more cleaning up
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@ -25,9 +25,9 @@ When the professors deviate from that general structure, they become less convin
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The question presented is whether there is a nutrition based poverty trap. A mathematical formalism for a poverty trap is presented, in which wealth at time t+1 depends on wealth at time t. A poverty trap appears if falling below a wealth threshold leads to a further sliding down, that is, if the relationship between wealth at time t and t+1 looks like:
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 as opposed to like
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 as opposed to like
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(In both cases, you start at y0 at time 0, move to y1 at time 1, to y2 at time 2, etc.)
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@ -15,9 +15,9 @@ In [Shapley values: Better than counterfactuals](https://forum.effectivealtruism
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* We propose several speculative forms of Shapley values: Shapley values + Moments of Consciousness, Shapley Values + Decision Theory, Shapley Values + Expected Value
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* We conclude with some complimentary (and yet obligatory) ramblings about Shapley Values, Goodhart's law, and Stanovich's disrationalia.
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Because this post is long, images will be interspersed throughout to clearly separate sections and provide rest for tired eyes. This is an habit I have from my blogging days, though which I have not seen used in this forum.
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Because this post is long,https://images.nunosempere.com/blog/2020/03/10/shapley-values-ii will be interspersed throughout to clearly separate sections and provide rest for tired eyes. This is an habit I have from my blogging days, though which I have not seen used in this forum.
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## Philantropic Coordination Theory:
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@ -42,7 +42,7 @@ If we try to calculate the Shapley value in this case, we notice that it depends
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In any case, their Shapley values are:
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One can understand this as follows: Player A has Value({A}) already in their hand, and Player B has Value({B}), and they're considering whether to cooperate. If they do, then the surplus from cooperating is Value({A,B}) - (Value({A}) + Value({B})), and it get's shared equally:
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@ -136,7 +136,7 @@ Note that, because GiveWell's alternative is known, GiveWell doesn't have to see
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* When buying a certificate of impact, the donor would in fact be willing to pay _more_ than $1, because $1 dollar can't get him that much value any more, due to diminishing returns. Similarly, GiveWell would be willing to sell it for _less_ than $1, because of the same reasons; once diminishing returns start setting in, they would have to donate less than $1 to their best alternative to get the equivalent of $1 dollar of donations to SCI. I've thus pretended that in this market with one seller and one buyer, the price is agreed to be $1. Another solution would be to have an efficient market in certificates of impact.
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* The value certificate equilibrium is very similar regardless of whether one is thinking in terms of Shapley values or counterfactuals. I feel, but can't prove, that Shapley values add a kind of clarity and crispness to the reasoning, if only because they force you to consider all the moving parts.
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## Shapley values of forecasters.
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@ -202,7 +202,7 @@ while the new forecaster gets rewarded in proportion to
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This still preserves some of the same incentives as above, though in this case, attribution becomes more tricky. Further, anecdotically, seeing someone's distribution before updating gives more information than seeing someone's distribution after they've updated, so just seeing the contrast between g(0) and g(1) might be useful to future forecasters.
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## A value attribution impossibility theorem.
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@ -279,7 +279,7 @@ With that in mind, one of the most interesting facts about Arrow's impossibility
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As such, I'm hedging my bets: impossibility theorems must be taken with a grain of salt; they can be stepped over if their assumptions do not hold.
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## Parfit's [_Five Mistakes in Moral Mathematics_](http://www.stafforini.com/docs/Parfit%20-%20Five%20mistakes%20in%20moral%20mathematics.pdf).
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@ -289,7 +289,7 @@ The Indian Mathematician Brahmagupta describes the solution to the quadratic equ
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to describe the solution to the quadratic equation ax^2 + bx = c.
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I read Parfit's piece with the same admiration, sadness and sorrow with which I read the above paragraph. On the one hand, he is oftent clearly right. On the other hand, he's just working with very rudimentary tools: mere words.
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@ -356,7 +356,7 @@ Overall, I think that Shapley values do pretty well on the problems posed by Par
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> (C7) Even if an act harms no one, this act may be wrong because it is one of a set of acts that together harm other people. Similarly, even if some act benefits no one, it can be what someone ought to do, because it is one of a set of acts that together benefit other people.
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## Shapley value puzzles
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@ -433,7 +433,7 @@ Calculate the Shapley values for:
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* You (Emma)
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* Lucy
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## Speculative Shapley extensions.
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@ -478,7 +478,7 @@ If you the agent's information for those expected values, this allows you to pun
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However, Shapley values are probably be too unsophisticated to be used in situations which are primarily about social incentives.
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## Some complimentary (and yet obligatory) ramblings about Shapley Values, Goodhart's law, and Stanovich's disrationalia.
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@ -537,7 +537,7 @@ Suppose that you have two players, player a and Player B, and three charities: G
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Or, in graph form:
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Caveat:
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@ -128,4 +128,4 @@ Of the questions which have been added recently to the Good Judgment Open, the c
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* [Zvi on Evaluating Predictions in Hindsight](https://www.lesswrong.com/posts/BthNiWJDagLuf2LN2/evaluating-predictions-in-hindsight). A fun read. Of course, the dissing of Scott Alexander's prediction is fun to read, but I really want to see how a list of Zvi's predictions fares.
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* An oldie related to the upcoming US elections: [Which Economic Indicators Best Predict Presidential Elections?](https://fivethirtyeight.blogs.nytimes.com/2011/11/18/which-economic-indicators-best-predict-presidential-elections/), from 2011's Nate Silver.
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* [A rad comment exchange at GJOpen](https://www.gjopen.com/comments/comments/1018771) in which cool superforecaster @Anneinak shares some pointers.
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* [As the efficient markets hypothesis turns 50, it is time to bin it](https://www.ft.com/content/dbf88254-22af-11ea-b8a1-584213ee7b2b) for a Financial Times article, from Jan 1st and thus untainted by coronavirus discussion. Related: [This LW comment by Wei Dai](https://www.lesswrong.com/posts/jAixPHwn5bmSLXiMZ/open-and-welcome-thread-february-2020?commentId=a9YCk3ZtpQZCDqeqR#wAHCXmnywzfhoQT9c) and [this tweet](https://twitter.com/ESYudkowsky/status/1233174331133284353) from Eliezer Yudkowsky. See also a very rambly article by an Australian neswpaper: [Pandemic highlights problems with efficient-market hypothesis](https://independentaustralia.net/politics/politics-display/pandemic-highlights-problems-with-efficient-market-hypothesis,13776).
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* [As the efficient markets hypothesis turns 50, it is time to bin it](https://www.ft.com/content/dbf88254-22af-11ea-b8a1-584213ee7b2b) for a Financial Times article, from Jan 1st and thus untainted by coronavirus discussion. Related: [This LW comment by Wei Dai](https://www.lesswrong.com/posts/jAixPHwn5bmSLXiMZ/open-and-welcome-thread-february-2020?commentId=a9YCk3ZtpQZCDqeqR#wAHCXmnywzfhoQT9c) and [this tweet](https://twitter.com/ESYudkowsky/status/1233174331133284353) from Eliezer Yudkowsky. See also a very rambly article by an Australian neswpaper: [Pandemic highlights problems with efficient-market hypothesis](https://independentaustralia.net/politics/politics-display/pandemic-highlights-problems-with-efficient-market-hypothesis,13776).
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@ -105,7 +105,7 @@ This month they've organized a flurry of activities, most notably:
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PredictIt is a prediction platform restricted to US citizens, but also accessible with a VPN. This month, they present a map about the electoral college result in the USA. States are colored according to the market prices:
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Some of the predictions I found most interesting follow. The market probabilities can be found below; the engaged reader might want to write down their own probabilities and then compare.
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@ -259,7 +259,7 @@ This section contains items which have recently come to my attention, but which
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> Our judges in this study were eight individuals, carefully selected for their expertise as handicappers. Each judge was presented with a list of 88 variables culled from the past performance charts. He was asked to indicate which five variables out of the 88 he would wish to use when handicapping a race, if all he could have was five variables. He was then asked to indicate which 10, which 20, and which 40 he would use if 10, 20, or 40 were available to him.
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> We see that accuracy was as good with five variables as it was with 10, 20, or 40. The flat curve is an average over eight subjects and is somewhat misleading. Three of the eight actually showed a decrease in accuracy with more information, two improved, and three stayed about the same. All of the handicappers became more confident in their judgments as information increased.
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> 
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> 
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* The study contains other nuggets, such as:
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* An experiment on trying to predict the outcome of a given equation. When the feedback has a margin of error, this confuses respondents.
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@ -1,8 +1,6 @@
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Forecasting Newsletter: June 2020.
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==============
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# Forecasting Newsletter. June 2020.
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## Highlights
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1. Facebook launches [Forecast](https://www.forecastapp.net/), a community for crowdsourced predictions.
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@ -163,4 +161,4 @@ Note to the future: All links are added automatically to the Internet Archive. I
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> "I beseech you, in the bowels of Christ, think it possible that you may be mistaken." [Oliver Cromwell](https://en.wikipedia.org/wiki/Cromwell%27s_rule)
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@ -115,7 +115,7 @@ Ordered in subjective order of importance:
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* The International Energy Agency had terrible forecasts on solar photo-voltaic energy production, until [recently](https://pv-magazine-usa.com/2020/07/12/has-the-international-energy-agency-finally-improved-at-forecasting-solar-growth/):
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> 
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> 
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> ...It’s a scenario assuming current policies are kept and no new policies are added.
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@ -241,7 +241,7 @@ Ordered in subjective order of importance:
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* [Taleb](https://forecasters.org/blog/2020/06/14/on-single-point-forecasts-for-fat-tailed-variables/): _On single point forecasts for fat tailed variables_. Leitmotiv: Pandemics are fat-tailed.
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>  
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>  
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> We do not need more evidence under fat tailed distributions — it is there in the properties themselves (properties for which we have ample evidence) and these clearly represent risk that must be killed in the egg (when it is still cheap to do so). Secondly, unreliable data — or any source of uncertainty — should make us follow the most paranoid route. \[...\] more uncertainty in a system makes precautionary decisions very easy to make (if I am uncertain about the skills of the pilot, I get off the plane).
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@ -179,7 +179,7 @@ The Foresight Insitute organizes weekly talks; here is one with Samo Burja on [l
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Last, but not least, Ozzie Gooen on [Multivariate estimation & the Squiggly language](https://www.lesswrong.com/posts/kTzADPE26xh3dyTEu/multivariate-estimation-and-the-squiggly-language):
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