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In this post, I hope to examine the Bayesian Adjustment paradigm presented by Holden Karnofsky of Givewell from a mathematical viewpoint, in particular looking at how we can rigorously manage the notion of uncertainty in our models and the stability of an estimate. Several recent posts have touched on related issues.
In practise, we will need to have some substantive prior on the likely range of impacts that interventions can achieve, and I will look briefly at what kinds of log-ranges are supported in the literature, and the extent to which these can preclude extreme impact scenarios. I will then briefly look at less formal notions of confidence in a model, which may be more tractable either computationally or for heuristic purposes than a formal bayesian approach.
I am highly grateful to Alexey Morgunov and Adam Casey for reviewing and commenting on an earlier draft of this post, and pestering me into migrating the content from many emails to a somewhat coherent post.
Will Crouch has posted about the Centre for Effective Altruism and in a follow up post discussed questions in more detail. The general sense of the discussion of that post was that the arguments were convincing and that donating to CEA is a good idea. Recently, he visited Cambridge, primarily to discuss 80,000 hours, and several Cambridge LWers spoke with him. These discussions caused a number of us to substantially downgrade our estimates of the effectiveness of CEA, and made our concerns more concrete.
This post is part of the Cambridge LW meetup group's attempt to publish what works for us, and try to make good meetups easier.
Breaking the ice and topic selection
A consistent problem has been starting discussion, and more generally breaking the ice. Last week, an Execute by Default style hack was used to reduce social inhibitions (everyone danced for 30 seconds), which was highly successful, though awkward. It was proposed again this week, and there was sufficient collective laughter at the recollection to effectively break the ice. This may also have been helped by a change in room, which replaced chairs with couches.
A new algorithm for selecting a topic was used: One person proposed a (deliberately easy-to-beat) topic, and running around the group, each person proposed a alternate topic or passed. This was followed by multiple passes for people to affiliate with any proposed topic. Amongst 7 people, the first pass produced a 5-2 split, and the group of two merged into the main topic.
The topic chosen was involuntary signalling. The others are here so as to keep them salient for future meetups.
Signalling by Dress
It was observed that most people seem to react to dress, and that as a group (largely mathematicians or similarly inclined) there is a tendency not to optimise the reactions we generate. Several people asked what might work better, and checked to see whether the social status of others in the social group of mathematicians correlated with their appearance or dress. It appeared that if it did, we are insufficiently good at observing our cognitive processes to notice. As a corollary, it wasn't clear that feedback from other members of the group was likely to contain much signal.
A concrete mechanism to extract information on how other people perceive dress was made: Generate multiple photos in various styles, and then use OKCupid's "MyBestFace" or similar services to get some information back
Signalling for Access
There was some discussion of how one might present in interview; this was confounded by a lack of access to interviewers. Discussion was more productive when moved to aspects of social engineering. Specific examples raised were accessing a hospital outside of visiting hours, entering a college without being challenged by porters, or avoiding inconvenience in airports. A combination of speed, posture (head level, back straight, shoulders back) and contextual dress was the extent of noted tricks.
Signalling by Posture
Considerable time was spent discussing how posture signals. Some people went around the group, saying what they would draw from other people's body language. Some postural changes were noted as very saliently causing a change in perception of the correctness of statements made at the same time (in particular, straightening the back and lifting the head). Extant scholarship was not discussed, but extensive experimentation occurred targeting specific received signals and querying specific postures. The dynamics of norm violation were also discussed, in the context of taking the communal coffee table as a footrest.
Specific suggestions to use a mirror or camera to analyse oneself or attempt to analyse other people in general were made.
All of the public commitments made last week were done, which seemed to be a cheap win. We reran the procedure:
- Jonathan: Post meetup feedback etc. by midnight
- Adam: Get last two years of past papers done by next Sunday
- Adam: Email parents by Wednesday midnight
- Ben: Finish list of definitions by next Sunday
- Ben: Continue Diary until next Sunday
- Concoct childish example of Bayes' Theorem (to motivate better alternatives)
- Self-sabotage, noticing and avoiding.
- Fermi estimate game
- Examine week 1 of Ben's diary to try to help in debiasing.
- Non-real valued utility functions
Discussion article for the meetup : First meetup in Budapest
Meeting at California Coffee Company Basilica (coffee shop), Szent Istvan ter 4-5. http://www.californiacoffeeco.net/?page_id=50〈=en.
Please come and bring friends. If you have questions, contact AlexeyM.
Discussion article for the meetup : First meetup in Budapest
Response to Beauty quips, "I'd shut up and multiply!"
This is somewhat introductory. Observers play a vital role in the classic anthropic thought experiments, most notably the Sleeping Beauty and Presumptuous Philosopher gedankens. Specifically, it is remarkably common to condition simply on the existence of an observer, in spite of the continuity problems this raises. The source of confusion appears to be based on the distinction between the probability of an observer and the expectation number of observers, with the former not being a linear function of problem definitions.
There is a related difference between the expected gain of a problem and the expected gain per decision, which has been exploited in more complex counterfactual mugging scenarios. As in the case of the 1/2 or 1/3 confusion, the issue is the number of decisions that are expected to be made, and recasting problems so that there is at most one decision provides a clear intuition pump.