p=0.95).
On re-reading the old post, I have to admit I cannot remember the precise intention I had when I wrote this. Given the annual time frame of the remainder of the bets, and the narrative of that time being that we were certainly going to leave on the 29th March (as repeated over 100 times by May - remember her? - and the rest of them) I do believe I must have been referring to leaving during 2019. After all, I could never hope to validate a bet of infinite duration. So yes, I'm going to give myself this one.
On the other hand, I did actually think that we would probably not be stupid enough to leave at all, and clearly I misunderestimated the electorate and also the dishonesty of the Conservative Party, or perhaps as it should be known, the English National Party.
I have learnt from that misjudgment and will not be offering any predictions as to where we end up at the end of next year. Which is sort of inconvenient, as we are trying to arrange a new contract with our European friends for work which could extend into 2021. Our options would seem to include: limiting the scope of the contract to what we can confidently complete strictly within 2020, which is far from ideal, or shifting everything to Estonia (incurring additional costs and inconvenience for us, though it may be the best option in the long term). Or just take a punt and cross our fingers that it all turns out ok, despite there being as yet no hint of a sketch of a plan as to how the sales of services into the EU will be regulated or taxed past 2020. It is quite possible that we'll just shut down the (very modest) operation and put our feet up.
I'm still waiting for the brexiters to tell me how any of this is in the country's interests. But that's a rant for another day. Perhaps it's something to do with having enough of experts.
As for scoring my predictions: the idea of a “proper scoring rule” is to provide a useful measure of performance for probabilistic prediction. A natural choice is the logarithmic scoring rule L = log(p) where p is the probability assigned to the outcome, and with all of my predictions having a binary yes/no basis I'll use base 2 for the calculation. The aim is to maximise the score (ie minimise its negativity, as the log of numbers in the range 0 to 1 is negative). A certain prediction where we assign a probability of p=1 to something that comes out right scores a maximum 0, a coin toss is -1 whether right or wrong but if you predict something to only have a p=0.1 chance and it happens, then the score is log(0.1) which in base 2 is a whopping -3.3. Assigning a probability of 0 to the event that happens is a bad idea, the score is infinitely negative...oops.
My score is therefore:
0 - 0.42 - .23 - .07 -1.32 - .07 = 2.11
or about 0.35 per bet, which is equivalent to assigning p=0.78 to the correct outcome each time (which is just the geometric mean of the probabilities I did assign). Of course some were very easy, but that's why I gave them high p estimates which means high score (but a big risk if I'd got them wrong). I could have given a higher probability to the temperature prediction if I'd bothered thinking about it a bit more carefully. The running one was the only truly difficult prediction, because I was specifically calibrating the threshold to be close to the border of what I might achieve. It might have been better presented as a distribution for my finish time, where I would have had to judge the sharpness of the pdf as well as its location (ie mean).
I have learnt from that misjudgment and will not be offering any predictions as to where we end up at the end of next year. Which is sort of inconvenient, as we are trying to arrange a new contract with our European friends for work which could extend into 2021. Our options would seem to include: limiting the scope of the contract to what we can confidently complete strictly within 2020, which is far from ideal, or shifting everything to Estonia (incurring additional costs and inconvenience for us, though it may be the best option in the long term). Or just take a punt and cross our fingers that it all turns out ok, despite there being as yet no hint of a sketch of a plan as to how the sales of services into the EU will be regulated or taxed past 2020. It is quite possible that we'll just shut down the (very modest) operation and put our feet up.
I'm still waiting for the brexiters to tell me how any of this is in the country's interests. But that's a rant for another day. Perhaps it's something to do with having enough of experts.
As for scoring my predictions: the idea of a “proper scoring rule” is to provide a useful measure of performance for probabilistic prediction. A natural choice is the logarithmic scoring rule L = log(p) where p is the probability assigned to the outcome, and with all of my predictions having a binary yes/no basis I'll use base 2 for the calculation. The aim is to maximise the score (ie minimise its negativity, as the log of numbers in the range 0 to 1 is negative). A certain prediction where we assign a probability of p=1 to something that comes out right scores a maximum 0, a coin toss is -1 whether right or wrong but if you predict something to only have a p=0.1 chance and it happens, then the score is log(0.1) which in base 2 is a whopping -3.3. Assigning a probability of 0 to the event that happens is a bad idea, the score is infinitely negative...oops.
My score is therefore:
0 - 0.42 - .23 - .07 -1.32 - .07 = 2.11
or about 0.35 per bet, which is equivalent to assigning p=0.78 to the correct outcome each time (which is just the geometric mean of the probabilities I did assign). Of course some were very easy, but that's why I gave them high p estimates which means high score (but a big risk if I'd got them wrong). I could have given a higher probability to the temperature prediction if I'd bothered thinking about it a bit more carefully. The running one was the only truly difficult prediction, because I was specifically calibrating the threshold to be close to the border of what I might achieve. It might have been better presented as a distribution for my finish time, where I would have had to judge the sharpness of the pdf as well as its location (ie mean).
Tuesday, December 17, 2019
BlueSkiesResearch.org.uk: Is the concept of ‘tipping point’ helpful for describing and communicating possible climate futures?
There’s a new book just out "Contemporary Climate Change Debates: A Student Primer" edited by Mike Hulme. I contributed a short essay arguing the negative side of the above question. It was originally intended to be "Will exceeding 2C of warming lock the world onto a 'Hothouse Earth' trajectory?" but no-one could be found to argue in favour of that (this was shortly after the publication of the Steffen nonsense) so we settled on something a bit more vague. Maybe I should summarise my compelling argument but don’t have time right now so you’ll have to take my word for it.
I haven’t had time to read the book but my complementary copy just arrived (hence the post) and the table of contents is quite interesting, so maybe it would make a good Christmas present for the person who is interested in climate change – or even for someone who isn’t!
Introduction: Why and how to debate climate changeMike Hulme
1. Is climate change the most important challenge of our times?
Sarah Cornell and Aarti Gupta
PART I: What do we need to know?
2. Is the concept of 'tipping point' helpful for describing and communicating possible climate futures?
Michel Crucifix and James Annan
3. Should individual extreme weather events be attributed to human agency?
Friederike E.L. Otto and Greg Lusk
4. Does climate change drive violence, conflict and human migration?
David D. Zhang and Qing Pei; Christiane Fröhlich and Tobias Ide
5. Can the social cost of carbon be calculated?
Reyer Gerlagh and Roweno Heijmans; Kozo Torasan Mayumi
PART II: What should we do?
6. Are carbon markets the best way to address climate change?
Misato Sato and Timothy Laing; Mike Hulme
7. Should future investments in energy technology be limited exclusively to renewables?
Jennie C. Stephens and Gregory Nemet
8. Is it necessary to research solar climate engineering as a possible backstop technology?
Jane C.S. Long and Rose Cairns
PART III: On what grounds should we base our actions?
9. Is emphasising consensus in climate science helpful for policymaking?
John Cook and Warren Pearce
10. Do rich people rather than rich countries bear the greatest responsibility for climate change?
Paul G. Harris and Kenneth Shockley
11. Is climate change a human rights violation?
Catriona McKinnon and Marie-Catherine Petersmann
PART IV: Who should be the agents of change?
12. Does successful emissions reduction lie in the hands of non-state rather than state actors?
Liliana B. Andronova and Kim Coetzee
13. Is legal adjudication essential for enforcing ambitious climate change policies?
Eloise Scotford; Marjan Peeters and Ellen Vos
14. Does the 'Chinese model' of environmental governance demonstrate to the world how to govern the climate?
Tianbao Qin and Meng Zhang; Lei Liu and Pu Wang
15. Are social media making constructive climate policymaking harder?
Mike S. Schäfer and Peter North
Thursday, December 05, 2019
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