Hide table of contents

This post is a tutorial for Visualisation of Probability Mass, a tool that lets you represent credences as percentages in a 10x10 grid. It's useful for turning visual intuitions into coherent percentages, or numerical intuitions into a visual representation.

At the end, I'll briefly mention other tools useful for visualising quantitative beliefs.

When can Visualisation of Probability Mass be Useful?

  • Visually demonstrate your expectation of how a project will turn out, or how likely it is to succeed on some metric, to show your manager 
  • Turn a rough intuition about the harms and benefits of veganism into coherent[1] percentages 
  • Compare your AI worries to a friends'
  • Guess how many people at a party are your friends vs your friends' friends vs your friends' friends' friends
  • Forecast the contents of your fridge by category (It's fine! I'm working, I promise! I have to get calibrated!)

Tutorial

Visualisation of Probability Mass is a tool for quickly representing images of percentages in a 10x10 grid, where each box represents 1%. You can click on any colour (on the right) and then on any tile (on the left) to change its colour.

You can add a title and description for each colour or for the graph in total, and export it as a jpeg.

Here's a worked example (numbers picked mostly at random, not intended as a true claim on this topic):

Personal Experience

Honestly, I find the visual output of Visualisation of Probability Mass more confusing than useful, though when I discussed it with others they found it quite useful. I expect that for some, most of the value is in using the visual half of the tool as an input to turn an intuition into percentages that are coherent[1], rather than as an output, unless you have <4 possible outcomes. 

Another tool which can be used to represent probability density intuitively is Click and Drag Probability Elicitation. However, the web demo is currently too limited to be useful, but I’m excited about its potential when fully usable. If you're building a survey (in Qualtrics, LimeSurvey, or oTree) where people can input probability density graphs, consider using it. 

Tools that can be used to share quantitative beliefs as whole models include coding languages like Squiggle, Guesstimate, and Python

Try it Yourself!

Show us your beliefs on a topic! Post it in the comments. If you can't think of anything, try looking in the comments to get ideas & compare your beliefs with others.  Here's some other suggestions:

  • How did you meet your friends? (i.e. work, sports, other friends, EA, etc.)
  • How do you spend most of your time in an average week? How would you like to spend your time each week?
  • How many people apply to EA jobs they're not suited for vs how many don't apply to enough that they are (and how many get it just right)?
  • How many people live on each continent?
  • How many people have been born in total in each of the last 10 centuries?
  • How many EAs are working on different cause areas?
  • How many EAs do you think should be working on different cause areas?

 

I'll also be running an event in the EA GatherTown today at 6pm GMT to get a feel for the tool together, please come along! 

Tomorrow: Loom, a tool for recording videos! I used it to record all the videos for this sequence. I'll be running another event in the GatherTown tomorrow for Loom. 

 

  1. ^

     I.e. sum to 100%

Comments6


Sorted by Click to highlight new comments since:

I don't know if it's exactly this that will save all communication or anything, but I would be excited to see if this kind of thing could be used to quickly convey takes on even hard community issues in addition to object level things, especially since it points in the direction of considering multiple hypotheses.

e.g. I really loved this picture from this post and even made this speadsheet so you could put in your own numbers, and it seems like a standard part of data visualization to let people see the differences between distributions clearly

Cool spreadsheet! Yeah a tool similar to the square one but in a horizontal line instead seems more useful.

Also: Confido works for intuitively eliciting probabilities

Seeing Theory is also a beautiful guide to visualizing probability.

It looks great, and there are a few others I like as well, but does this let you visualize your own credences? That's what seems like the value here, to me.

Nope, I skimmed the post and missed that that was the specific goal. My bad!

Curated and popular this week
Paul Present
 ·  · 28m read
 · 
Note: I am not a malaria expert. This is my best-faith attempt at answering a question that was bothering me, but this field is a large and complex field, and I’ve almost certainly misunderstood something somewhere along the way. Summary While the world made incredible progress in reducing malaria cases from 2000 to 2015, the past 10 years have seen malaria cases stop declining and start rising. I investigated potential reasons behind this increase through reading the existing literature and looking at publicly available data, and I identified three key factors explaining the rise: 1. Population Growth: Africa's population has increased by approximately 75% since 2000. This alone explains most of the increase in absolute case numbers, while cases per capita have remained relatively flat since 2015. 2. Stagnant Funding: After rapid growth starting in 2000, funding for malaria prevention plateaued around 2010. 3. Insecticide Resistance: Mosquitoes have become increasingly resistant to the insecticides used in bednets over the past 20 years. This has made older models of bednets less effective, although they still have some effect. Newer models of bednets developed in response to insecticide resistance are more effective but still not widely deployed.  I very crudely estimate that without any of these factors, there would be 55% fewer malaria cases in the world than what we see today. I think all three of these factors are roughly equally important in explaining the difference.  Alternative explanations like removal of PFAS, climate change, or invasive mosquito species don't appear to be major contributors.  Overall this investigation made me more convinced that bednets are an effective global health intervention.  Introduction In 2015, malaria rates were down, and EAs were celebrating. Giving What We Can posted this incredible gif showing the decrease in malaria cases across Africa since 2000: Giving What We Can said that > The reduction in malaria has be
LewisBollard
 ·  · 8m read
 · 
> How the dismal science can help us end the dismal treatment of farm animals By Martin Gould ---------------------------------------- Note: This post was crossposted from the Open Philanthropy Farm Animal Welfare Research Newsletter by the Forum team, with the author's permission. The author may not see or respond to comments on this post. ---------------------------------------- This year we’ll be sharing a few notes from my colleagues on their areas of expertise. The first is from Martin. I’ll be back next month. - Lewis In 2024, Denmark announced plans to introduce the world’s first carbon tax on cow, sheep, and pig farming. Climate advocates celebrated, but animal advocates should be much more cautious. When Denmark’s Aarhus municipality tested a similar tax in 2022, beef purchases dropped by 40% while demand for chicken and pork increased. Beef is the most emissions-intensive meat, so carbon taxes hit it hardest — and Denmark’s policies don’t even cover chicken or fish. When the price of beef rises, consumers mostly shift to other meats like chicken. And replacing beef with chicken means more animals suffer in worse conditions — about 190 chickens are needed to match the meat from one cow, and chickens are raised in much worse conditions. It may be possible to design carbon taxes which avoid this outcome; a recent paper argues that a broad carbon tax would reduce all meat production (although it omits impacts on egg or dairy production). But with cows ten times more emissions-intensive than chicken per kilogram of meat, other governments may follow Denmark’s lead — focusing taxes on the highest emitters while ignoring the welfare implications. Beef is easily the most emissions-intensive meat, but also requires the fewest animals for a given amount. The graph shows climate emissions per tonne of meat on the right-hand side, and the number of animals needed to produce a kilogram of meat on the left. The fish “lives lost” number varies significantly by