Forecasting Newsletter. Tweaks, and making sure it's presented as a draft

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Whatever happened to forecasting? May 2020
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Whatever happened to forecasting? May 2020 [Draft]
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A forecasting digest with a focus on experimental forecasting. You can sign up [here](https://mailchi.mp/18fccca46f83/forecastingnewsletter). The newsletter itself is experimental, but there will be at least five more iterations.
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- [Central Bankers Adopt Scenario Forecasting for Post-Virus World](https://www.bloomberg.com/news/articles/2020-05-11/central-bankers-adopt-scenario-forecasting-for-post-virus-world). I find it cute that China, seeing as how they're not going to be able to meet their GDP targets, is "considering dropping its traditional numerical GDP target". Otherwise, central banks are coming to terms with the depths of their uncertainty.
- [Locust-tracking application for the UN](https://www.research.noaa.gov/article/ArtMID/587/ArticleID/2620/NOAA-teams-with-the-United-Nations-to-create-locust-tracking-application). (and [here](https://www.washingtonpost.com/weather/2020/05/13/east-africa-locust-forecast-tool/) is a take by the Washington Post), using software originally intended to track the movements of air polution. NOAA also sounds like a really cool organization: "NOAA Research enables better forecasts, earlier warnings for natural disasters, and a greater understanding of the Earth. Our role is to provide unbiased science to better manage the environment, nationally, and globally."
- [United Nations: World Economic Situation and Prospects as of mid-2020](https://www.un.org/development/desa/dpad/publication/world-economic-situation-and-prospects-as-of-mid-2020/). A recent report is out, which predicts a 3.2% contraction of the global economy. Between 34 and 160 million people are expected to fall below the extreme poverty line this year.
- [Kelsey Piper of Vox disses on the IHME model](https://www.vox.com/future-perfect/2020/5/2/21241261/coronavirus-modeling-us-deaths-ihme-pandemic). "Some of the factors that make the IHME model unreliable at predicting the virus may have gotten people to pay attention to it;"or "Other researchers found the true deaths were outside of the 95 percent confidence interval given by the model 70 percent of the time."
- [Fox News](https://www.fox10phoenix.com/news/cdc-says-all-models-forecast-increase-in-covid-19-deaths-in-coming-weeks-exceeding-100k-by-june-1) and [Business Insider](https://www.businessinsider.com/cdc-forecasts-100000-coronavirus-deaths-by-june-1-2020-5?r=KINDLYSTOPTRACKINGUS) report over the CDC forecasting 100k deaths by June the 1st, differently.
- [Kelsey Piper of Vox disses on the IHME model](https://www.vox.com/future-perfect/2020/5/2/21241261/coronavirus-modeling-us-deaths-ihme-pandemic). "Some of the factors that make the IHME model unreliable at predicting the virus may have gotten people to pay attention to it;" or "Other researchers found the true deaths were outside of the 95 percent confidence interval given by the model 70 percent of the time."
- [Fox News](https://www.fox10phoenix.com/news/cdc-says-all-models-forecast-increase-in-covid-19-deaths-in-coming-weeks-exceeding-100k-by-june-1) and [Business Insider](https://www.businessinsider.com/cdc-forecasts-100000-coronavirus-deaths-by-june-1-2020-5?r=KINDLYSTOPTRACKINGUS) report about the CDC forecasting 100k deaths by June the 1st, differently.
- Yahoo has automated finance forecast reporting. It took me a while (three months) to notice that the low quality finance articles that were popping up in my google alerts were machine generated. See [Synovus Financial Corp. Earnings Missed Analyst Estimates: Here's What Analysts Are Forecasting Now](https://finance.yahoo.com/news/synovus-financial-corp-earnings-missed-152645825.html), [Wienerberger AG Earnings Missed Analyst Estimates: Here's What Analysts Are Forecasting Now](https://finance.yahoo.com/news/wienerberger-ag-earnings-missed-analyst-070545629.html), [Park Lawn Corporation Earnings Missed Analyst Estimates: Here's What Analysts Are Forecasting Now](https://news.yahoo.com/park-lawn-corporation-earnings-missed-120314826.html); they have a similar structure, paragraph per paragraph, and seem to have been generated from a template which changes a little bit depending on the data (they seem to have different templates for very positive, positive, neutral and negative change). To be clear, I could program something like this given a good finance api and a spare week/month, and in fact did so a couple of years ago for an automatic poetry generator. *But I didn't notice because I wasn't paying attention*.
- [Sports betting alternatives which are booming during the Corona shutdown](https://thegamehaus.com/sports/sports-betting-alternatives-which-are-booming-during-the-corona-shutdown/2020/05/15/). Suggested alternatives for bettors include e-sports, casinos, politics and reality-tv.
- Some transcient content on 538 about [Biden vs past democratic nomines](https://fivethirtyeight.com/features/how-does-biden-stack-up-to-past-democratic-nominees/), about [Trump vs Biden polls](https://fivethirtyeight.com/features/you-can-pay-attention-to-those-trump-vs-biden-polls-but-be-cautious/) and about [the USA vicepresidential draft](https://fivethirtyeight.com/features/its-time-for-another-2020-vice-presidential-draft/), and an old [review of the impact of VP candidates in USA elections](http://baseballot.blogspot.com/2012/07/politically-veepstakes-isnt-worth.html) which seems to have aged well. 538 also brings us this overview of [models with unrealistic-yet-clearly-stated assumptions](https://projects.fivethirtyeight.com/covid-forecasts/); apparently, deaths (not "confirmed deaths", just "deaths") according to John Hopkins University are flat out *linear* from April 1 to May 1.
- [Why Economic Forecasting Is So Difficult in the Pandemic](https://hbr.org/2020/05/why-economic-forecasting-is-so-difficult-in-the-pandemic). Harvard Review Economists share their difficulties. Problems include "not knowing for sure what is going to happen", the government passing legislation unusually fast, sampling errors and reduced response rates from surveys, and lack of knowledge about epidemiology.
- [Why Economic Forecasting Is So Difficult in the Pandemic](https://hbr.org/2020/05/why-economic-forecasting-is-so-difficult-in-the-pandemic). Harvard Review Economists share their difficulties. Problems include "not knowing for sure what is going to happen", the government passing legislation uncharacteristically fast, sampling errors and reduced response rates from surveys, and lack of knowledge about epidemiology.
- [Nowcasting the weather in Africa](https://phys.org/news/2020-05-storm-chasers-life-saving.html) to reduce fatalities.
- The [Washington post](https://www.washingtonpost.com/outlook/2020/05/19/lets-check-donald-trumps-chances-getting-reelected/) offers a highly partisan view of Trump's chances of winning the election. The author, having already made a past prediction, and seeing as how other media outlets offer a conflicting perspective, rejects the information he's learnt, and instead can only come up with reasons which confirm his initial position. Problem could be solved with a prediction market.
- [IBM releases new AI forecasting tool](https://www.ibm.com/products/planning-analytics): "IBM Planning Analytics is an AI-infused integrated planning solution that automates planning, forecasting and budgeting. By accelerating processes and obtaining more reliable results, Planning Analytics powers more intelligent workflows that drive greater accuracy and efficiency. Now you can quickly and easily drive faster, more accurate plans for financial operations, sales, supply chain and beyond. And with the new release of the Planning Analytics On Demand version, small to medium businesses can transcend the limits of spreadsheet planning and drive better business decisions at a lower price point." See [here](https://www.channelasia.tech/article/679887/ibm-adds-ai-fuelled-forecasting-planning-analytics-platform/) or [here](https://www.cio.com/article/3544611/ibm-adds-ai-fueled-forecasting-to-planning-analytics-platform.html) for a news take.
[Auditor urges more oversight, better forecasting at the United State's Department of Transport](https://www.wral.com/coronavirus/auditor-urges-more-oversight-better-forecasting-at-dot/19106691/): "Instead of basing its spending plan on project-specific cost estimates, Wood said, the agency uses prior-year spending. That forecasting method doesn't account for cost increases or for years when there are more projects in the works." The budget of the organization is $5.9 billion. Problem could be solved with a prediction market.
- [IBM releases new AI forecasting tool](https://www.ibm.com/products/planning-analytics): "IBM Planning Analytics is an AI-infused integrated planning solution that automates planning, forecasting and budgeting." See [here](https://www.channelasia.tech/article/679887/ibm-adds-ai-fuelled-forecasting-planning-analytics-platform/) or [here](https://www.cio.com/article/3544611/ibm-adds-ai-fueled-forecasting-to-planning-analytics-platform.html) for a news take.
- [Auditor urges more oversight, better forecasting at the United State's Department of Transport](https://www.wral.com/coronavirus/auditor-urges-more-oversight-better-forecasting-at-dot/19106691/): "Instead of basing its spending plan on project-specific cost estimates, Wood said, the agency uses prior-year spending. That forecasting method doesn't account for cost increases or for years when there are more projects in the works." The budget of the organization is $5.9 billion. Problem could be solved with a prediction market.
- [California politics pretends to be about recession forecasts](https://calmatters.org/economy/2020/05/newsom-economic-forecast-criticism-california-model-recession-budget/). Problem could be solved with a prediction market. See also: [Simulacra levels](https://www.lesswrong.com/posts/fEX7G2N7CtmZQ3eB5/simulacra-and-subjectivity?commentId=FgajiMrSpY9MxTS8b); the article is on simulacrum level 3. Key quote, about a given forecasting model: "Its just preposterously negative... How can you say that out loud without giggling?"
## Grab bag
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- [Space Weather Challenge and Forecasting Implications of Rossby Waves](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018SW002109). Recent advances may help predict solar flares better. I don't know how bad the worst solar flare could be, and how much a two year warning could buy us, but I tend to view developments like this very positively.
- [The advantages and limitations of forecasting](https://rwer.wordpress.com/2020/05/12/the-advantages-and-limitations-of-forecasting/). A short and sweet blog post, with a couple of forecasting anecdotes and zingers.
- The [University of Washington Medicine](https://patch.com/washington/seattle/uw-medicine-forecasting-losses-500-million-summers-end) might be pretending they need more money to try to bait donors. Of course, America being America, they might actually not have enough money. During a pandemic. "UW Medicine has been at the forefront of the national response to COVID-19 in treating critically ill patients".
- [Forecasting drug utilization and expenditure: ten years of experience in Stockholm](https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-020-05170-0). A normally pretty good forecasting model had the bad luck of not foreseeing a Black Swan, and sending a study to a journal just before a pandemic, so that it's being published now. They write: "According to the forecasts, the total pharmaceutical expenditure was estimated to increase between 2 and 8% annually. Our analyses showed that the accuracy of these forecasts varied over the years with a mean absolute error of 1.9 percentage points." They further conclude: "Based on the analyses of all forecasting reports produced since the model was established in Stockholm in the late 2000s, we demonstrated that it is feasible to forecast pharmaceutical expenditure with a reasonable accuracy." Presumably, this has increased further because of covid, sending the mean absolute error through the roof.
- [Forecasting drug utilization and expenditure: ten years of experience in Stockholm](https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-020-05170-0). A normally pretty good forecasting model had the bad luck of not foreseeing a Black Swan, and sending a study to a journal just before a pandemic, so that it's being published now. They write: "According to the forecasts, the total pharmaceutical expenditure was estimated to increase between 2 and 8% annually. Our analyses showed that the accuracy of these forecasts varied over the years with a mean absolute error of 1.9 percentage points." They further conclude: "Based on the analyses of all forecasting reports produced since the model was established in Stockholm in the late 2000s, we demonstrated that it is feasible to forecast pharmaceutical expenditure with a reasonable accuracy." Presumably, this has increased further because of covid, sending the mean absolute error through the roof. If the author of this paper bites you, you become a Nassim Taleb.
- In this time of need, where global cooperation might prove to be immensely valuable, Italy has lessons to share about how to forecast the coronavirus. The article [Forecasting in the Time Of The Coronavirus](https://www.bancaditalia.it/media/notizie/2020/en_Previsioni_al_tempo_del_coronavirus_Locarno_Zizza.pdf), by the Central Bank of Italy, is only available in Italian. Mysteriously, the press release, however, is in [English](https://www.bancaditalia.it/media/notizia/forecasting-in-the-time-of-coronavirus/).
- [An analogy-based method for strong convection forecasts in China using GFS forecast data](https://www.tandfonline.com/doi/full/10.1080/16742834.2020.1717329). "Times in the past when the forecast parameters are most similar to those forecast at the current time are identified by searching a large historical numerical dataset", and this is used to better predict one particular class of meteorological phenomena. See [here](https://www.eurekalert.org/pub_releases/2020-05/ioap-ata051520.php) for a press release.
- Some interesting discussion about forecasting over at Twitter, in [David Manheim](https://twitter.com/davidmanheim)'s, [Philip Tetlock](https://twitter.com/PTetlock)'s accounts, some of which have been incorporated into this newsletter. [This twitter thread](https://twitter.com/lukeprog/status/1262492767869009920) contains some discussion about how Good Judgement Open, Metaculus and expert forecasters fare against each other. In particular, note the caveats by @LinchZhang: "For Survey 10, Metaculus said that question resolution was on 4pm ET Sunday, a lot of predictors (correctly) gauged that the data update on Sunday will be delayed and answered the letter rather than the spirit of the question (Metaculus ended up resolving it ambiguous). [This thread](https://twitter.com/mlipsitch/status/1257857079756365824) by Marc Lipsitch has become popular, and I personally also enjoyed [these](https://twitter.com/LinchZhang/status/1262127601176334336) [two](https://twitter.com/LinchZhang/status/1261427045977874432) twitter threads by Linchuan Zhang, on forecasting mistakes.
- Some interesting discussion about forecasting over at Twitter, in [David Manheim](https://twitter.com/davidmanheim)'s, [Philip Tetlock](https://twitter.com/PTetlock)'s accounts, some of which have been incorporated into this newsletter. [This twitter thread](https://twitter.com/lukeprog/status/1262492767869009920) contains some discussion about how Good Judgement Open, Metaculus and expert forecasters fare against each other. In particular, note the caveats by @LinchZhang: "For Survey 10, Metaculus said that question resolution was on 4pm ET Sunday, a lot of predictors (correctly) gauged that the data update on Sunday will be delayed and answered the letter rather than the spirit of the question (Metaculus ended up resolving it ambiguous)." [This thread](https://twitter.com/mlipsitch/status/1257857079756365824) by Marc Lipsitch has become popular, and I personally also enjoyed [these](https://twitter.com/LinchZhang/status/1262127601176334336) [two](https://twitter.com/LinchZhang/status/1261427045977874432) twitter threads by Linchuan Zhang, on forecasting mistakes.
- The Cato Institute releases [12 New Immigration Ideas for the 21st Century](https://www.cato.org/publications/white-paper/12-new-immigration-ideas-21st-century), including two from Robin Hanson: Choosing Immigrants through Prediction Markets & Transferable Citizenship.
- [Forecasting the Weather in 1946](https://www.smh.com.au/environment/weather/from-the-archives-1946-forecasting-the-world-s-weather-20200515-p54tfd.html)
- Some films are so bad it's funny. [This article fills the same niche](https://www.moneyweb.co.za/investing/yes-it-is-possible-to-predict-the-market/) for forecasting. It has it all: Pythagorean laws of vibration, epicycles, an old and legendary master with mystical abilities, 90 year predictions which come true. Further, from the [Wikipedia entry](https://en.wikipedia.org/wiki/William_Delbert_Gann#Controversy): "He told me that his famous father could not support his family by trading but earned his living by writing and selling instructional courses."
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- How do we get from here to there? and/or What should I be looking out for?
> "We do not claim our assessments are infallible. Instead, we assert that we offer our most deeply and objectively based and carefully considered estimates."
- [How to Measure Anything](https://www.lesswrong.com/posts/ybYBCK9D7MZCcdArB/how-to-measure-anything), a review.
- [How to Measure Anything](https://www.lesswrong.com/posts/ybYBCK9D7MZCcdArB/how-to-measure-anything), a review. "Anything can be measured. If a thing can be observed in any way at all, it lends itself to some type of measurement method. No matter how “fuzzy” the measurement is, its still a measurement if it tells you more than you knew before. And those very things most likely to be seen as immeasurable are, virtually always, solved by relatively simple measurement methods."
- The World Meteorological organization, on their mandate to guarantee that [no one is surprised by a flood](https://public.wmo.int/en/our-mandate/water/no-one-is-surprised-by-a-flood). Browsing the webpage it seems that the organization is either a Key Organization Safeguarding the Vital Interests of the World or Just Another of the Many Bureaucracies Already in Existence, but it's unclear how to differentiate between the two.
- [95%-ile isn't that good](https://danluu.com/p95-skill/): "Reaching 95%-ile isn't very impressive because it's not that hard to do."
- [The Backwards Arrow of Time of the Coherently Bayesian Statistical Mechanic](https://arxiv.org/abs/cond-mat/0410063): Identifying thermodinamic entropy with the Bayesian uncertainty of an ideal observer leads to a contradiction, because as the observer observes more about the system, they update on this information, which reduces uncertainty, and thus entropy.
- [The Backwards Arrow of Time of the Coherently Bayesian Statistical Mechanic](https://arxiv.org/abs/cond-mat/0410063): Identifying thermodinamic entropy with the Bayesian uncertainty of an ideal observer leads to problems, because as the observer observes more about the system, they update on this information, which in expectation reduces uncertainty, and thus entropy. But entropy increases with time.
- This might be interesting to students in the tradition of E.T. Jaynes: for example, the paper directly conflicts with this LessWrong post: [The Second Law of Thermodynamics, and Engines of Cognition](https://www.lesswrong.com/posts/QkX2bAkwG2EpGvNug/the-second-law-of-thermodynamics-and-engines-of-cognition), part of *Rationality, From AI to Zombies*. The way out might be to postulate that actually, the Bayesian updating process itself would increase entropy, in the form of e.g., the work needed to update bits on a computer. Any applications to Christian lore are left as an excercise for the reader. Otherwise, seeing two bright people being cogently convinced of different perspectives does something funny to my probabilities: it pushes them towards 50%, but also increases the expected time I'd have to spend on the topic to move them away from 50%.
- [Behavioral Problems of Adhering to a Decision Policy](https://pdfs.semanticscholar.org/7a79/28d5f133e4a274dcaec4d0a207daecde8068.pdf)
> Our judges in this study were eight individuals, carefully selected for their expertise as