Tag Archives: being and seeming

Comparing dictatorships

Why compare evil regimes? Sometimes a choice must be made which one to support. Inaction and refusal to choose is also a choice and may favour one or another. The help or harm to some regime may be indirect, e.g. through the enemy of an enemy.

How to compare evil regimes? I have encountered people who compare based on words, justifying crimes against humanity by some countries with the argument that their goals were good or the ideology was good, just wrongly implemented. (The subtext here is that if it was wrongly implemented in the past, perhaps it should be tried again in the hopes of implementing it rightly.) I disagree. Actions should be the basis of judgement, not narratives. A failure of a political system is a negative signal about it. Regardless of whether it signals a fundamental flaw or a low likelihood of right implementation, until all other systems have been tried and have accumulated a similar weight of negative signals, the failed system should not be tried again. This can be mathematically formalized as optimal sequential control under incomplete information.

I believe comparisons of countries should be based on objective criteria, preferably specified before the data is gathered (as in the scientific method). These objective criteria are for example the number of people killed, tortured, wrongly imprisoned, expropriated, the number and extent of wars started, the territory and population conquered and for how long, the economic and environmental damage caused. The number of ethnic or religious groups eliminated may also be counted, but this has the effect of weighting the deaths of people from smaller groups more.

The measures can be total or divided by time or by the number of supporters of the regime. The total of these criteria is generally larger for bigger countries. There is simply more opportunity to kill, torture, etc when there are more people available. The total measures are of interest because they show the whole negative impact on the world.

Division by time results in criteria that measure the flow of evil done. If the decision is which regime to eliminate first, it is optimal to focus on the one with the greatest predicted negative influence per unit of time. This strategy minimizes the total impact of evil regimes.

To find the expected number of crimes of a person from a dictatorship (or its leadership) without other data about them, the total crimes of the regime should be divided by its population (or number of leaders). Dividing by both people and time gives the expected flow of evil per person, suggesting an optimal strategy of removing leaders of criminal regimes.

The above focusses on past evil, but for predictive purposes the attractiveness or “selling power” of the regime also matters. How likely is the dictatorship to survive and expand? If more people favour and justify it, including outside its borders, it has a greater opportunity to do evil in the future. So the niceness of the narrative used to excuse its actions is actually a negative signal about an otherwise criminal regime. If the stories the dictatorship tells about itself make people consider its goals good or ideology good, then the dictatorship is more dangerous than another that cannot manipulate audiences into supporting it.

Principles help in resisting the siren call of “the end justifies the means.” For example the principle that nothing justifies crimes against humanity. No story about the greater good, no idealistic ideology. Another good principle is that actions speak louder than words. If a regime fails at good governance, excuses should be ignored.

Religion is wrong with positive probability

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When a person says that some god wants X or rewards X and punishes Y, then how do they know? They, a limited human, claim knowledge of the mind of a god. When asked how they know, they say either that the god told them directly (using some revelation or sign perhaps) or that the god told some other person (a prophet) in the past, who passed on the message in the form of a book or an oral tradition. They certainly do not have replicable experimental evidence. If some other person was told, then recall the telephone game (children whispering in each other’s ear change the message radically) and people’s general lying, misunderstanding and misremembering. In any case, at some point a god must have told a person.
Let us look at this unavoidable transmission link between a purported god and a human. Could not an evil spirit have impersonated the god to the human (if evil spirits exist in their religion)? Or could it have been just a hallucination, dream, false memory? Psychology shows false memories are easy to induce (Brainerd and Reyna 2005 “The science of false memory”). How could a human tell the difference? Plenty of people in insane asylums claim not only to know a god’s will but also to be a god.
If there is a method for distinguishing real revelations from gods from false ones, how do you know it works? Either a god told you directly that it works or a person told you. In both cases we arrive at the previous question: how do you know it was a god and that you (or the other person) understood and remember the message correctly? If there is a method for finding and verifying good methods of distinguishing real from fake revelations, how do you know that works? And so on. Everything eventually relies on belief in the claim of a human. There is always a positive probability that the human imagined things that were not there or is deceiving self or others. Any religious claim is wrong with positive probability.
The next fallback position for advocates of religion is that even if it is wrong with positive probability, it does no harm to believe it. But how do they know? Back to the question about knowing the mind of a god. Why cannot a god reward nonbelievers and punish believers? And which religion out of the multitude in the world should one believe for maximal benefit? Some religions claim that a wrong religion is worse than none (heresy worse than paganism), some the opposite. To compare the expected benefit from believing different religions and from atheism, one needs to know the size of the rewards and punishments and also their probabilities. All this reduces to (conflicting) claims by humans about the will of a god. Which are wrong with positive probability.

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.

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.

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.

Claims that the economics job market is tough this year

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It seems that every year since I started grad school, I hear someone say that the economics job market is tough (for candidates) that year. Usually it is in connection with some graduate student on the market getting a less good job than one anticipated. But the toughness of the market is a relative measure, so relative to what year is this year tough? Relative to 1950? After the Second World War, the US expanded its university sector with the GI Bill, which created a large demand for new faculty members. This made the market easy for candidates and as the effect gradually faded, the market got tougher. This is probably not what people have in mind when they claim a tough market.
As computing power becomes cheaper, the demand for people who are substitutes of computers (theorists) falls and the demand for complements of computers (empirical and computational researchers) rises. So the theory market may get tougher for candidates over time, but the empirical market should get easier.
There are other long term trends, like the fraction of the population getting a university degree increasing, but at a decreasing rate. If the university sector expands to cater to the increased demand, the market should get easier for candidates. But this also depends on the expectations of the universities. Hiring responds to anticipated future enrollment, not just the current number of students. So if demand for university education rises less than expected (it does not have to fall), the demand for new faculty members falls.
Lengthening lifespans mean older faculty members free up fewer spots in universities, which reduces demand for new faculty members, but this effect is tiny, because lifespans lengthen very slowly.
A short term effect on hiring was the financial crisis, which reduced university hiring budgets. This made 2009 a tough year for candidates relative to the surrounding years.
A study on how tough the market really is would be interesting, but hard to do, because it requires a measure of the quality of candidates that is independent of the jobs they get or papers they publish. Both jobs and papers are subject to a congestion effect, so the toughness of the job market or publication market affects these measures. The definition of toughness is that the tougher the market, the worse the results for a graduating student of a given quality.
The market for economists is worldwide, so it would be easier to study academics in some field that is country-specific and thus has barriers to trade, say law.

Claims that placement officers do a great job

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Those on the economics job market have probably heard statements in their department like “our placement officers do a great job” and “we place our students very well”. First, no university would say they place students badly, because then students would not apply there. Second, faculty members don’t want to be in committees, including placement, so if one faculty member said that another does a bad job in placement, then the immediate response would be: “You do it then, and do better.” Anticipating this, no faculty member will criticize another’s committee work quality.
Hence, an empirical project idea: how does the placement outcome (e.g. rank of institution making job offer) depend on student quality (e.g. papers published before graduating) and the placement committee and university fixed effects? The measures of quality and outcome are of course noisy, but the sample size (people on the job market) is fairly large.

Who discriminates whom?

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In social networks with multiple races, ethnic or religious groups involved it is generally the case that there are fewer links between groups and more within groups than would be expected from uniform random matching. One piece of research exploring this is Currarini, Jackson, Pin (2009).

When observing fewer intergroup links than equal-probability matching predicts, the natural question is who discriminates whom. If group A and group B don’t form links, then is it because group A does not want to link to B or because B does not link to A? If we observe more couples where the man is white and the woman is Asian than expected from uniform random matching, is this due to the `yellow fever’ of white men or a preference of Asian women for white men? It could also be caused by white men and Asian women meeting more frequently than other groups, but this particular kind of biased matching seems unlikely.

Assume both sides’ consent is needed for a link to form. Then the probability that a member of A and a member of B form a link is the product of the probabilities of A accepting B and B accepting A. We can interpret these probabilities as the preference of A for B and B for A and say that if the preference of A for A is stronger than the preference of A for B, then A discriminates against B. From data on undirected links alone, only the product of the probabilities can be calculated, not the separate probabilities. So based only on this data it is impossible to tell who discriminates whom.

If there are more than two groups in the society, then for each pair of groups the same problem occurs. Under the additional assumption that a person treats all other groups the same, only his own group possibly differently from the other groups, the preference of each group for each group can be calculated. This assumption is unlikely to hold in practice though.

If only one side’s consent is needed for a link to form, then from data on these directed links, the preference of each group for each group can again be calculated. The preference of A for B is just the fraction of A’s links that are to B, divided by the fraction of B in the population.

With additional data on who initiated a link or how much effort each side is putting into a link, the preference parameters may be identifiable. The online dating website OKCupid has some statistics on how likely each race is to initiate contact with each other race and how likely each race is to respond to an initial message by another race. If these statistics covered the whole population, then it would be easy to calculate who discriminates whom. In the case of a dating website however, the set of people using it is unlikely to be a representative sample of the population. This may change the results in a major way.

If the average attractiveness of group A in just the dating website (not in the whole population) is higher than that of other groups, then group A is likely to receive more initial contact attempts just because they are attractive. They can also afford to respond to fewer contact attempts since, being attractive, they can be pickier and make less effort to form links. If we disregard the nonrepresentative sample problem and just calculate the preferences of all groups for all other groups, then all groups will be found discriminating in favour of group A, and group A will be found discriminating against all others. But in the general population this may not be the case.

The attractiveness of group A in the dating website can differ from their average attractiveness if the website is more popular with group A and there is adverse selection into using the website. Adverse selection here means that only the people sufficiently unattractive to find a match by chance during their everyday life make the extra effort of starting to use the website to look for matches. So the average attractiveness of all groups using the website is lower than the population’s average attractiveness.

If a larger fraction of group A prefers to use the website and the users from all groups are drawn from the bottom end of the attractiveness distribution, then the website is relatively more popular with attractive members of A than with attractive members of other groups. Therefore the average attractiveness of those members of A using the website is higher than the average attractiveness of those members of other groups using the website. The higher preference of group A for using the website must be exogeneous, i.e. due to something other than A’s lower average attractiveness, otherwise this preference does not cause A’s attractiveness on the website to rise. It could be that members of A are more familiar with the internet, so have a lower effort cost of using any website. Or there may be a social stigma against using online dating sites, which could be smaller in group A than in other groups.

If statistics from a nonrandom sample show discrimination, there may or may not be actual discrimination in the population, depending on the bias of the sample. It could also be that the actual discrimination is larger than the sample shows, if the sample bias goes in the opposite direction from the one described above.