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Theory and data both needed for prediction

Clearly, data is required for prediction. Theory only says: “If this, then that.” It connects assumptions and conclusions. Data tells whether the assumptions are true. It allows the theory to be applied.
Theory is also required for prediction, although that is less obvious. For example, after observing a variable taking the value 1 a million times, what is the prediction for the next realization for the variable? Under the theory that the variable is constant, the next value is predicted to be 1. If the theory says there are a million 1-s followed by a million 0-s followed by a million 1-s etc, then the next value is 0. This theory may sound more complicated than the other, but prediction is concerned with correctness, not complexity. Also, the simplicity of a theory is a slippery concept – see the “grue-bleen example” in philosophy.
The constant sequence may sound like a more “natural” theory, but actually both the “natural” and the correct theory depend on where the data comes from. For example, the data may be generated by measuring whether it is day or night every millisecond. Day=1, night=0. Then a theory that a large number of 1-s are followed by a large number of 0-s, etc is more natural and correct than the theory that the sequence is constant.
Sometimes the theory is so simple that it is not noticed, like when forecasting a constant sequence. Which is more important for prediction, theory or data? Both equally, because the lack of either makes prediction impossible. If the situation is simple, then theorists may not be necessary, but theory still is.

Targeting university donations more precisely

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If a donation is an expression of gratitude to a university where one acquired great skills or had a good time, then why not target it more precisely? Why donate to the entire university or a particular department as opposed to the people making up the university? Some people probably contributed more than others to the excellent university experience. It would make sense to reward them more. The people who made the studies enjoyable or useful may be gone from the university, especially if they were coursemates, but the employees of universities also change jobs. Those who are gone do not benefit from a donation to the university. A gratitude-based donation should go directly to the people one wants to thank.
If a donation is for the purpose of advancing education and research, then the money should be targeted to where it does the most good. But the universities receiving the most donations are those who are already rich. It is difficult to measure the benefit to education or research that an additional unit of money generates in different universities, but diminishing marginal returns seem reasonable. In that case, do-good donations should go to the poorest regions of the world and the poorest universities.
The richest universities often spend money on fancy architecture, with stonecarvings on the outside of buildings and woodcarvings and paintings inside them. The money thus spent clearly does not contribute to education or research. It may even have a negative value if architecturally interesting buildings are less well suited to study and work than a standard office block (this is true in my experience).
It is not enough to donate under the condition that the university must spend the money on scholarships or salaries, not buildings. There is a crowding-out effect: if the university receives a donation for a particular purpose, it spends less of its own money for that purpose than it would have without the donation. Effectively, part of the donation still goes to buildings.

Signalling by encouraging good decisionmaking

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Con artists pressure people into quick decisions. Marketing mentions that the offer is for a limited time only, so buy now, no time to read the small print. Date rapists try to get victims drunk or drugged. In all these cases, the goal is to prevent careful reasoning about what is happening and the decisions to be made. Also to prevent the victim from consulting others. Being pressured, confused or bullied while deciding is a danger sign, so one way for honest sellers to distinguish themselves is by encouraging good decisionmaking. Giving people time, referring them to neutral sources of info, asking them to think things over before deciding are all ways to make decisions more accurate.
More accurate decisions distinguish between good and bad deals better, which benefits honest sellers and harms con artists. This differential effect of information on good and bad types enables signalling by precision of information, where good types want to reveal as much as possible and bad types want to obfuscate. Information unravelling results – the best type has an incentive to reveal itself, then the second best type, then the third best etc. By not revealing, one is pooled with the average of the remaining types. In the end, the only type who does not strictly prefer to reveal itself is the worst type.

Repeal regulation requiring ratings

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The credit rating agencies (Moody’s, Fitch etc) have been accused of inflating the ratings of companies after their ratings underestimated the default risk during the 2008 financial crisis. First, it is strange to accept ratings expressed as letters (AAA, AAB etc) when the market participants care about the default risk and the letter codes are based (or so the rating agencies say) on the default risk. Remove the coarse letter codes and require the rating to equal the estimated probability of default over the next n years. The probability should have enough significant digits and should report standard errors. It should not vaguely claim that the default probability is somewhere between x and y. The potential for rating inflation and later justification of wrong ratings is reduced by transparency.
A good punishment for the rating agencies that also increases transparency is to repeal any regulation requiring the use of their ratings. Currently, banks are only allowed to invest in “investment grade” bonds, where the grade is determined by the credit rating (agencies). The purpose of the regulation should be to prevent banks from taking too much risk, so the variable of interest is the default probability, not the rating. Replace the requirement of “investment grade” rating with a requirement that the predicted default probability over the next n years must be below x. The obvious question is who predicts this probability.
The restriction to investing only in bonds predicted to be unlikely to default is similar to the vague requirement of due diligence. The investing bank must be able to justify its decision later if the investment turns out badly. The bank must use all available sources of info (maybe even rating agencies) and state of the art methods to predict default probabilities for bonds it intends to invest in. To prevent the bank from manufacturing a justification ex post to excuse its bad decision, the methodology it uses to predict must be provably unchanged from the time of investing. This can be achieved by sharing the methodology with the regulator.
There is a concern that business secrets leak from the regulator to competitors. This can be eliminated by encrypting the info that the bank gives the regulator, with the bank keeping the key. The encrypted info can even be publicly posted on the web. If concerns arise, the bank can later be ordered to give the key to the regulator (or even to the public), who can then verify the info received in the past. If the bank claims to have lost the key, the punishment should be the same as for the lawbreaking that the key is intended to verify.

Why messages of attraction are ambiguous

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There are many behaviours by which one human shows being sexually attracted to another – staring at them, running fingers through one’s hair, standing close, smiling at them, etc. Most of these are ambiguous, meaning they can be explained away by nonsexual reasons. Staring may be due to being lost in thought and looking absently at a single point, which happens to contain a person. Adjusting the hair could happen because the hair feels messy. One could randomly stand close to someone, smile because one is happy for unrelated reasons and so on.
There are obvious benefits of clear messages – no wasted effort chasing someone not interested, no awkward situations, no false accusations that one’s partner was sending signals of interest to someone else. Why has evolution led to messages of attraction that create doubt in the observers?
If someone’s sexual advances are unsuccessful, this is interpreted as a negative signal about the rejected person and lowers their chances in the future. Rejection makes one wonder what the rejecter knew about their admirer that is unattractive. If a person has characteristics that makes others reject them, the offspring of that person are likely to inherit these and also be unsuccessful in mating. Unsuccessful offspring mean the fitness of the rejected person is low, justifying rejecting them. This evolutionary mechanism is called Fisherian sexual selection. Because of it, nobody wants to be seen to be rejected. One way to hide rejections is to hide the wooing and if rejected, pretend to be uninterested anyway (sour grapes).
Someone attempting to cheat on their partner obviously does not want others to see their advances on another person. People gossip, so hidden signals with plausible deniability are useful.
Some people take advantage of those attracted to them (the advantage may differ for men and women), so it is good to send messages of attraction only to those who are attracted in return. Someone who is interested pays more attention to a person, so is more likely to notice ambiguous messages from them. Wishful thinking makes an interested recipient interpret mixed messages favourably. Of course there is a positive probability of a mistake, but the difference between the probability of interested people versus unintended recipients noticing a signal is greater for ambiguous than clear messages. This is like encryption – there is a positive probability of friendlies having lost the encryption key, but the difference between the probability of friendlies versus hostiles understanding the message is greater for encrypted text.
Dating websites have probably figured this out, because they allow private messages. An additional improvement may be self-destructing messages that can only be viewed once. This makes it harder for the recipient of a message to prove someone’s interest to others and thus lower their admirer’s reputation after rejecting them. Randomly generating messages of attraction and sending them to people would give plausible deniability to those who are rejected. The benefit of deniability must be weighed against the loss to the recipients of false signals.

Of airline food and a day of service

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The purpose of airline food is not to feed people but to show that the airline cares. The small plastic boxes with different food in each are a pretense of a multi-course meal. Multi-course meals are considered fancy. If the goal was to feed people, then a large sandwich or a bowl of pasta would be logistically simpler to provide and eat, cheaper and more filling.
Similarly a day of service (of volunteering) of some organization is not designed to help others but to show that the organization cares. The organization wants to be seen to be helping. If educated employees go and clean the park or work at a soup kitchen, it is a waste of their talents. It would be more productive to do their regular work and donate their salary to hire cheaper labour for the simple volunteering jobs. More volunteering output (cleaner park, food for the homeless) would be produced. Division of labour increases overall productivity, as Adam Smith pointed out.
Volunteering by highly qualified people may make sense if it is a vacation for them – their enjoyment outweighs the productivity loss relative to the efficient arrangement where everyone does their specialized job. A different type of work is a break from routine, which may be restful.
Once I participated in the Yale Day of Service. It was supposed to last from 9:00 to 14:00, so more like a half-day of service. Many people were late, so we started going towards the worksite at about 9:30 and reached it in ten or fifteen minutes. We were supposed to clear the underbrush among some park trees. The tools were dull gardening shears. The work ended at about 12:30. One person with a motorized trimmer could have done in ten minutes what twenty people with shears did in two hours. Clearly the goal was not to clear the park of bushes and weeds, but either a social event or a show of caring. Namewise, Yale Two Hours of Service sounds less nice than a Day.

Wasteful academic travel

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Academics fly around the world to meet coauthors, go to conferences or present seminars. These things could easily be done by videoconferencing, saving money, travel time, environment and productivity lost to jetlag. An objection I have heard is that video calls are not the same thing. What other senses besides sight and hearing do people use to communicate with their colleagues? A handshake maybe. Then build a robotic arm that gives haptic feedback to imitate any person’s hand and that can be used to shake hands at a distance.
If a wall-sized screen disguised at the edges is put in a seminar room and the audience walks in together, it would be a challenge to distinguish a real speaker at the front of the room from a speaker shown on the big screen. Eye tracking software can adjust the screen image as the viewer changes position to give the impression of 3D. Or the audience can wear virtual reality glasses like Oculus Rift.
Other than habit, commitment may be a reason for physical travel. If a person has travelled to give a seminar, the audience would feel embarrassed for not attending. This would be felt less if the presentation is via video and could be recorded. Then the option to watch it later would give people the excuse to constantly postpone watching. If an academic travels to a conference, there are fewer distractions than at home or at work, so a greater chance of actually going to the presentations.
The proliferation of laptops, smartphones and tablets is undermining this commitment – one can attend a talk and not pay attention, checking email or surfing the web instead. Google Glasses would have an even stronger effect: the eyes can be pointed towards the speaker while actually watching and listening something else.

A random world as an argument against fanatism

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Theoretical physicists may debate whether the universe is random or not, but for practical purposes it is, because any sufficiently complicated deterministic system looks random to someone who does not fully understand it. This is the example from Lipman (1991) “How to decide how to decide…”: the output of a complicated deterministic function that is written down still looks random to a person who cannot calculate its output.
If the world is random, we should not put probability one on any event. Nothing is certain, so any fanatical belief that some claim is certainly true is almost certainly wrong. This applies to religion, ideology, personal memories and also things right before your eyes. The eyes can deceive, as evidenced by the numerous visual illusions invented and published in the past. If you see your friend, is that really the same person? How detailed a memory of your friend’s face do you have? Makeup can alter appearance quite radically (http://www.mtv.com/news/1963507/woman-celebrity-makeup-transformation/).
This way lies paranoia, but actually in a random world, a tiny amount of paranoia about everything is appropriate. A large amount of paranoia, say putting probability more than 1% on conspiracy theories, is probably a wrong belief.
How to know whether something is true then? A famous quote: “Everything is possible, but not everything is likely” points the way. Use logic and statistics, apply Bayes’ rule. Statistics may be wrong, but they are much less likely to be wrong than rumours. A source that was right in the past is more likely to be right at present than a previously inaccurate source. Science does not know everything, but this is not a reason to believe charlatans.

Politeness levels transformed in translation

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Different languages use different phrasing to express the same level of politeness, formality or familiarity. Directly translating the words from one language to another may result in a sentence at a different (usually unintended) level. This creates the impression that the speakers of a language (usually coinciding with people from a particular country and culture) are all polite or all rude. They simply translate their thought to another language, where the phrasing is above or below the intended level of politeness. This is one way that stereotypes about a nationality’s modesty, assertiveness or formality are created.
As an Estonian in the UK, I was considered rude, because I answered “yes” or “no” instead of “yes, please” or “no, thanks.” In Estonian at the time, “no, thank you” would have been formal or ironic (mockingly formal) and I just translated my ordinary reply to English.
The level of formality in a language changes over time and with social class. A good example is TV series about 19th century Britain, where people say things like “you are too kind, Sir”. In modern times, this would sound strange.

Empirical project ideas with econjobmarket and AEAweb JOE

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The websites econjobmarket.org and AEAweb JOE are centralized job finding sites for economics PhDs. These have databases of application materials of thousands of job candidates, and the interviews many of them got. The subsequent jobs and publications of the job candidates are listed on the web. There are many empirical projects that can be done with this data, for example how certain keywords in recommendation letters predict the job that a candidate gets, or how the CV at the time of job application predicts future performance. One comparison that has been done in the sciences (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2572075/) is how recommendations of male and female candidates differ, i.e. what words are frequently used for one gender that are not used for the other. It is likely that economics recommendation letters contain similar biases.
The professors of top universities who have access to the databases of the job market websites have an advantage in hiring. They can predict which candidates perform well in the future and offer jobs to those. The employers without access to the databases are left with less promising candidates.