Comment because this is answering a different question than “should I use wrist weights”
I have found that a weight vest is a nice improvement to the game. I’d recommend trying it, and it possibly might have some of the common benefits with the wrist weights without some of the downsides.
I’m surprised this hasn’t got more comments. Julian, I’ve been incredibly impressed by your work in RL so far, and I’m super excited to see what you end up working on next.
I hope folks will forgive me just putting down some opinions about what problems in RL to work on:
I think I want us (RL, as a field) to move past games—board games, video games, etc—and into more real-world problems.
Where to go looking for problems?
These are much harder to make tractable! Most of the unsolved problems are very hard. I like referencing the NAE’s Engineering Grand Challenges and the UN’s Sustainable/Millennium Development Goals when I want to think about global challenges. Each one is much bigger than a research project, but I find them “food for thought” when I think about problems to work on.
What characteristics probably make for good problems for deep RL?
1. Outside of human factors—either too big for humans, or too small, or top fast, or too precise, etc.
2. Episodic/resettable—has some sort of short periodicity, giving bounds on long-term credit assignment
3. Already connected to computers—solving a task with RL in a domain that isn’t already hooked up to software/sensors/computers is going to be 99% setup and 1% RL
4. Supervised/Unsupervised failed—I think in general it makes sense only to try RL after we’ve tried the simpler methods and they’ve failed to work (perhaps the data is too few, or labels too weak/noisy)
What are candidate problem domains?
Robotics is usually the first thing people say, so best just get it out of the way first. I think this is exactly right, but I think the robots we have access to today are terrible, so this turns into mostly a robot design problem with a comparatively smaller ML problem on top. (After working with robots & RL for years I have hours of this but saving that for another time)
Automatic control systems is underrated as a domain. Many problems involving manufacturing with machines involve all sorts of small/strange adjustments to things like “feed rate” “rotor speed” “head pressure” etc etc etc. Often these are tuned/adjusted by people who build up intuition over time, then transfer intuition to other humans. I expect it would be possible for RL to learn how to “play” these machines better and faster than any human. (Machines include: CNC machines, chemical processing steps, textile manufacture machines, etc etc etc)
Language models have been very exciting to me lately, and I really like this approach to RL with language models: https://openai.com/blog/fine-tuning-gpt-2/ I think the large language models are a really great substrate to work with (so far much better than robots!) but specializing them to particular purposes remains difficult. I think having much better RL science here would be really great.
Some ‘basic research’ topics
Fundamental research into RL scaling. It seems to me that we still don’t really have a great understanding of the science of RL. Compared to scaling laws in other domains, RL is hard to predict, and has a much less well understood set of scaling laws (model size, batch size, etc etc). https://arxiv.org/abs/2001.08361 is a great example of the sort of thing I’d like to have for RL.
Multi-objective RL. In general if you ask RL people about multi-objective, you’ll get a “why don’t you just combine them into a single objective” or “just use one as an aux goal”, but it’s much more complex than that in the deep RL case, where the objective changes the exploration distribution. I think having multiple objectives is a much more natural way of expressing what we want systems to do. I’m very excited about at some point having transferrable objectives (since there are many things we want many systems to do, like “don’t run into the human” and “don’t knock over the shelf”, in addition to whatever specific goal).
Trying to find some concrete examples, I’m coming up short.
I’m sorry I didn’t meet the recommendation for replies, but glad to have put something on here. I think this is far too few replies for a question like this.
It might be useful for every person responding to attempt to define precisely what they mean by human level AGI.
In case other folks would be interested, here is the trailer on youtube: https://www.youtube.com/watch?v=micrLvzThs8&feature=emb_title
Not obvious from the review (to me): it’s a fictional drama about a conflict between humans and a rogue AI.
I don’t really have much but this is at least from last year:
Steve Hsu discusses Human Genetic Engineering and CRISPR babies in this (the first?) episode of the podcast he has w/ Corey Washington https://manifoldlearning.com/podcast-episode-1-crispr-babies/?utm_source=rss&utm_medium=rss&utm_campaign=podcast-episode-1-crispr-babies
Transcript: https://manifoldlearning.com/episode-001-transcript/
Sharing an idea that came to mind while reading it, low confidence.
Maybe “forming great cultures” is really just the upper tail of “forming cultures”—the more cultures we form, the more great cultures we get.
In this case the interesting thing is tracking how many cultures we form, and what factors control this rate.
I think over the timescales described, humans haven’t really gotten much more interesting to other humans. Humans are pretty great, hanging out together is (to many) more fun and exciting than hanging out alone.
A difference could be that the alternatives have been getting more and more interesting—wandering in the woods is pretty but also boring. Walking around town might be less boring. Reading a book less boring still. Listening to music is better for some. The internet has created a whole lot of less-boring activities. Somewhere in there we crossed a threshold for “forming cultures” becoming less and less interesting.
This is basically the idea “we form cultures when we get bored, and we’re less bored”.
But let’s say I personally find the idea of starting a culture exciting, does this still affect me?
I think ‘yes’, because the people I try to recruit for participating in my culture will also have to choose between joining me and the BATNA.
Things that would update me against this: models that show “starting a culture” continues to be exciting, models that show “hedonic setpoint” reasons for the ‘BATNA gets better’ idea to be broken, evidence that more cultures are being started now than ever before.
Of all the things here I think the idea I’m most interested in inspecting is “formation of great cultures tracks formation of cultures in general”.
This is probably better put in another post, but I think I agree with your read of the situation and recommendations, and want to follow it with the (to me) logical next step: “how to get some of the good things we could get by moving, without moving”
I like this post because it does talk about a bunch of things that could be got (sense of safety, isolation from political unrest, etc).
It seems not-easy and also not-impossible to brainstorm ways of dealing with this as a community in a sane and cost-effective way.
One way this could go: (sketch of a vision)
Right now SF and most cities have a bunch of “earthquake disaster preparation” advice. Things you should have on hand, ready to go, plans you should have made ahead of time, people to contact in case communications go down, things to do to prepare your home structure, attach your furniture to the walls, etc.
We could make some community version of that, pointed directly at the things we want to point at.
Thanks for writing this up! I think having more well-researched and well-written dives into things like this are great
A bunch of scattered thoughts and replies to these:
Overall I agree with the central idea (Nuclear War is unlikely to cause human extinction), but I disagree enough with the reasoning to want to hammer it into better shape.
Writing this I feel like in a “editing/constructive feedback” mood, but I’d welcome you to throw it all out if its not what you’re going for. To the feedback!
This seems to only consider current known nuclear weapons arsenals. It seems worth including probabilities that different kinds of weapons are built before such a war. In particular, longer-lived species of bombs (e.g. salt bombs, cobalt bombs, etc)
I think I want to separate “kill everyone with acute radiation right away” and “kill everyone with radiation in all of the food/water”, and the latter seems less addressed by the energy/half life argument. I think the weapon design space here is pretty huge, so ruling these out seems hard to me. (Though I do think if we’re assigning probabilities, they should get lower probabilities than conventional weapons)
In general I would prefer approximate numbers or at least likelihood ratios for what you think this evidence balances out to, and what likelihood of odds you would put on different outcomes.
(For example: “what is the likelihood ratio of the 3.C evidence that nuclear war planners are familiar with ideas like nuclear winter”—I don’t think these are strictly required, but they really help me contextualize and integrate this information)
In particular, Toby Ord gives a bunch of excellent quantitative analysis of X-risks, including nuclear war risk, in The Precipice.
(In fact, if your main point of the post was to present a different model from that one, adding numbers would greatly help in comparing and contrasting and integrating them)
Finally, I think my mental models of case 3) are basically the same as any event that is a significant change to the biosphere—and it seems reasoning about this gets harder given your premise.
A hypothetical: if there are 3 major climate events in the next 100 years (of which one is a bellicose nuclear exchange), and humanity goes extinct due to climate related symptoms, does the nuclear war “cause” the human extinction in a way you’re trying to capture?
Maybe what I want is for the premise to be more precise: define a time limit (extinct within X years) and maybe factor what it means to “cause” (for example, it seems like this suggests that an economic collapse triggered by an nuclear war, which triggers other things, that eventually leads to extinction, is not as clearly “caused by nuclear war”)
Also maybe define a bit what “full-scale” means? I assume that it means total war (as opposed to limited war), but good to clear up in any case.
That’s all that came to mind for now. Thanks again for sharing!~
Can LessWrong pull another “crypto” with Illinois?
I have been following the issue with the US state Illinois’ debt with growing horror.
Their bond status has been heavily degraded—most states’ bonds are “high quality” with the standards agencies (moodys, standard & poor, fitch), and Illinois is “low quality”. If they get downgraded more they become a “junk” bond, and lose access to a bunch of the institutional buyers that would otherwise be continuing to lend.
COVID has increased many states costs’, for reasons I can go into later, so it seems reasonable to think we’re much closer to a tipping point than we were last year.
As much as I would like to work to make the situation better I don’t know what to do. In the meantime I’m left thinking about how to “bet my beliefs” and how one could stake a position against Illinois.
Separately I want to look more into EU debt / restructuring / etc as its probably a good historical example of how this could go. Additionally previously the largest entity to go bankrupt in the USA was the city of Detroit, which probably is also another good example to learn from.
Looking at this more, I think I my uncertainty is resolving towards “No”.
Some things:
- It’s hard to bet against the bonds themselves, since we’re unlikely to hold them as individuals
- It’s hard to make money on the “this will experience a sharp decline at an uncertain point in the future” kind of prediction (much easier to do this for the “will go up in price” version, which is just buying/long)
- It’s not clear anyone was able to time this properly for Detroit, which is the closest analog in many ways
- Precise timing would be difficult, much more so while being far away from the state
I’ll continue to track this just because of my family in the state, though.
Point of data: it was 3 years between Detroit bonds hitting “junk” status, and the city going bankrupt (in the legal filing sense), which is useful for me for intuitions as to the speed of these.
I really appreciate how this and the previous posts does a lot to describe and frame the problems that moving would solve, such that it’s possible to make progress on them.
I think it’s harder to clearly frame the problem (or clearly break a big/vague problem into concrete subproblems).
Anyways, some babbling:
Home
Redecorate or remodel
Arrange a furniture swap with friends and neighbors
Marie kondo your stuff
Give friends a virtual tour of your space and ask what they would change
Ask your friends for virtual tours of their spaces for ideas
Have a garage sale / get rid of a bunch of stuff
Buy land
buy a house
buy a condo
Work
Ask for a raise
Ask for a promotion
Interview at other companies
Get career coaching advice (80k / EA folks are pretty practiced at this!)
Give career coaching advice (showing up, as a professional in my 30s, to EA / early career events has been more fulfilling than I thought it would be)
Go to conferences in your field
Go to conferences in the field you want to be in (strongly recommend this)
Join vocational groups (meetups, etc) to connect with folks in your field
Give presentations about your job/work at vocational groups
Try moving around weekend days (wednesday/sunday?)
Try working from home (okay this would probably have been more useful pre-pandemic)
Change up your commute (bike or walk or public transit or drive or motorcycle)
Join a carpool with people you think are cool to be in cars with
Start a carpool with people you think are cool to be in cars with
Take an online course (bonus: get your work to pay for it)
Get a professional degree (online or night school or whatever)
Relationships (mostly not romantic)
Make it easy for other people to schedule 1:1s with you (calendly / etc)
Reach out to possible mentors you would want to have
Reach out to possible mentees (many people I know could be great mentors but are too uncertain to take the first step towards mentorship themselves)
Retire a mentor of yours that you’re no longer getting lots of value out of (but maybe still be friends?)
Start a book club
Start a paper reading group
Start a running group or gym group
Host more parties (post-vaccine)
Go to more parties (post-vaccine)
Join a monastery / religion / church / etc (not for everyone)
Start a religion / etc (I think the anti-digital movement has room for a neo-luddite group to coalesce, but thats a separate post)
Other
Find a counselor or therapist
Turn off your internet after dark
Go on a 10 day silent retreat
Spend a month in an RV
Travel much more often (for me this could be “travel at least 3 days every month” but for others could vary)
Future City Idea: an interface for safe AI-control of traffic lights
We want a traffic light that
* Can function autonomously if there is no network connection
* Meets some minimum timing guidelines (for example, green in a particular direction no less than 15 seconds and no more than 30 seconds, etc)
* Secure interface to communicate with city-central control
* Has sensors that allow some feedback for measuring traffic efficiency or throughput
This gives constraints, and I bet an AI system could be trained to optimize efficiency or throughput within the constraints. Additionally, you can narrow the constraints (for example, only choosing 15 or 16 seconds for green) and slowly widen them in order to change flows slowly.
This is the sort of thing Hash would be great for, simulation wise. There’s probably dedicated traffic simulators, as well.
At something like a quarter million dollars a traffic light, I think there’s an opportunity here for startup.
(I don’t know Matt Gentzel’s LW handle but credit for inspiration to him)
+1 Plan 9.
I think it (weirdly) especially hits a strange place with the “forgotten” mark, in that pieces of it keep getting rediscovered (sometimes multiple times).
I got to work w/ some of the Plan 9 folks, and they would point out (with citations) when highly regarded papers in OSDI had been built (and published) in Plan 9, sometimes 10-20 years prior.
One form of this “forgotten” tech is tech that we keep forgetting and rediscovering, but:
maybe this isn’t the type of forget the original question is about, and
possibly academia itself is incentivizing this (since instead of only getting one paper out of a good idea, if it can get re-used, then that’s good for grad students / labs that need publications)
Thinking more about the singleton risk / global stable totalitarian government risk from Bostrom’s Superintelligence, human factors, and theory of the firm.
Human factors represent human capacities or limits that are unlikely to change in the short term. For example, the number of people one can “know” (for some definition of that term), limits to long-term and working memory, etc.
Theory of the firm tries to answer “why are economies markets but businesses autocracies” and related questions. I’m interested in the subquestion of “what factors given the upper bound on coordination for a single business”, related to “how big can a business be”.
I think this is related to “how big can an autocracy (robustly/stably) be”, which is how it relates to the singleton risk.
Some thoughts this produces for me:
Communication and coordination technology (telephones, email, etc) that increase the upper bounds of coordination for businesses ALSO increase the upper bound on coordination for autocracies/singletons
My belief is that the current max size (in people) of a singleton is much lower than current global population
This weakly suggests that a large global population is a good preventative for a singleton
I don’t think this means we can “war of the cradle” our way out of singleton risk, given how fast tech moves and how slow population moves
I think this does mean that any non-extinction event that dramatically reduces population also dramatically increases singleton risk
I think that it’s possible to get a long-term government aligned with the values of the governed, and “singleton risk” is the risk of an unaligned global government
So I think I’d be interested in tracking two “competing” technologies (for a hand-wavy definition of the term)
communication and coordination technologies—tools which increase the maximum effective size of coordination
soft/human alignment technologies—tools which increase alignment between government and governed
I don’t know if he used that phrasing, but he’s definitely talked about the risks (and advantages) posed by singletons.
1. What am I missing from church?
(Or, in general, by lacking a religious/spiritual practice I share with others)
For the past few months I’ve been thinking about this question.
I haven’t regularly attended church in over ten years. Given how prevalent it is as part of human existence, and how much I have changed in a decade, it seems like “trying it out” or experimenting is at least somewhat warranted.
I predict that there is a church in my city that is culturally compatible with me.
Compatible means a lot of things, but mostly means that I’m better off with them than without them, and they’re better off with me than without me.
Unpacking that probably will get into a bunch of specifics about beliefs, epistemics, and related topics—which seem pretty germane to rationality.
2. John Vervaeke’s Awakening from the Meaning Crisis is bizzarely excellent.
I don’t exactly have handles for exactly everything it is, or exactly why I like it so much, but I’ll try to do it some justice.
It feels like rationality / cognitive tech, in that it cuts at the root of how we think and how we think about how we think.
(I’m less than 20% through the series, but I expect it continues in the way it has been going.)
Maybe it’s partially his speaking style, and partially the topics and discussion, but it reminded me strongly of sermons from childhood.
In particular: they have a timeless quality to them. By “timeless” I mean I think I would take away different learnings from them if I saw them at different points in my life.
In my work & research (and communicating this) -- I’ve largely strived to be clear and concise. Designing for layered meaning seems antithetical to clarity.
However I think this “timelessness” is a missing nutrient to me, and has me interested in seeking it out elsewhere.
For the time being I at least have a bunch more lectures in the series to go!
This is my favorite kind of lesswrong post—a quick rationality technique that I can immediately go try and report back on.
I was able to prototype it quickly in my notes list by using a dedicated symbol as the marker. It looks like any weird/unused symbol could be used as this. Seems like a quick hack to work with any digital list (I used §).
Question about non-stationarity: How often is the “stable” prerequisite violated in practice?
E.g. if a bunch of items are physically exhausting, and a bunch are not, I might want to not do physically exhausting items in sequence. I didn’t run into this personally in my tiny trial, so at least the answer isn’t “all the time”.
Sure!
It seems like the prerequisite assumptions are likely to be violated sometimes (in general most assumptions aren’t total rules).
My question is about the rate of violations to this prerequisite assumption.
A few ways to cut at it (feel free to answer just one or none of them):
When going through a list subsequent times, how often do you notice/feel internally that your views on a past item have shifted?
How often do you make a new list and start the process anew, even though you have an existing list that could be continued on?
How often do you go back and erase or modify marks on a list while using this process?
I think I find my internal experience (and relation to stuff on my to-do list) changes pretty significantly over the course of a day.
Epistemic status: I’m not really an expert at NLP. I’ve only been working on language modeling for ~8mo, which is much less than some of the folks here, and this is based on my experiences.
Beam Search:
Beam search with large unsupervised generatively pretrained transformers (GPTs) is weirder than it appears in the NLP literature. Other commenters have mentioned degeneracies, but for me the sticking points for beam search were:
It tends to quickly fall on a modal response — so it’s already bad for any sort of situation you want to generate a diversity of samples and choose the best from
It’s hard to correctly score between varying-length segments. Every paper that uses beam search has some heuristic hack here, which is almost always some parametrized function they pulled from another paper or hacked together.
It seems to mostly do best (once tuned) at some narrow/specific distribution (e.g. generating short responses in a chat setting). It’s hard to get beam search tuned to work well across the full distribution used to train these models (i.e. “text on the internet”)
Given these three issues, in my experience it’s been better to just focus on tuning naive sampling, with a few key parameters: temperature, top_p, etc (these are part of the OpenAI API).
Caveat: it’s possible I’m just bad at tuning beam search. It’s possible I’m bad at scholarship and missed the “one key paper” that would make it all clear to me. I would take the above as more of an anecdote than a scientific result.
Separation of training and sampling:
This has been mentioned by other commenters, but might bear repeating that there is no sampling at all in the training process for GPTs. They’re trained to approximate marginal next token distributions, and the default is to share the loss on the prediction for every token equally. In practice the loss on later tokens is lower.
All of this is saying that training is a separate process for sampling. I think there is probably very good research to be done in better sampling — in particular, I think it is possible to have a machine which aligns sampling from an unaligned model.
Lookahead & pondering:
I think the point about lookahead is still worth considering. One of the differences between transformers and the previous most-popular architecture for language models (LSTMs) is that transformers use the same amount of compute for every token. (It’s possible to build them otherwise, but I haven’t seen any of these that I’ve been impressed by yet)
I think my favorite example of this in the literature is Adaptive Computation Time (ACT)[https://arxiv.org/abs/1603.08983], where essentially the model learns how to “spend” extra compute on certain characters.
(One of the things going on with ACT is dealing with the non-uniformity of the distribution of information content in character strings — for GPTs this is at least partially ameliorated by the byte-pair encoding)
So I think it is reasonable to train a model to be able to use extra “pondering” time when sampling. Either by having an external controller that tells the model when to ponder and when to output, or by having the model learn itself how to ponder (which is the “halting neuron” signal in ACT).
I do think that any sort of pondering is subject to mesa-optimization concerns.
Fix 1 - BERT:
Caveat: I haven’t trained BERT models or taken a trained one and tried hard to get high quality samples from it. This is based on intuitions and hearsay.
Here I’ll use “GPT” to refer to autoregressive next token prediction objectives, to mirror the style of the article. This objective can of course be used with other architectures in other settings.
Instead of thinking the “mask-part-out prediction” (BERT) and the “mask future text” (GPT) as two separate tasks, think of them as points in the space of distributions over masks.
In particular, its trivial to come up with mask distributions that include both a preponderance of masks which leave small parts out (BERT-like) and masks which leave future tokens out (GPT-like) as well as possibly other mask patterns.
My intuition is that the higher probability you mask out all future tokens, the easier it is to get high quality samples from that model.
Fix 1 - Editing Text:
(Same caveat as above regarding inexperience w/ BERT models)
BERT objectives by themselves do not allow efficient text editing, and neither do GPT objectives.
Thinking about the task of composing an edit you, the model needs to:
Identify the section that will be removed (if any)
Figure out the length of the replacement text (if any)
Compose the replacement text (if any)
Possibly also have some way of attending over the old text, while still knowing to replace it
Neither BERT nor GPT objectives do a great job of this by itself. If I had to choose, though, I think you can encode this sort of thing in the GPT dataset and have it autoregressively generate edits.
(This is part of a conjecture I’ve been meaning to writeup for lesswrong of “the dataset is the interface” for GPT models)
Fix 2 - Changing the training:
I think there’s some interesting stuff here, but so far this is in the regime of training algorithms that are unexplored, enormously complex, and poorly understood.
The clearest part here is that it uses sampling in the training loop which so far I’ve almost exclusively seen in reinforcement learning (RL).
But, we can probably implement something like this with RL. In particular, training is a process of selecting a context (masking), sampling from the model to fill in the mask, and scoring based on the objective.
In this case, drawing some analogies to RL:
Action—token
Action distribution—token distribution (the basic output of a GPT model given an input context)
Policy—language model (in particular a GPT model, though with hacks BERT/other models could be used)
Reward—objective (log-loss on the true document, for a GPT model)
Environment—a document, probably with some starting context already provided
It’s pretty easy to see here that this wouldn’t work well from generating from scratch. If I provide zero contextual tokens to the model, sample N tokens, and then score it on how close it got to a true (hidden) document, I am going to have a very bad time.
This might be a good approach for fine-tuning a GPT model — which is (exactly what some colleagues did)[https://openai.com/blog/fine-tuning-gpt-2/].
Even in the fine-tuning case, we have all of the myriad and sundry problems with RL (instability, inefficiency, etc) that our plain-and-simple language modeling objective lacks.
Fix 2 - update away:
I think this probably won’t work just from experience. I’ve found it very hard to get the model to “reduce your probability on the most likely outcome and increase your probability on the next most likely outcome” — instead objectives like this tend to just increase the temperature of everything (or worse, it puts all of the increase in entropy in the long tail of bad answers).
It’s possible there is a good way to do this, but for now I don’t know of a good way to get a model to increase the probability of “secondary options” without just degenerating into increasing entropy.
Fix 2 - track updates:
If I understand this correctly, I think this is easily approximated by having an objective/loss/reward term which penalizes differences from the original model. For small deltas I think this is a good approach, and unfortunately is only as good as the original model you’re comparing it too.
As far as the specific proposal for managing updates towards/away from beam search updates, that seems also possible via a similar mechanism — penalize distributional difference from those samples.
I think we haven’t really explored these sort of penalties enough, and in particular how they interact when combined with other objectives.
Fix 2 - will it stunt:
I think that any objective that scores better predictions higher will incentivize some sort of lookahead/pondering.
If you prevent it from being coincident with the beam search distribution, then I expect the model will learn how to do lookahead/pondering in the null space of beam search.
Will these solve mesa-optimization:
This isn’t clear to me, but I think it’s worth studying.
In particular, it would be good to figure out some way of contriving a mesa-optimization setup, such that we could measure if these fixes would prevent it or not.
Beam Search in the API:
I think my above comments about Beam Search apply here.
Beam search, like any optimization algorithm, is hugely dependent on its scoring function. If you score on likelihood, you’ll end up with high-likelihood (“unsurprising”) text.
Future thoughts—sampling research:
I think in general we’re in a weirdly asymmetric world, where we have a huge amount of compute and effort into computing auto-regressive next token distributions, and comparatively very little sophistication in sampling from them.
This comment is probably too long already for me to expand too much on this, but in particular, I think the log-likelihood objective is default unaligned (as most datasets are default unaligned) but I think we can find ways of sampling from log-likelihood optimized models in ways that are aligned.
I’m pretty surprised this entire argument goes without using any amount of quantitive modeling or data analysis.
I do think it presents a bunch of persuasive and philosophical arguments in the direction of your conclusions, but it’s easy to imagine (and find, searching on the internet) persuasive and philosophical arguments in the opposite direction.
(Caveat: I’m a bit new to this forum and how things work, but surely for folks here this is better answered by building a model and incorporating uncertainty?)
A few of the specifics you give I’ve found are not borne out in the research I’ve done (e.g. price sensitivity has more to do with location/centrality than it does with luxury, though more luxurious homes tend to be built farther from city centers). This could just be from different sources, but I’m noticing want to wave several large [CITATION NEEDED] flags.
Also maybe it’d be useful to share the quantitative analysis I’ve done? Basically “modeling the financial, legal, and social implications of buying a house together” has been the biggest project of mine for 2019 outside of work, but I’m not an expert (most of us in my house, myself included, are first time home buyers). I’d consider myself better informed than the average person who has not owned a house for an extended period of time, but would be very interested in learning more and learning where my models are bad.
For interested folks I found Shiller’s Irrational Exuberance to give a bunch of nice solid models (backed with data!) on speculative pricing bubbles in ways that seem to apply to SF bay area housing in particular.