Arhiiv kuude lõikes: March 2018

Scientific thinking coordination game

If most people in a society use the scientific method for decision-making, then telling stories will not persuade them – they will demand evidence. In that case, bullshit artists and storytellers will not have much influence. It is then profitable to learn to provide evidence, which is positively correlated with learning to understand and use evidence. If young people respond to incentives and want to become influential in society (get a high income and social status), then young people will learn and use the scientific method, which reinforces the demand for evidence and reduces the demand for narratives.
If most people are not scientifically minded, but believe stories, then it is profitable to learn to tell stories. The skilled storytellers will be able to manipulate people, thus will gain wealth and power. Young people who want to climb the social and income ladder will then gravitate towards narrative fields of study. They will not learn to understand and use evidence, which reinforces the low demand for evidence.
Both the scientific and the narrative society are self-reinforcing, thus there is a coordination game of people choosing to become evidence-users or storytellers. Note that using the scientific method does not mean being a scientist. Most researchers who I have met do not use science in their everyday decisions, but believe the stories they read in the media or hear from their friends. I have met Yale PhD-s in STEM fields who held beliefs that most people in the world would agree to be false.
One signal of not thinking scientifically is asking people what the weather is like in some place one has not visited (I don’t mean asking in order to make small talk, but asking to gain information). Weather statistics for most places in the world are available online and are much more accurate than acquaintances’ opinions of the weather. This is because weather statistics are based on a much longer time series and on physically measured temperature, rainfall, wind, etc, not on a person’s guess of these variables.

Giving oneself tenure

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Senior academics tell juniors that an assistant professor does not have to get tenure at his or her current university, but “in the profession”, i.e. at some university. To extend this reasoning, one does not have to get tenure at all, just guarantee one’s ability to pay one’s living costs with as low effort as possible. Government jobs are also secure – not quite tenure, but close.
Economically, tenure is guaranteed income for life (or until a mandatory retirement age) in exchange for teaching and administrative work. The income may vary somewhat, based on research and teaching success, but there is some lower bound on salary. Many nontenured academics are obsessed about getting tenure. The main reason is probably not the prestige of being called Professor, but the income security. People with families seem especially risk averse and motivated to secure their job.
Guaranteed income can be obtained by other means than tenure, e.g. by saving enough to live off the interest and dividends (becoming a rentier). Accumulating such savings is better than tenure, because there is no teaching and administration requirement. If one wishes, one can always teach for free. Similarly, research can be done in one’s free time. If expensive equipment is needed for the research, then one can pay a university or other institution for access to it. The payment may be in labour (becoming an unpaid research assistant). Becoming financially independent therefore means giving oneself more than tenure. Not many academics seem to have noticed this option, because they choose a wasteful consumerist lifestyle and do not plan their finances.
Given the scarcity of tenure-track jobs in many fields, choosing the highest-paying private-sector position (to accumulate savings), may be a quicker and more certain path to the economic equivalent of tenure than completing sequential postdocs. The option of an industry job seems risky to graduate students, because unlike in academia, one can get fired. However, the chance of layoffs should be compared to failing to get a second postdoc at an institution of the same or higher prestige. When one industry job ends, there are others. Like in academia, moving downward is easier than up.
To properly compare the prospects in academia and industry, one should look at the statistics, not listen to anecdotal tales of one’s acquaintances or the promises of recruiters. If one aspires to be a researcher, then one should base one’s life decisions on properly researched facts. It is surprising how many academics do not. The relevant statistics on the percentage of graduates or postdocs who get a tenure-track job or later tenure have been published for several fields (http://www.nature.com/ncb/journal/v12/n12/full/ncb1210-1123.html, http://www.education.uw.edu/cirge/wp-content/uploads/2012/11/so-you-want-to-become-a-professor.pdf, https://www.aeaweb.org/articles?id=10.1257/jep.28.3.205). The earnings in both higher education and various industries are published as part of national labour force statistics. Objective information on job security (frequency of firing) is harder to get, but administrative data from the Nordic countries has it.
Of course, earnings are not the whole story. If one has to live in an expensive city to get a high salary, then the disposable income may be lower than with a smaller salary in a cheaper location. Non-monetary aspects of the job matter, such as hazardous or hostile work environment, the hours and flexibility. Junior academics normally work much longer than the 40 hours per week standard in most jobs, but the highest-paid private-sector positions may require even more time and effort than academia. The hours may be more flexible in academia, other than the teaching times. The work is probably of the same low danger level. There is no reason to suppose the friendliness of the colleagues to differ.
Besides higher salary, a benefit of industry jobs is that they can be started earlier in life, before the 6 years in graduate school and a few more in postdoc positions. Starting early helps with savings accumulation, due to compound interest. Some people have become financially independent in their early thirties this way (see mrmoneymustache.com).
If one likes all aspects of an academic job (teaching, research and service), then it is reasonable to choose an academic career. If some aspects are not inherently rewarding, then one should consider the alternative scenario in which the hours spent on those aspects are spent on paid employment instead. The rewarding parts of the job are done in one’s free time. Does this alternative scenario yield a higher salary? The non-monetary parts of this scenario seem comparable to academia.
Tenure is becoming more difficult to get, as evidenced by the lengthening PhD duration, the increasing average number of postdocs people do before getting tenure, and by the lengthening tenure clocks (9 years at Carnegie Mellon vs the standard 6). Senior academics (who have guarateed jobs) benefit from increased competition among junior academics, because then the juniors will do more work for the seniors for less money. So the senior academics have an incentive to lure young people into academia (to work in their labs as students and postdocs), even if this is not in the young people’s interest. The seniors do not fear competition from juniors, due to the aforementioned guaranteed jobs.
Graduate student and postdoc unions are lobbying universities and governments to give them more money. This has at best a limited impact, because in the end the jobs and salaries are determined by supply and demand. If the unions want to make current students and postdocs better off, then they should discourage new students from entering academia. If they want everyone to be better off, then they should encourage research-based decision-making by everyone. I do not mean presenting isolated facts that support their political agenda (like the unions do now), but promoting the use of the full set of labour force statistics available, asking people to think about their life goals and what jobs will help achieve those goals, and developing predictive models along the lines of “if you do a PhD in this field in this university, then your probable job and income at age 30, 40, etc is…”.

On the optimal burden of proof

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All claims should be considered false until proven otherwise, because lies can be invented much faster than refuted. In other words, the maker of a claim has the burden of providing high-quality scientific proof, for example by referencing previous research on the subject. Strangely enough, some people seem to believe marketing, political spin and conspiracy theories even after such claims have been proven false. It remains to wish that everyone received the consequences of their choices (so that karma works).
Considering all claims false until proven otherwise runs into a logical problem: a claim and its opposite claim cannot be simultaneously false. The priority for falsity should be given to actively made claims, e.g. someone saying that a product or a policy works, or that there is a conspiracy behind an accident. Especially suspect are claims that benefit their maker if people believe them. A higher probability of falsity should also be attached to positive claims, e.g. that something has an effect in whatever direction (as opposed to no effect) or that an event is due to non-obvious causes, not chance. The lack of an effect should be the null hypothesis. Similarly, ignorance and carelessness, not malice, should be the default explanation for bad events.
Sometimes two opposing claims are actively made and belief in them benefits their makers, e.g. in politics or when competing products are marketed. This is the hardest case to find the truth in, but a partial and probabilistic solution is possible. Until rigorous proof is found, one should keep an open mind. Keeping an open mind creates a vulnerability to manipulation: after some claim is proven false, its proponents often try to defend it by asking its opponents to keep an open mind, i.e. ignore evidence. In such cases, the mind should be closed to the claim until its proponents provide enough counter-evidence for a neutral view to be reasonable again.
To find which opposing claim is true, the first test is logic. If a claim is logically inconsistent with itself, then it is false by syntactic reasoning alone. A broader test is whether the claim is consistent with other claims of the same person. For example, Vladimir Putin said that there were no Russian soldiers in Crimea, but a month later gave medals to some Russian soldiers, citing their successful operation in Crimea. At least one of the claims must be false, because either there were Russian soldiers in Crimea or not. The way people try to weasel out of such self-contradictions is to say that the two claims referred to different time periods, definitions or circumstances. In other words, change the interpretation of words. A difficulty for the truth-seeker is that sometimes such a change in interpretation is a legitimate clarification. Tongues do slip. Nonetheless, a contradiction is probabilistic evidence for lying.
The second test for falsity is objective evidence. If there is a streetfight and the two sides accuse each other of starting it, then sometimes a security camera video can refute one of the contradicting claims. What evidence is objective is, sadly, subject to interpretation. Videos can be photoshopped, though it is difficult and time-consuming. The objectivity of the evidence is strongly positively correlated with the scientific rigour of its collection process. „Hard” evidence is a signal of the truth, but a probabilistic signal. In this world, most signals are probabilistic.
The third test of falsity is the testimony of neutral observers, preferably several of them, because people misperceive and misremember even under the best intentions. The neutrality of observers is again up for debate and interpretation. In some cases, an observer is a statistics-gathering organisation. Just like objective evidence, testimony and statistics are probabilistic signals.
The fourth test of falsity is the testimony of interested parties, to which the above caveats apply even more strongly.
Integrating conflicting evidence should use Bayes’ rule, because it keeps probabilities consistent. Consistency helps glean information about one aspect of the question from data on other aspects. Background knowledge should be combined with the evidence, for example by ruling out physical impossibilities. If a camera shows a car disappearing behind a corner and immediately reappearing, moving in the opposite direction, then physics says that the original car couldn’t have changed direction so fast. The appearing car must be a different one. Knowledge of human interactions and psychology is part of the background information, e.g. if smaller, weaker and outnumbered people rarely attack the stronger and more numerous, then this provides probabilistic info about who started a fight. Legal theory incorporates background knowledge of human nature to get information about the crime – human nature suggests motives. Asking: „Who benefits?” has a long history in law.

Salami tactics in everyday life

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Salami tactics mean doing something others dislike little by little to keep them from obstructing or retaliating. Each small step is too small to be worth retaliation, but together the steps add up to an action that, if taken all at once, would definitely be worth stopping or retaliating for.
I have neighbours who put their unwanted things (broken bicycles, toys, furniture) in my parking space, using salami tactics. They don’t put the objects right in the middle of the space all at once, but initially put them mostly outside my parking space, with a small part of the object sticking into the space. If I don’t do anything, then the neighbours shift the objects so that a somewhat larger fraction of them is in my space, or add more objects that slightly stick into the space. If I push some of their rubbish out of my space, then other things appear, sticking into a different part of the space. They have plausible deniability in case I confront them – the object was only slightly in my space, so placing it there can be excused as an accident. This dance has gone on over a year, with them pushing their stuff into my space when I’m not there and me pushing it out when they are not there. Their encroachment attempts have become somewhat humorous, and I am observing them as a social science field study. The same encroachment happens in the common hallway, which fire regulations require to be clear at all times, but where the neighbours store their unwanted furniture. The furniture starts out near their door and gradually moves further and spreads out.
People in another house in this suburb have gradually squatted on a piece of the public park. They planted a hedge around that piece, which adjoins their dwelling. Now it is somewhat difficult to get into that part of the park (one has to squeeze through the hedge), so other people have stopped going there. It is effectively a private garden. The hedge did not appear overnight – the planting happened at a rate of about one bush per month, so it wasn’t too obvious to people who regularly pass through the park. The squatters planted the bushes in daylight, so deniability would have been a bit stretched if someone had confronted them. To some extent, planting one bush at a time in a public park can still be claimed an idle hobby, with no intent to encroach. However, the hedge that now clearly encloses a plot next to their dwelling does look suspicious.
A related tactic does not slice the salami slowly over time, but slices it many times at once to different people. At work, there are people who, upon receiving a task from the boss, ask one colleague at their level to help with one part of the task, another colleague to help with another part, etc, until they have delegated the whole task piece by piece. Each piece comes with an excuse why the delegator cannot do it. Then when the colleagues ask for help in return, the delegator is absent, unavailable due to family responsibilities or has some other excuse.

Korruptsioon on ühiskonnalt varastamine

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Korruptiivse tehinguga on nõus nii altkäemaksu andja kui võtja, kuna mõlemad saavad kasu. Kahju kannatab ühiskond, näiteks kui kinnisvaraarendaja saab linnaplaneerimise ametnikult loa ehituseks, mis linnapilti ja konkureerivaid ehitajaid kahjustab või ohutusnõuetele ei vasta. Summaarne kahju on üldiselt suurem kui altkäemaksu andja ja võtja kogukasu, muidu võiksid korruptandid pakkuda ülejäänutele kompensatsiooni, mis on suurem nende kahjust. Nii võiks kogu ühiskonna nõusse saada seadust või planeeringut muutma ja tehing poleks enam korruptiivne, vaid aus äri.
Tehingu kogukahju, mis on suurem kasust tehingu osapooltele, on kuritegelike tehingute üldine omadus. Näiteks kui taskuvargad teevad koostööd (üks tõmbab ohvri tähelepanu, teine varastab) ja jagavad saagi, siis mõlemad vargad võidavad, aga ohver kaotab rohkem, sest peab lisaks raha kaotusele asendama dokumendid ja rahakoti. Vargad saavad aga ainult raha. Jällegi, kui kasu oleks kahjust suurem, võiks tehingus (antud juhul rahakoti uuele omanikule andmises) ausalt kokku leppida ja tulemuse jaotada nii, et kõik võidavad.
Korruptandid lepivad sisuliselt kokku ülejäänud ühiskonnalt varastamises.

Karma ja efektiivsus

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Miks peaks igaüks vastutama oma valikute tagajärgede eest? Majandusteoorias on leitud, et efektiivse (ühiskonna summaarset heaolu maksimeeriva) tulemuse saavutamiseks tuleb igaühele anda tema tegevuse eest tasu, mis võrdub selle tegevuse mõjuga ülejäänud ühiskonnale. Kui tasu on negatiivne, nimetatakse seda karistuseks, ja see vastab negatiivsele mõjule. Asjakorraldust, mis igaühele tema tegevuse tagajärjed annab, nimetatakse majandusteoorias Vickrey-Clark-Grovesi (VCG) mehhanismiks ja India usundites karmaks.
Põhjus, miks VCG mehhanism efektiivsele tulemusele viib, on, et selle mehhanismi toimimise korral on igaühe jaoks parim (enda kasulikkust maksimeeriv) otsus see, mis maksimeerib ühiskonna kogukasulikkust. Rakendub Kanti kategooriline imperatiiv: igaüks teeb seda, mis ühiskonna normiks muutudes ühiskonna kogukasulikkust maksimeerib. Tegevuse tagajärg endale ja ühiskonnale on sama, nii et igaüks teeb teistele seda, mis endale. Ja kuna endale teeb inimene seda, mida tahab, et talle tehakse, siis teeb ta VCG mehhanismi all teistele seda, mida tahab, et talle tehakse – rakendub mitmest usundist tuttav „kuldreegel”.
Kasu ja kahju ei pruugi VCG mehhanismis ja majandusteoorias üldiselt olla deterministlik. Kiiruseületus, ohutusnõuete või keskkonnakaitsereeglite rikkumine tekitab kahju teatud tõenäosusega – siis, kui juhtub õnnetus või saaste pääseb loodusesse. Efektiivsuse saavutamiseks peaks otsustaja kandma otsuse oodatava kahju otsustamise hetkel. Juhusliku või hilisema kahju puhul otsustajale peaks see tema kasulikkust mõjutama samamoodi, nagu otsuse hetkel saadav oodatav kahju. Ehk riskikartlikule otsustajale piisab juhuslikust kahjust, mis on ooteväärtuselt väiksem ühiskonnale tekitatavast. Riski armastavale otsustajale peab aga juhusliku kahju ooteväärtus olema suurem ühiskonnale tekitatavast.

“Relative to opportunity” evaluation and anti-discrimination laws

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Most countries have some anti-discrimination laws, requiring employers to pay people with different productivities equally, or to give someone who took parental leave their job back after they return. One reason why unequally productive people are paid equally is evaluation “relative to opportunity”, i.e. the bar for a promotion or a raise is lower for someone from a historically disadvantaged group or who has family responsibilities. Suppose that there is a consensus in society for supporting certain groups. Why might the cost of this support be placed only on specific employers, namely those who employ the target group? Why doesn’t the support take the form of direct subsidies from the government to the target group, financed by taxing all employers equally?
An explanation from political economy is as follows. Clever people in society or government want to pay a larger subsidy to high-income members of the target group, perhaps because the clever people are themselves high-income and belong to the target group. However, the majority of voters would not like high-income people getting a bigger subsidy. So the clever people disguise the subsidy as something that looks equal, namely every member of the target group gets the same duration of parental leave, the same guarantee of their job back at the end of leave, the same privileges and special treatment for promotions and raises. The value of these guarantees and privileges is greater for higher-paying jobs. For example, a promotion from a high-paying job usually gives a bigger salary increase than from a low-paying job. A guarantee of getting a high-paying job back after parental leave is worth more than a guaranteed low-paid job. Thus the support provided to the high-income members of the target group is more valuable.
Further, productivity at a high-income job is typically more responsive to the employee’s human capital, and the skills deteriorate faster. A truck driver who has not driven for some years retains a greater fraction of driving ability than a surgeon retains from surgical technique after not operating for the same number of years. The productivity difference between an employee returning from parental leave and someone continuously employed is on average greater for higher-paying jobs. So the cost to an employer of keeping a job for a returning employee instead of hiring a new person is greater if the job is higher-paid. The subsidy to the high-income members of the target group thus costs more per person.
Income is positively correlated with intelligence, so the smart members of the target group are likely the wealthy members who benefit from this kind of unequal subsidy. They are likely to vote and campaign in favour of the unequal support, instead of an equal cash subsidy for everyone. The less smart members of the target group who lose from an unequal subsidy (compared to an equal one) are less likely to understand that they lose. This makes them abstain from opposing the unequal subsidy.
In effect, the smart members of the target group redistribute a baseline-equal subsidy from the less smart (and less wealthy) to themselves, at the social cost of losing some efficiency in the economy. Keeping a job for someone when a more productive potential hire is available means losing the difference in the productivities of the two people. Such efficiency losses are typical of re-distributive policies that are not cash transfers.
In principle, the cost of the current policies could still be equally distributed between employers by taxing them all and subsidising those who employ the target group. Or equivalently, taxing only those who don’t employ the target group. However, to equalise the cost to employers, the firms employing highly-paid members of the target group must be compensated more than the ones employing low-paid members. The differential compensation to firms would call attention to the unequal support that people with different incomes are receiving, thus weakening the disguise of the subsidy. The clever people want to avoid that, so do not campaign for equalising the costs of anti-discrimination laws across employers.