Measure is unceasing

RSS Feed, subscribe per email, all content

List of past fraudsters similar to SBF (2022/11/28)

To inform my forecasting around FTX events, I looked at the Wikipedia list of fraudsters and selected those I subjectively found similar—you can see a spreadsheet with my selection here. For each of the similar fraudsters, I present some common basic details below together with some notes.

My main takeaway is that many salient aspects of FTX have precedents: the incestuous relationship between an exchange and a trading house (Bernie Madoff, Richard Whitney), a philosophical or philanthropic component (Enric Duran, Tom Petters, etc.), embroiling friends and families in the scheme (Charles Ponzi), or multi-billion fraud not getting found out for years (Elizabeth Holmes, many others).

Fraud with a philosophical, philanthropic or religious component

Some data on the stock of EA™ funding (2022/11/20)

Overall Open Philanthropy funding

Open Philanthropy’s allocation of funding through time looks as follows:

Bar graph of OpenPhil allocation by year. Global health leads for most years. Catastrophic risks are usually second since 2017. Overall spend increases over time.

Forecasting Newsletter for October 2022 (2022/11/15)

Highlights

Tracking the money flows in forecasting (2022/11/06)

This list of forecasting organizations includes:

Metaforecast late 2022 update: GraphQL API, Charts, better infrastructure behind the scenes. (2022/11/04)

tl;drMetaforecast is a search engine and an associated repository for forecasting questions. Since our last update, we have added a GraphQL API, charts, and dashboards. We have also reworked our infrastructure to make it more stable. 

New API

Our most significant new addition is our GraphQL API. It allows other people to build on top of our efforts. It can be accessed on metaforecast.org/api/graphql, and looks similar to the EA Forum's own graphql api.

Brief thoughts on my personal research strategy (2022/10/31)

Here are a few estimation related things that I can be doing:

  1. In-house longtermist estimation: I estimate the value of speculative projects, organizations, etc.
  2. Improving marginal efficiency: I advise groups making specific decisions on how to better maximize expected value.
  3. Building up estimation capacity: I train more people, popularize or create tooling, create templates and acquire and communicate estimation know-how, and make it so that we can “estimate all the things”.

Are flimsy evaluations worth it? (2022/10/27)

I recently received a bit of grief over a brief evaluations of the impact of the top-10 billionnaires. It seems possible that this topic is worth discussing. In what follows I outline a few non-exhaustive considerations, as well as a few questions of interest.


“Duty Calls” , by xkcd

Value of flimsy evaluations

Brief evaluations of top-10 billionnaires (2022/10/21)

As part of my work with the Quantified Uncertainty Research Institute, I am experimenting with speculative evaluations that could be potentially scalable. Billionaires were an interesting evaluation target because there are a fair number of them, and at least some ate nominally aiming to do good.

For now, for each top 10 billionaire, I have tried to get an idea of:

  1. How much value have they created through their business activities?
  2. How much impact have they created through their philanthropic activities

Sometimes you give to the commons, and sometimes you take from the commons (2022/10/17)

Sometimes you give to the commons, and sometimes you trade from the commons. And through this giving and taking, people are able to smooth consumption. This is good because getting more ressources from the commons when you temporarily have fewer of them is more positive than giving ressources away when you temporarily have more of them.


Engraving depicting the curse of Tantalus

Anyways, a phenomenon I’ve noticed is that sometimes, you can only give to the commons, but you can’t take from the commons. This is dysfunctional, and defeats the whole purpose of the commons.

Some examples, vaguely based on real life:

Forecasting Newsletter: September 2022. (2022/10/12)

Highlights

Five slightly more hardcore Squiggle models. (2022/10/10)

Following up on Simple estimation examples in Squiggle, this post goes through some more complicated models in Squiggle.

Initial setup

As well as in the playground, Squiggle can also be used inside VS Code, after one installs this extension, following the instructions here. This is more convenient when working with more advanced models because models can be more quickly saved, and the overall experience is nicer.

Samotsvety Nuclear Risk update October 2022 (2022/10/03)

 After recent events in Ukraine, Samotsvety convened to update our probabilities of nuclear war. In March 2022, at the beginning of the Ukraine war, we were at ~0.01% that London would be hit with a nuclear weapon in the next month. Now, we are at ~0.02% for the next 1-3 months, and at 16% that Russia uses any type of nuclear weapon in Ukraine in the next year. 

Expected values are more finicky and more person-dependent than probabilities, and readers are encouraged to enter their own estimates, for which we provide a template. We’d guess that readers would lose 2 to 300 hours by staying in London in the next 1–3 months, but this estimate is at the end of a garden of forking paths, and more pessimistic or optimistic readers might make different methodological choices. We would recommend leaving if Russia uses a tactical nuclear weapon in Ukraine.

Since March, we have also added our track record to samotsvety.org/track-record, which might be of use to readers when considering how much weight to give to our predictions. 

Use a less coarse analysis of AMF beneficiary age and consider counterfactual deaths (2022/09/28)

tl;dr: GiveWell considers a fairly coarse division of beneficiary age, and groups children of 0 to 5 years old together. This may lead to inaccurate or inexact calculations. In addition, GiveWell doesn’t completely account for counterfactual mortality: where a beneficiary is saved from dying of malaria but dies later anyways.

Following up on Use distributions to more parsimoniously estimate impact, I was looking at the population analysis of the AMF distributions, because a previous attempt at adding uncertainty to the analysis was messier than I would have wished.

But following up on that analysis, I realized that the strategy GiveWell uses is:

$5k challenge to quantify the impact of 80,000 hours' top career paths (2022/09/23)

Motivation

80,000 hours has identified a number of promising career paths. They have a fair amount of analysis behind their recommendations, and in particular, they have a list of top ten priority paths. 

However, 80,000 hours doesn’t quite[^1] have quantitative estimates of these paths' value. Although their usefulness would not be guaranteed, quantitative estimates could make it clearer:

Utilitarianism: An Incomplete Approach (2022/09/19)

This blog post gives the sketch of a book, or maybe a long article, that’s been on my mind for a while. I wrote it last week, over the course of an hour an a half, with The Incredibles blasting on the background and with me feeling intellectually alive.

Chapter 1. Utilitarism: The building blocks

This chapter would define utilitarianism, and go over the building blocks of expected utility maximization, like I did in this post but without boring the reader to death. The building blocks are:

Use distributions to more parsimoniously estimate impact (2022/09/15)

Summary

By incorporating uncertainty into its estimates, GiveWell would produce better estimates. This is best done by working with distributions, as opposed to point estimates. For example, “$294 per doubling of consumption” is a point estimate[^1], but the following is a distribution:

More content