The only app you will ever need is "Google Sheets".
I started using it ~3 years ago as a time-sheet to track my work, activities, sleep, mood etc. I tried to do what you did - see how X impacts my mood and look for trends. Here's some of my findings over the past three years in that regard:
You learn by doing it.
Find someone you like (or who likes you) and start dating them!
Without dating apps:
I had this same thought recently regarding friendship and connection as "divide and conquer" for understanding the world.
WRT why people cling to collective worldviews despite contrary evidence... one answer lies in interesting work in Social Psych called "Terror Management Theory."
Basically, people seek to avoid death, but we all know that we will die. To avoid being paralyzed by this fear, we are socialized by our parents & society into worldviews that grant us "immortality" of sorts: religion, prestige, being "goo...
Many of the other comments deal with thought experiments rather than looking at the reality of how "many worlds" is USED. From my point of view as a non-physicist it seems to primarily be used as psuedo-science "woo" - a revival of mystery and awe under the cloak of scientific authority. A kind of paradoxical mysticism for non-religious people, or fans of "science-ism".
An agent might act differently from MISUNDERSTANDING many worlds theory. Or by paying more attention to it. Psychological "priming" is real ansd powerful.
The answer by TAG below is case in p
...I guess the actionable version is to develop transferable skills, abilities, wealth, or social capital that are highly valued by many different tribes.
Then you have the leverage to flit from one to the next, and not care about standing up for any particular tribe.
However, the game to acquire wealth, social capital, and valued skills is basically the game that we are all playing and has lots of competition. The only way to "opt out" is to join a local monopoly (i.e. a tribe). Also, in the real world, tribes often "loan us resources" to ...
I find this very true.
In fact, portraying a STRONGER identity often is met more easily results in better responses. The trick is that you can be strategic about it. By selecting between "personas" or "roles" you can select what kind of responses you want to get.
I find it helpful to think about the different situations I am in (work meetings, studying in cafes, meeting friends, etc.), and then think about "what is the most ideal response I could get" - and think about "what kind of person / action would provoke that kind ...
Does anyone really track the marginal utility of their possible investment this way? Utilons - sure. But ROI on status? ROI on "warm fuzzies"?
Also, this assumes we have good estimates of the ROI on all our options. Where do these estimates come from? In the real world, we often seem to spread our bets - constantly playing a game of multi-armed bandit with concept drift.
Can you explain what you mean by the problem of job training?
You mean job vs. career vs. calling?
If by "job training" you mean maximizing short-run over long-run earnings, I agree with you. But for that reason, if you move the "slider" toward a longer payoff period, then the schools will be incentivized to teach more fundamental skills, not short-term "job training".
On the other hand, sometimes people just need to get their foot in the door to get up and running. As they accumulate savings, on the job experience, professiona...
Nearly all education should be funded by income sharing agreements.
E1 = student's expected income without the credential / training (for the next n years).
E2 = student's expected income with the credentia / training (over the next n years). Machine learning can estimate this separately for each student.
C = cost of the program
R = Percent of income above E1 that student must pay back = (E2-E1)/C
Give students a list of majors / courses / coaches / apprenticeships, etc. with an estimate of expected income E2 and rate of repayment R.
Benefits:
Easy answer:
(1) Learning via experience imposes costs (rejection, broken heart, etc.); so learning will be slow and tentative.
(2) People who get good enough (via talent or experience) generally exit the dating market (they find a partner). Whoever is left is either still learning (expensive) or a "player" who prefers serial dating / hooking up to a long-term relationship (LTR). There may be a few people gaming the system by having multiple partners (the stereotypical "alpha"), but I doubt this is a significant fraction.
Depending on how things were weighted would really make a big difference. Especially since a lot of these theories make use of (aggregates of) proxies to measure what they really care about as the data you care about is long lost to history.
WRT overfitting, it would not be too hard to measure the error on a holdout set. Turchin et al. essentially used China as their holdout set.
Nobel Laureate in Econ Elinor Ostrom describes how in the real world we have a variety of formal And informal governance structures (that do negotiated decision making, monitoring, conflict resolution, punishments, etc.) to allow both local and global optima. From an info-processing view you know locally what’s best for you, but you need a way of aligning local decisions to reach global optima. Because this is very complex and fuzzy we humans have nested overlapping norms and institutions to govern behavior while allowing freedom and flexibility.
To see culture from a more CS perspective look up papers on “cultural evolution” and cooperation. The books / blog by evolutionary biologist / historian Peter Turchin “War and Peace and War” and “Super Cooperators”. Behavioral research on altruism and costly punishment in repeated prisoners dilemma games also shows the importance and impact of culture.
In this interpretation a “good” culture is one that has more solidarity & honor than back-stabbing & free riding. From a purely economic perspective it creates greater overall welfare and trust.
From
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Correlation is linear. Many causal functions can be non-linear.
Think of medicine. X is the dosage, Y is the improvement of health. If the dose is too low, you will get no response. If the does is within a good range, health improves. If the does is too high, you will begin to get even sicker. If data was gathered all along this inverted parabola, the correlation might be zero. But there is still a causal relationship between health and dosage.
Thus you can have causation without correlation.
You can probably think of many such functions with dimi... (read more)