Personality Vectors (2019)
yangvincent.com
yangvincent.com
Even more, since for questions there are:
- questions one decided to answer (which says something on its own)
- answers
- declared answers they accept in their partners
- actual partners they pursue (judged by matches, or dates)
While I expect that mostly similarities attract (so-called associative mating), there are compatible traits (e.g one person loves to listen, one person loves to talk), and there is the level of lack of self-knowledge, or hypocrisy (what we SAY we like, vs what we actually do).
And then e.g. probability that person A likes person B can be expressed as:
sigmoid(actualPrefVecA * personalityVecB)
...and with gradient descent magic, we can turn people into vectors!
https://twitter.com/pmigdal/status/1268959822378082304
It used to be much more informative, especially as the answers are weighted. Of course, a much more informative approach would be to use update weights from data.
Also, when it comes to your example:
- I know people with a deep believer and a lukewarm one (I don't take for granted that it is a more important question than if someone enjoys horror movies),
- for important matters (e.g. whole conservative-religious-right-traditional) it is not a single answer but many, many.
Also, usually, there are much fewer deal-breakers than one initially states (vide https://www.ted.com/talks/amy_webb_how_i_hacked_online_datin...). Again, looking at data (vs assuming what is important) would be a big deal.
And as you point out, that makes the notion of a dealbreaker a fairly nuanced one - e.g. I might tell myself that I would never date someone who doesn't want kids because I do want kids, so why would I waste my time? On a website that shows these preferences, I will likely not engage with profiles who mention that they don't want kids, and ignore their messages.
But of course when the new guy shows up at work and he turns out to be really cute and interesting, I may be interested in him/we may start a romantic relationship before these preferences become apparent.
Which can then go in any number of directions - the relationship might fizzle out really fast. Or it may engender personal change for the partners. Or it might lead to many decades of bitter resentment. Who knows! This could make the rational argument that one should accept to chat with anyone on a dating profile regardless of their displayed preferences, but of course that's not how humans tend to reason.
Also, it is not only about personality. People make much stronger judgments on someone's else appearance: https://priceonomics.com/online-dating-and-the-death-of-the-...
> Rudder even pinpoints the two OkCupid profile questions that best predict a couple's longevity if they both answer the same way: Do you like horror movies? And, have you ever traveled to another country alone? [0]
[0] https://www.thedailybeast.com/heartache-by-the-numbers-and-o...
You might enjoy https://psycnet.apa.org/record/1982-01296-001 (taken from the comments from the same post but on reddit). tl;dr: proposition that social relations follow a logarithmic trend
Lots of overlap among these, but rather than start from scratch, a bit of reading in almost any of them, perhaps starting with behavioral economics or social psych, might enhance the "vectors".
There’s a term “off-kilter”, which is easy to explain using vectors like this.
If we take the general vector of society, just sum up all personal vectors and normalise, we form a big vector for society. This is what’s “normal”.
Kilter refers to the concept of how aligned we are with society’s vector. So the concept of being off-kilter is how skewed you are wrt this normal vector essentially.
Of course valid for any of the eigenvectors corresponding to subfields again, and this also goes some way to help form the overton window, which has recently been up here in some posts...
It’s a fun sociomathematical formulation.
Society feels like multiple interacting vector fields ala magnetic and electric fields. But instead of 2 fields there are probably many...personality, knowledge, energy, needs etc. And you exist as some charged particle (negative or positive?) thrown into the middle of all that dynamic chaos being pushed and pulled in various directions.
theoretically if you add the value-vector of all people together you end up with a long vector that represents the direction of society (even the opposing ones), and you can then normalize this to have the unit vector of society. i.e. what is the "direction of society".
Your skew wr.t. this one (inner product with, or projection on to this) will be some number between -1 and 1 (if we account for opposition I guess)
Basically if both vectors are of unit length, you get the cos(angle between). Completely off kilter would be a score of 0. While "opposed" would be -1
No, you can't.
Also, people don't have "preferences" in the way the article posits
Honestly, this article is fascinating to me for reasons that might discomfit its author.
Not that ignorance of the last 300 years of philosophy on this topic is something necessarily to be ashamed of, but this essay, and the reaction to it here, is of interest to me because (a) this is not an empirically-validated, or even merely well-argued, approach to reasoning about people, but (b) seems to be thought well of anyway, reading the room in this comments section.
I understand better the class of ontological and epistemological elisions system-thinking folks tend not to 'see', and I am reminded of the (now thankfully bygone) era of analytic philosophy where you couldn't make an argument in a respectable journal without translating your propositions into a symbolic logic (your choice of which).
An excellent kettleball to level up with. It's like geocaching but with unsupported assertions.
I fully admit I know nothing of the actual research in psychology/related fields, and this is just my current understanding built up from when I was a kid (completely anecdotal). I'm primarily interested in people's thoughts in general (both good and bad) as well as where I might be able to improve my understanding with the research that's actually been done.
"Preferences" here might be more accurately stated as tendencies with some motive.
If you need interpretability just use an autoencoder or simese network instead of PCA. This way the new "personality" vectors are highly meaningful and visualizations are better...
If we had a Chatbot that would interact with someone hypothetically we could have this kind of distinction between Personality Vectors to mutate its own behavior and to interact with others.
Its sort of the idea I had when I watched the movie Interstellar and TARS is told to decrease his own humor. So if the system would just know who its talking to it would create a preference vector for each person it interacts with.
But you would need some kind of baseline so basically the Personality Vectors would be set to neutral. And each interaction with a Personality vector would be a system that stores a preference vector for each person.
As the system time evolves (which is also in the article) you would basically end up with a set of graphs. There is a notion out there which is called a Dynamic Network analysis [1]. If you also stored the history of that you could then not just be able to rewind the system backwards to a previous checkpoint.
The way you could start learning how to learn a personality vector is a reinforcement learning situation. If you say "bad AI I didn't like that" the system would then look at what it said and mutate the preference vector for that person. Or you could just change the vector manually by a technique that was illustrated above by just saying "reduce humor by 70%".
Then you could have model which would do conditional generation [1] of text based upon interactions with others. It would be basically a reinforcement learning system paired with a generative text model. The reinforcement system would store the Personality and Preference vectors for each person. For each behavior and preference you would need a corpus to bootstrap the system. The reinforcement system would be a retrieval network and it would retrieve a pertained system based on user(s) input and get a generational text network to provide a response. This would be similar to Alpha Go's design. [3]
[1] https://en.wikipedia.org/wiki/Dynamic_network_analysis [2] https://github.com/salesforce/ctrl [3] https://datascience.stackexchange.com/questions/10932/differ...
For those of you who are interested, a good introduction can be found here : https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.30...
- HIPSTERS: If you really like something, but don't want others to crowd your interests, you will pretend to be nonchalant about it.
- AUTHENTICITY: A person's interests in a topic can be vetted as "authentic" if they are influenced by others whose average of personality vectors matches the person's.
- SOCIOPATHS: You can make in-roads with a group of interest by transforming your PV; see AUTHENTICITY.
- SKEW: You can influence others more by projecting a _very_ high value for a specific topic. If you love a particular form of music to an absurd degree, you're more likely to convert others.
I don't have any non-anecdotal evidence of any of these phenomena. Rather, these are some reasonable if not amusing deductions from the author's model.