Monthly Archives: March 2016

Spordiala valiku kriteeriumid

Spordi eesmärgiks võib olla hea välimus, tervis, sotsiaalne suhtlemine, lõbus ajaviide jne. Inimesed ei paista spordiala aga strateegiliselt valivat. Ei küsita, mis ala kõige paremini eesmärke täidab.

Huvitavus on individuaalne, aga sõltub osaliselt ka trennikaaslastest. See, millises trennis on toredad inimesed, on juhuslik. Suhtlemise osas on eelis ilmselt mitmeinimesealadel nagu pallimängud. Sõudmine üheinimesepaadis lobisemist ei soodusta.

Kiire liikumisega alad on enamikule huvitavamad, näiteks võistlustants huvitavam kui jooga. Otsese vastasega alad on adrenaliinirohkemad: maadlus põnevam kui jõutõstmine. Adrenaliiniga reklaamitakse muidugi ekstreemsporte, kus vigastusoht tekitab põnevust. Ala huvitavust võib ise sporti tegemata hinnata selle põhjal, kui paljud seda telekast vaatavad.

Tervise osas saab osaliselt otsustada statistika põhjal – milliseid vigastusi ja kui palju saadakse ajaühiku kohta erinevat sporti tehes. Ekstreemspordid on ilmselt tipus, aga peaks kontrollima. Teoreetiliselt peaks jalgpallis ja teistes jooksupõhistes mängudes olema rohkem vigastusi kui lihtsas jooksmises, sest võidakse kokku põrgata, keegi võib jalad alt niita jne. Poks on ohtlikum kui selle sarnane aeroobika või kotitagumine, sest mitte ainult ei anta, vaid ka saadakse. Ameerika jalgpall ja poks on ajukahjustuste poolest kuulsad.

Suured kiirused, raskused ja kaugus abist suurendavad ohtu. Inimene on nõrk, nii et välist jõudu (gravitatsiooni, tuult, lainet) kasutavad alad on üldiselt kõige kiiremad (nt mäesuusatamine, purjelaud, lainelaud). Motospordiks nimetatavaid valdkondi ma spordiks ei loe.

Tänapäeva inimeste eluviisi puhul on südameveresoonkonna trenni tavaliselt vähe, nii et tervise jaoks peaks eelistama vastupidavusalasid lühikese kiire jõupingutuse valdkondadele. Nii et ujumine tõstmise asemel, parem jalgrattasõit, kui akrobaatika jne. Liialdamine kestusega on jällegi halb, näiteks maraton nõrgestab organismi rohkem kui keskmaajooks.

Ilu osas tasub teha sümmeetrilisi spordialasid, mis treenivad kehapooli võrdselt. Piltidel on näha, kuidas tennisistidel on üks käsi jämedam. See näib kole. Sarnased probleemid reketialadele on ilmselt vehklemisel ja viskealadel (pesapall, kettaheide, odavise). Lihaseid võiks treenida üle kogu keha, mitte näiteks ainult jalgu nagu jalgrattasõidus või käsivarsi ja õlgu nagu mägironimises. Internetis on iga ala tippude kogukehaportreesid ja mõned on ikka üsna ebainimlikud. Näiteks maasvõitlusalad paistavad tekitavat ettepoole kummargil gorillarühti.

Rühist rääkides – selja sirgsust nõudvad ja seljalihaseid treenivad alad on selles osas kasulikud. Nimetaksin siinkohal tõstmist, sõudmist ja joogat.

Kui vanemad valivad sporti lapsele, võivad nad tahta poisse ja tüdrukuid erinevalt suunata. Iluideaalid on sugudel erinevad ja osad alad treenivad õlad laiaks. Ujumine näiteks. Ilusate jalgade nimel tasub ilmselt joosta, aga üldise välimuse nimel oleks ka kõhu- ja seljalihaste trenni vaja.

Strateegiliselt sporti valides peaks ka kulukust arvestama. Ratsutamine, golf ja purjetamine on rikaste rida. Mida vähem varustust vaja läheb, seda keskkonnasõbralikum ja odavam ala üldiselt on. Varustus hõlmab ka spordisaali või -väljakut, ujumisbasseini, suusahüppemäge jne. Sõit metsa orienteeruma või mere äärde lohelauatama läheb samuti kuludesse.

Kui ala on elukohas populaarne, siis on tõenäoliselt selle varustust palju saada, sealhulgas kasutatud ja odavat. Konkurents ajab hinna alla. Püsikulu (väljaku ehitus) jaotub suurema hulga inimeste vahel. Samuti on palju inimesi, kellega seda sporti arutada või koos teha. Populaarsetel aladel on seega kulu- ja suhtlemiseelis.

Kes tahab oma rahaga eputada, valib muidugi kallid alad – rikkuse näitamine on põhjus, miks inimesed ratsapolot ja golfi mängivad. Vaesed kiidelda tahtjad võivad odavamaid eksootilisi sporte proovida: mallakamb, Austraalia jalgpall, Türgi õlimaadlus.

Kui tahta spordiga raha teenida, peab tippu jõudma või treeneriks õppima. Tippu jõudmine on raske ja vähetõenäoline. Treeneritele on rakendust peamiselt populaarsetel aladel nagu jalgpall, aga peaks arvestama ka spordi tulevikupotentsiaali. Hääbuvatel aladel on nõudlust vähem kui moodi tulevatel.

Mõnest spordist võib elus laiemalt kasu olla (ujumine, orienteerumine, jalgrattasõit), enamikust aladest mitte (sulgpall, ballett). Võiks eelistada kasulikke.

Hea oleks, kui sporti saaks teha aasta läbi, aga lumega piirkonnas võib see nõuda eri alade kombineerimist. Näiteks suusatamine ja rullsuusatamine, uisutamine ja rulluisutamine. Eesti vihma- ja porihooaeg kevadel ja sügisel takistab kõiki õuesporte peale mudamaadluse. Siseruumialad nagu tõstmine ilmast ei sõltu, aga tore oleks sporti tehes ka loodusvaateid nautida. Seda saab aerutades, sukeldudes, orienteerudes.

Kas masinad asendavad inimeste töö?

Juba Teise maailmasõja eelsest ajast on kuulutatud masinate võidukäiku ja ennustatud inimtööjõu vajaduse lõppu. Daron Acemoglu artikkel (http://economics.mit.edu/files/11264) modelleerib tootmise automatiseerimist ja sellele suunatud uurimistööd, et vastata küsimusele, kas inimtööjõud tõrjutakse majanduse ja tehnoloogia arenguga ajapikku kõrvale.

Tuleb välja, et enamasti mitte. Põhjus on üsna sissejuhatava majanduskursuse maiguline – kui osa inimesi jääb automatiseerimise tagajärjel tööta, siis palgad langevad. Tööjõu odavnemine vähendab motivatsiooni automatiseerimiseks ja sellele suunatud uurimistööks ning suurendab sellise innovatsiooni tulusust, mis loob rohkem inimesi kasutavaid tehnoloogiaid. Sümmeetriliselt, kui uued meetodid nõuavad tootmises suuremat rahvahulka, siis tööjõukulu tõuseb ja nii muutub kapitalipõhine tootmine ja arendustegevus atraktiivsemaks. Majandus tasakaalustab end ise nii, et keskmiselt jääb kapitali ja tööjõu suhe tootmises samaks. Ajutiselt võidakse trendist kõrvale kalduda kui juhuslikult tuleb korraga palju uuendusi ühes suunas, näiteks inimeste vajadust vähendavaid.

Why taxes hit the middle class

The poor don’t have much wealth or income, so not much to tax. The rich find it worthwhile to hire accountants and lawyers to find loopholes useful for avoiding taxes. Optimizing one’s taxes is almost a fixed cost (does not depend much on the wealth or income), but the benefit is proportional to what is taxed. In other words, there are economies of scale in tax evasion. The middle class is not rich enough for professional loophole-seeking to be profitable, but has enough to tax.
This is only theoretical reasoning. Empirically, the average tax rate increases in income, as far as I know. Whether the rate of increase should be higher or lower than at present is a matter of political preference.
To avoid the unequal treatment based on the ability to hire tax advisors, the tax system should be simple. Changing income from one form to another (salary to dividends for self-employed for example), or changing the source (foreign corporation vs its domestic subsidiary) should not change the tax rate. Minimizing loopholes means no special deductions for belonging to select groups (farmers, pensioners, veterans, parents).
The existence of a profession for providing tax advice to ordinary people is a sign that the tax code is too complex. Those in a common salaried job should be able to understand and file their taxes themselves.

Publication delay provides incentives

From submitting a paper to a journal until getting the first referee reports takes about six months in economics. It is very rare to get accepted on the first try. Most papers are rejected, and an immediate acceptance implies having submitted to too weak a journal. Waiting for the referee reports on the revision and second revision takes another six plus a few months. This seems unnecessary (reading a paper does not take six months) and inefficient (creates delay in disseminating research results), but is used for incentives.
Delay discourages frivolous submissions. It forces authors to evaluate their own work with some honesty. If the referee reports were immediate, then everyone would start at the top journal and work their way down through every venue of publication until getting accepted. This would create a large refereeing and editing burden. Delay is a cost for the authors, because simultaneous submissions to multiple journals are not allowed. Trying for high-ranking journals is a risk, because the author may not have anything to show at the next evaluation. This reduces submissions to top journals. It may be optimal to start at the middle of the ranking where the chances of acceptance are higher.
A similar incentive to submit to the correct quality level of journal can be created by imposing a submission fee, forbidding further submissions for a period of time if rejected or requiring the author to write referee reports on others’ papers. A submission fee should be distinguished from publication fees, which are used at fake journals. The submission fee is paid no matter whether the paper is accepted, therefore does not create the incentive for the journal to lower its standards and publish more papers.
The submission fee would impose different costs on authors in different financial circumstances. Some have research funds to pay the fee, some do not. Similarly, delay has a larger effect on people whose evaluation is coming sooner. Being banned from a journal for a given amount of time after a rejection is worse for a researcher working in a single field. Interdisciplinary folk have a wider variety of venues to publish in. Writing referee reports as a price of having one’s work evaluated may lead to sloppy reviewing. Any mechanism to induce self-selection has a cost. Yet self-selection is needed.

Insurance in research

Most developed countries have programs to support research and encourage students to choose careers in it. This suggests scientists have a positive externality on the rest of their country that is not fully internalized in their income. Why not support research by paying the researchers its value, assuming the value can be measured? This would internalize the externality, leading to efficient effort provision.
A potential answer is different risk aversion of the organization supporting science and the scientists. If the institution is involved with many different projects, it is diversified and likely to be less risk averse than a researcher who only has a few projects. The arrangement optimal for both sides is then for the institution to offer insurance (at a cost). The researchers get paid a lower expected amount than the value of their work, but with a lower variance. Instead of the scientists taking loans to finance their work, becoming rich if the project succeeds and bankrupt if it fails, they avoid loans and get a fairly constant salary.
There is a tradeoff between incentives and insurance. If the salary does not depend on success, there is no incentive for effort, but perfect insurance. Having researchers take loans and get the full value of their work provides no insurance, but strong motivation. The compromise is that promotion and pay depend somewhat on research success, but not too much.

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.

On Trump and strategic voting

Edit 9 Nov 2016: I was wrong. To avoid publication bias, I will leave this post up. It will teach people not to trust my political judgement.

Commenting on Trump is fashionable lately, so let me jump on the bandwagon. Probably these points have all been made before.
It seems the people most against Trump are the Democrat supporters, which suggests they are ignoring strategic voting. A famous voting example is that if A is preferred to B by the majority, B to C and C to A by different majorities (1/3 of people prefer A to B to C, 1/3 prefer B to C to A and 1/3 C to A to B), then with naive voters the order of votes matters. If first the A vs B vote is held and the winner goes against C, then A wins against B, after which C wins against A. But if B and C are voted first, then B wins, after which it loses to A.
One possibility is that Trump is preferred to other Republican candidates, who in turn are preferred to Democrats, who are preferred to Trump. In this case the Democrats should strategically support Trump against other Republicans and be happy that the primary vote between Republicans happens before the general election, not after. A conspiracy theorist might even suspect collusion between Trump and the Democrats or at least secret Democrat support for Trump to split the Republicans, like in the plot of All the King’s Men.
What if the polls show swing voters to favour Trump over the Democrats? Who people claim to support in elections is not necessarily who they actually support (the Bradley effect). Voters may claim to support Trump as a joke, or actually favour him now, but reconsider closer to elections. Putting Trump in power is like a dangerous adventure – there is a thrill at the possibility and many (claim to) want to do it when it is in the distant future. When the opportunity actually arrives, it may look too scary and people may get cold feet.
In the end, if Trump actually becomes President, he has antagonized many Republicans. There is a good chance of a bypartisan effort to block all his initiatives. The crazy and illegal things Trump has promised to do are just election promises – politicians break those all the time. Even reasonable promises are broken – radical ones are even more likely to be ignored. If Trump tries to do the unconstitutional lunacies, both parties have an incentive to impeach him. The enemy of my enemy is my friend, so taking down Trump may be just the thing to make Republicans and Democrats strange bedfellows and narrow the political polarization in the US.

Vastasvõistkonna suurus teadmata

Sõjaväeõppused saab teha realistlikumaks ja mõned mängud (paintball, megazone) huvitavamaks kui vastasvõistkonna suurus on teadmata. Kui viia üks rühm inimesi kohale ja jagada nad seal kaheks ebavõrdseks võistkonnaks, saab enda võistkonda üle lugedes ja kogurühmast lahutades vastase suuruse teada. Võib muidugi viia kahes eraldi rühmas, aga see võib transporti raskendada ja osalejad on ikka enamvähem teada.
Selleks, et vastasvõistkonda välja arvutada ei saaks, tuleks korraldada kaks või enam paralleelset mängu. Igas mängus A ja B võistkond. Koos minnakse kohale, siis jagunetakse A ja B pooleks, kes eralduvad. Kui A ja B pool enam üksteist ei näe, siis jagatakse A pool juhuslikult erinevateks (A1, A2 jne) võistkondadeks ja B pool B1, B2 jne rühmadeks. Siis võistleb A1 B1-ga, A2 B2-ga jne. Enda võistkonna arv ei anna siis infot vastasvõistkonna suuruse kohta.

Bayesian vs frequentist statistics – how to decide?

Which predicts better, Bayesian or frequentist statistics? This is an empirical question. To find out, should we compare their predictions to the data using Bayesian or frequentist statistics? What if Bayesian statistics says frequentist is better and frequentist says Bayesian is better (Liar’s paradox)? To find the best method for measuring the quality of the predictions, should we use Bayesianism or frequentism? And to find the best method to find the best method for comparing predictions to data? How to decide how to decide how to decide, as in Lipman (1991)?

On grants, evaluation and efficiency

Getting grants counts positively in an academic’s evaluation and results in promotions and raises. But grants are supposed to be inputs for research, not outputs. Other things equal, it should be preferable to get the same output with fewer inputs (more efficiently and cheaply). Given an academic’s publications and patents, the grants they received in order to create these outputs should count negatively in their evaluation. The university administration is interested in motivating grant-getting, because they tax the grants – take a fraction of each for themselves. The motivating is done by promotions and raises. Rewarding more use of inputs inflates the cost of research and diverts effort from scientific output to getting more inputs.

A justification for rewarding grant-getting is that having current grants makes it easier to do research, thus increasing the expected scientific output in the near future. This only applies to a person’s current grants, not those already spent. Perhaps the current grants may count positively in an evaluation, but the spent ones should still have negative weight.

Once the system is in place, there may be an additional incentive to follow it: signalling obedience to rules. If academics are expected to apply for grants, then the ones that publicly do not may be considered contrarian, which may have negative consequences.

A similar reasoning applies to researchers from rich and poor institutions. If university resources are used for the work, then the person from a rich institution had more inputs for their work. The same output from a scientist in a poor university should be a more favourable signal about them.

An analogous adjustment is done in US college applications when low socioeconomic status confers an advantage. The direction of the correction is right, but its appropriate size remains to be determined.