This is kinda fun to watch.
http://move.rmi.org/features/oilmap.html
It would be even cooler to show the whole world’s oil flow.
Google Maps based graphic of oil imports to the US
Reply
Category Archives: Bloggy Things
Population Pyramids
One of the most informative sites on the web is:
US Census Bureau’s International Data Base (IDB)
For we graph junkies the dynamic population pyramids on the site are candy.
For example:
I’ve found several fun things at this site:
- Median ages climb to astounding heights in most of the “advanced” nations by 2050.
We’re talking median ages in the low 50’s. Heck, the US median age was in the high 20’s in 1975. Look at Nigeria today. 18 or 19. In 2050, Nigeria looks like the US anywhere in the last half-century.
Keep in mind that for most of human history, the median age was well under 20.
When you think about swords and sorcerers, middle ages times, think kids. That’s what people of the day were.
- Certain countries’ pyramids’ 80+ female bars go mondo by 2050.
Both Japan and Italy were striking before the web site’s pyramid graphics added age ranges up to 100+ years.
You really notice when a bar for the age of 80+ years is dramatically longer than any of the other bars.
The median age may be in the low 50’s, but, in a way, the most common person in the country is a woman over 80. I’m thinking that such a country will not be noted for its dynamicism.
- The US baby boom dies out in favor of the echo generation.
The turnover year is 2015 (kinda). That’s when the biggest 5-year birth group in the US switches from one in the 1955-1965 range to 1985-1990.
Keep that in mind when you read opinion pieces about how the US has been on such a thoughtless spending spree and, golly, we have bad times coming to us, if there is a God in heaven.
A slightly less negative view might be: the US has moved in to a period of child raising that’s at the highest expense level. Anyone who has gone through that period can understand how perhaps a wee bit of spending, painful as it might be for the frugal, is not unexpected.
My take is the higher education bubble is gonna burst in the next few years.
- Speaking of dynamicism, look where the centers of the universe go by 2050:
Countries and Areas Ranked by Population: 1950 -------------------------------------------------------- Rank Country or Area Population -------------------------------------------------------- 1 China 562,579,779 2 India 369,880,000 3 United States 152,271,000 4 Russia 101,936,816 5 Japan 83,805,000 6 Indonesia 82,978,392 7 Germany 68,374,572 8 Brazil 53,443,075 9 United Kingdom 50,127,000 10 Italy 47,105,000 -------------------------------------------------------- Note: Data updated 12-15-2008 (Release notes).Source: U.S. Census Bureau, International Data Base. Countries and Areas Ranked by Population: 2050 -------------------------------------------------------- Rank Country or Area Population -------------------------------------------------------- 1 India 1,807,878,574 2 China 1,424,161,948 3 United States 439,010,253 4 Indonesia 313,020,847 5 Pakistan 295,224,598 6 Ethiopia 278,283,137 7 Nigeria 264,262,405 8 Brazil 260,692,493 9 Bangladesh 233,587,279 10 Congo (Kinshasa) 189,310,849 -------------------------------------------------------- Note: Data updated 12-15-2008 (Release notes).Source: U.S. Census Bureau, International Data Base.There’s a trend to two places: The Indian sub-continent and a kinda central-to-northeast Africa area. That Africa area is bigger than it looks because so many of the countries in that area are little. They don’t make it to the top 10.
- Down the road, which country looks best, demographically?
Easy answer: The US.
The US continues to be an immigrant’s dream. So the demographics stays well balanced ‘tween old and young. The other “advanced” countries end up being hyper-Japans, old folks homes. The big kahuna of the other countries, China, falls off a demographic cliff in a couple decades. China’s median age skyrockets then. Think “greying, 1-child-per-family, spoiled brats”.
Fun stuff.
Techie details:
Apparently, the whole data set is in the WinDOS self-extracting zip file,
This file contains binary data and an ancient DOS program to view the data. XP under VMware is not amused. And you won’t be either.
Textually formatted numbers for the web site’s pyramids are hidden behind HTTP POSTs containing a gob of <input type=”hidden”> values. I’ve not done an automated extraction from the site. I did try a simple tzserver send of the Firefox HTTP headers to get numbers, but had no server response. Must have done something wrong.
The numbers behind the pyramid graphs may be had from pages such as (link is to the US):
Better, simpler numbers may be had from data pages such as (link is to the US):
http://www.census.gov/ipc/www/idb/country/%%%%%COUNTRY_CODE%%%%%portal.html
Here is the US table as retrieved from the population pyramid POST.
Letter to senators
Please vote against the auto bailout.
Here is why I believe that you should vote against making these loans:
- You have no safe, viable, honest source for this money.
Government loans must come from:- Taxes – From whom? Higher taxes in down times are not a good idea.
- Borrowing – We are already too far in hock. And shouldering aside private borrowers by undercutting them is not a recipe for good times.
- Plunder – Americans do not plunder others.
- Inflation – Stealing from the fiscally prudent has never been a clever move.
- Replacing de-leveraged money (that is, printing money without causing inflation) – This is dangerous skirting with real disaster – not the minor stuff we have seen so far. Available financial knowledge (relative lack of transparency) does not allow gross manipulations to be done from a central control point, as has been demonstrated by the current crisis.
- Buy the companies!?
The market caps of these companies is below the loan amount. It’s cheaper to buy the companies. If that doesn’t make sense – and it certainly does not – then how can these loans make sense?
- Have you personally loaned money to these companies?
If not, why not? How can you ethically loan other peoples money on terms you would not take, yourself?
- Loans should not be made available to only particular companies.
Why not make cheap loans available to all of the auto companies? Or all companies? What is special about these companies that they should get a special deal? Even if there is nothing unethical about the deal, there is a clear appearance of corruption of public officials in this bailout. Appearances count.
- These loans set a bad example for other countries.
America has provided leadership to the world. There is no reason we should stop doing so.Do transnational companies headquartered in our country deserve a special boost? What do they need to pay to government officials for such special treatment? Does such behavior sound like something we should be doing, as Americans? Can we be surprised or upset if other countries follow the same path?
If we want to go to economic war with other countries, then we have an easy, crushing win by simply slashing corporate income taxes and opening up H1B and green cards to higher-end foreigners. Do what has worked for every single successful economic unit in post-industrial history: make it easy to start and run a profitable business.
- Taxpayers should not be in the auto business.
There is no reason to expect that either the American citizen at large, taxpayers, or Congress-people have the expertise, time, inclination, or ability to be car builders. I might be highly skilled at my vocation. You might be highly skilled at yours. But that makes neither of us highly skilled at designing, building and selling cars. Let’s not fool ourselves into arrogantly thinking otherwise.When we make a loan we had better trust the person we loan to. And we need to verify that they are not frittering away the money. But, we have no business doing their job or calling the shots for them. If the loan requires that we do so, let’s find another place to loan the money.
If there are legal reasons – able to be reversed by congress – that make it impossible for these particular companies to get loans from people who are loaning their own money, then please fix that problem. Please do not try to fix that problem by making another problem.
Let’s address various arguments that could be made for these loans:
- These loans will keep a huge number of people employed.
How? Can these people build a competitive car? If not, then why, exactly do we need to buy their product? If so, then why, exactly, do they need to be treated specially? They can, after all, support themselves by building and selling cars! - These loans keep a key manufacturing capability inside the country.
How do sweetheart loans to particular multi-national companies do this? Can Americans (or more accurately, American robots) not manufacture competitive cars? And, why is this particular industry so specially needed? We are not currently on schedule to fight WWII again. Why take money that could be spent on beefing up strategic American strengths in cyber-tech or bio-tech (for instance, if we insist on going in hock to bet on particular industries) and fritter it away on the last war’s weapons? - These loans somehow say that the US refuses to fall in to a depression.
They say the opposite. That last depression was an artificially drawn-out, final transition out of a farming based economy. Do we need another depression, war, and new generation of people to get us mentally out of a manufacturing-based economy? Our economy is no longer based on employment in manufacturing no matter what sentimental, rosy-eyed reactionaries may think.Thank God.
Those “good jobs” in manufacturing were horrible jobs. Ask those who have worked “on the line.” And, please do not make the mistake of selecting a special-case time period (like the late 40’s and 50’s in the US) as a counter example. Those wonderful days of manufacturing look very, very poor compared to the wonderful days of web-programming of the late 90’s internet bubble. Special cases are easy to come by. But they are not examples of how to handle ourselves in other times.
- These loans will somehow give confidence to those whose lack of confidence is an impediment to better economic times.
Who might those unconfident people be?- Investors?
Investors (and I speak as one on the cusp of “retirement age”) are rightfully worried … worried because with all the normal uncertainties that must be dealt with, we must add the uncertainty of a gigantic government floundering around the financial world, taking our savings, side-tracking the fruits of the labor of those who support kids and old folks, and generally providing theater rather than clarity, honesty, and prudence.
- Others who might make such loans?
Others who might make loans are certainly not foolish enough to compete against someone with a multi-billion dollar weapons system budget and who, as de-facto world policeman, gets first divvy when loans go bad.
- Investors?
- These loans are a great deal.
Others who might make these loans are charging way, way too much out of fear?Right. And we needn’t worry because we, the taxpayers, will find a greater fool.
We might safely pay attention to someone with a track record of successes in such things, and who makes this argument shortly before piling their own money on to this particular bet. But this argument is notably not made by such people.
We can safely assume that we, the taxpayers, are in great danger of conning ourselves when we believe this argument.
- If the loans are not made, the taxpayer will pay the automakers’ pensions.
No they won’t. And, sure, some assert otherwise.Both “arguments” are simply assertions.
The WWII generation had a lot of babies to support them in their old age. The early baby boomers did not. Caring for the old folks will be a problem soon. This is a problem that should not be solved piecemeal by handing out goodies to those who use their power to elbow their way to the front of the line.
- These loans are a way to inject money in to a money-starved financial system.
If true, is targetting money toward particular, politically connected recipients the way to do this? Or should the recipients bid for money on equal terms with everyone else?Too, what mechanisms are in place to remove this money when the financial system returns to providing the trust they sell for a living.
- If we do not make these loans now, we will do something even worse when the new congress takes office.
The Democratic “branding” of the new congress might suggest this, but such a reality cannot be counted on.There has been enough panic and pass-the-buck behavior as it is. Help put a stop to it. A lot of professional financial people are scrambling to shuck their losses. They are doing rather well at this as they have a supply of rubes to take the bag off their hands. Time is on the rubes’ side here. Time for more sober thought. Let that time pass. And do not do the cynical thing and make a foolish move with the idea of blaming the outgoing administration for everything when the move turns sour.
Managing our resources and money is hard enough to do without needing to write legislators in desperate attempts to escape enforced spending.
Please do your part to stop the irrationality and vote these bailouts down.
On the subject of coins
Rooting through the boxes under the stairs for a blender to extract DNA from dried peas led me to a coin stash from way, way back.
Some of these coins came from “grandma” – as in my mother’s mother.
1809 half penny anyone?
I like the silver dollars. You don’t see a lot of them around in stores these days.
So what’s with the “Tax Token”? … … Ah. The Internet. Always waiting to answer your questions. It’s things like tax tokens that remind us of worser times before our births.
Speaking of which: who can forget steel pennies from WWII?
Wanna play some poker with bill serial numbers?
Several years ago, I started sporadically collecting bills with good serial numbers.
No cheating here. They all came through my wallet in day-to-day use.
So. Is a straight a flush, too?
And, what about 6, 7, or 8 card poker?
The Fed says that there is around $800 billion in cash out there. That’s 2 grand, plus, for every man, woman, and child in the states – over $100 for every human alive. Someone’s carrying around one … thick … wallet.
And $800 billion is very close to the bailout numbers. Hmmm. Coincidence? … … … I think not.
Let the meme begin.
Socializing gains, privatizing losses
Soon we’ll see observations that the Wall Street bailout expression “privatize the gains, socialize the losses” has a flip side.
Note that you can’t take more than $3000 in capital losses per year. Long ago, as is now possible with capital losses, you could spread one-time income over years of taxes. Not now. In effect, the current US tax system is one of “socialize the gains, privatize the losses”.
Ah, to be 17 again – outraged and baffled by the world’s inscrutable workings.
Who owns public corporations?
Who are the owners of a public corporation?
Now, that should be clear: the stockholders, right?
Nope.
Currently, direct stockholders rent companies like we rent cars. That is, the rental can be stopped and started very easily and cheaply.
When you buy a stock, it’s good to think in terms of your being an owner. Certainly, I like to. But the reality is, you’re not. You’re usually using the company as a tool to make a buck, much as I might use a computer or a carpenter might use a hammer.
Too, for the most part, stockholder “owners” are 1 step removed from stock ownership. Large (e.g. mutual) funds own public corporations. Individual “owners” have shares of funds, diversifying their “ownership” in an effort to avoid responsibility for isolated, horrible events in individual companys’ lives. Yes, the fund managers should be awake at the wheel, but the fact is, for them, it’s Other People’s Money. And, by design, index funds are completely asleep at the wheel.
The odd thing about this rental situation is that it brings to mind an explanation of why voting was often, historically, restricted to landed, property owners.
Why?
Because property owners cannot easily leave their homes. They must bear the brunt of bad votes. Non-property owners can hit the road after messing up a place with their bad votes. The objective of property requirements for voting is to balance power and responsibility. If you’re not responsible, why should you have power?
If stockholders are company renters, then should they even have a vote?
Well, what is “ownership”?
Having not been cursed by a too much formal education, I’ve discovered on my own that a private property economic system works miracles for a commendable reason: responsibility and power are balanced. This balance is A Good Thing. In particular (worth noting in a world that emphasizes the powers of ownership), if you know the owner of something, you know who to shoot when the thing does wrong. The “owner” is responsible.
I say that the “owner” is the person with power and responsibility. And their power and responsibility must be well balanced or their ownership is unstable and will not last.
Power and Responsibility in public companies:
Who has the power in public companies (that is, who calls the shots)?
Ordered by concentration of power in individuals, I’d say:
- CEO.
- Other high level execs.
- Board members.
- Other employees.
- A toss-up between citizens of entities the company pays taxes to and the ultimate, individual stockholders.
That is to say, the CEO has more power than any individual, high level employee, each of whom has more power than each board member, each of whom …
The order of this list varies, of course. There are companies with board members voting large percentages of the company’s stock. The distinction between various employee levels is fuzzy. What decisions are important? Etc.
Who has half the responsibility (that is, who takes the hit when things go wrong)?
- Other employees (markedly so in a company-town situation).
- Other high level execs.
- CEO.
- A toss-up between citizens of entities the company pays taxes to and the ultimate, individual stockholders.
- Board members.
Who has the other half of the responsibility (that is, who gets the goodies when things go right)?
- CEO.
- Other high level execs.
- A toss-up between citizens of entities the company pays taxes to and the ultimate, individual stockholders.
- Other employees (more so in a company-town situation).
- Board members.
Note the toss-ups.
One could think of taxable entities as being part owners of companies. After all, if you and your buddy had a store that made $100 every month and your buddy took $70 and you took $30, who owns the store? Looks like a 70/30 split, right?.
Now consider how much of the store’s earnings go to the tax man … which gets interesting when you consider that your partner, the tax man, can raise or lower his cut at any time without your agreement. … Some partner. … But I digress.
That citizens and stockholders are a ranked together as a toss-up emphasizes the rental aspect of stock holdings.
Conclusions:
- The board members are on the high side of the teeter-totter, with low level employees, shareholders, and the taxman looking up from the other side.
- The CEO and other high level executives are the company owners.
It would be nice to put some numbers on these rankings. And calculate the numbers’ changes over time. That is, are these rankings mostly correct, and are they different from, say, 50 years ago?
Maybe these conclusions would be different if I appreciated more the board’s responsibilities.
But, the conclusions are unexpected. … By me, anyway.
Compression Methods
I count 3 ways to compress data:
- Use short strings/symbols often, long strings rarely (e.g. Huffman coding, Zipf’s law’s effect on words, I me you the / prestidigitation onomatopoeia)
- Refer back to things (e.g. zip, gif, lz, “One if by land, two if by sea.”, symbols)
- Remove unwanted stuff (e.g. jpg, mpg, stop reading boring stuff)
Are there any others?
Our living language
It was time to scan the B2 todo.doc idea file (hashes of which are published to alt.security.keydist for lack of a better, public place to dump ’em).
One of the odd items in B2 was a note of curiosity about what words would change in the future. Specifically, what words would be shortened because they are used a lot? And what words would drop out of use because they are too short for their own good? I speculated that words that are too short are pompous, fuddy-duddy words, scheduled to go out of use, and words that are too long are hip-happening words, scheduled to be replaced by shortened forms of the word (“something” becomes “sum’em”, “about” becomes “bout”, “OK” becomes “K”).
The thing is, words that are in common use are short, e.g. “I the you me”. And rare words are usually big words. That makes sense. Huffman type compression is a natural phenomenon.
There are lots of word list out there. I turned to Wiktionary’s TV script word frequency list.
And, from a while back, I just happened to have a copy of all the audio word recordings from Merriam Webster.
Now, the durations of these recordings are not a very good indication of the duration of the words, but it’s a start. (I considered using a phoneme count from Wiktionary’s pronunciation guides)
If you sort the words by word-count and give them each an index corresponding to where they are in the list, and do the same for durations, you should be able to figure out which words have very different indices/rankings in the two sorted lists.
The sorted, absolute-value results should order the words in “stability”. That is, the words at the top of the list should either be too-short words, or too-long words.
Here they are, grouped:
Too-short, fuddy-duddy words:
aught 6 15795 4074 37 0.99775 kiddy 6 15795 4331 93 0.99434 chirp 6 15795 4516 170 0.98965 pomp 6 15795 4599 220 0.98661 clunk 6 15795 4844 390 0.97626 teat 6 15795 4925 473 0.97121 hera 6 15795 4965 524 0.96810 peat 7 15269 4161 56 0.96329 pic 7 15269 4163 57 0.96323 debit 6 15795 5040 607 0.96305 wort 6 15795 5101 678 0.95873 cud 6 15795 5158 750 0.95435 deft 7 15269 4574 205 0.95422 yolk 7 15269 4687 267 0.95045 berth 7 15269 4778 338 0.94612 putter 7 15269 4804 352 0.94527 aright 6 15795 5282 925 0.94369 airy 7 15269 4868 414 0.94150 capper 6 15795 5345 1013 0.93834 heller 7 15269 4934 488 0.93699 amuck 7 15269 4973 542 0.93371 heady 7 15269 4989 552 0.93310 lite 7 15269 4990 554 0.93298 punt 8 14787 4359 107 0.92967 alum 7 15269 5051 619 0.92902 bauble 8 14787 4426 132 0.92815 pellet 7 15269 5070 637 0.92792 breadth 8 14787 4554 188 0.92474 beagle 8 14787 4554 188 0.92474 erie 8 14787 4702 278 0.91926 buoy 7 15269 5176 785 0.91891 bap 7 15269 5189 802 0.91788 simp 7 15269 5203 824 0.91654 wisp 6 15795 5519 1378 0.91612 whist 6 15795 5537 1402 0.91466 ardent 8 14787 4821 372 0.91354 beech 8 14787 4842 385 0.91275 heath 8 14787 4852 399 0.91189 dewy 8 14787 4859 404 0.91159
Too-long, hip-happening words:
relationship 3880 543 10302 15572 -0.91352 responsibility 1157 1262 11876 16298 -0.91219 apologize 1932 885 10730 15895 -0.91153 themselves 1117 1287 11861 16296 -0.91048 affair 865 1527 22434 16425 -0.90314 investigation 951 1433 11692 16260 -0.89905 sharon 1750 957 10540 15751 -0.89820 outside 4260 514 10052 15284 -0.89782 situation 3359 595 10107 15354 -0.89695 opportunity 1601 1011 10526 15742 -0.89423 experience 1767 952 10388 15638 -0.89164 mia 1105 1295 10838 15953 -0.88910 information 3063 635 10028 15251 -0.88815 realize 4114 527 9922 15110 -0.88641 explanation 847 1545 11186 16119 -0.88337 surprise 3439 585 9908 15091 -0.88158 otherwise 1499 1051 10273 15537 -0.87922 suppose 2952 652 9838 15009 -0.87234 security 2237 817 9960 15164 -0.87133 necessary 1215 1219 10288 15561 -0.87005 conversation 2266 808 9919 15107 -0.86843 besides 3355 596 9738 14868 -0.86731 absolutely 4704 482 9648 14724 -0.86576 ridiculous 1943 881 9941 15138 -0.86570 downstairs 1071 1327 10333 15596 -0.86534 girlfriend 2325 789 9846 15014 -0.86397 eventually 985 1400 10380 15634 -0.86303 ourselves 1525 1041 9980 15200 -0.85934 sometime 871 1518 10440 15680 -0.85836 someplace 1165 1253 10132 15384 -0.85712 grandfather 1006 1377 10191 15444 -0.85292 sacrifice 472 2244 11921 16308 -0.85063 recognize 909 1470 10225 15486 -0.84959 psychiatrist 463 2272 11731 16269 -0.84648 necessarily 478 2222 11402 16186 -0.84459 champagne 1085 1316 9998 15220 -0.84315 understand 16724 191 9299 14020 -0.84133 meantime 701 1741 10275 15540 -0.83572 imagination 459 2284 11062 16060 -0.83300
Well, it was a thought, anyway.
Some data and code:
Here is a selection of the results in “stability” order from least to most stable:
; Thu Oct 30 16:38:35 2008 ; counts=15795 durations=16428 unique_counts=2110 unique_durations=5812 ; Word count cnti dur duri offness aught 6 15795 4074 37 0.99775 kiddy 6 15795 4331 93 0.99434 chirp 6 15795 4516 170 0.98965 pomp 6 15795 4599 220 0.98661 clunk 6 15795 4844 390 0.97626 teat 6 15795 4925 473 0.97121 hera 6 15795 4965 524 0.96810 peat 7 15269 4161 56 0.96329 pic 7 15269 4163 57 0.96323 debit 6 15795 5040 607 0.96305 wort 6 15795 5101 678 0.95873 cud 6 15795 5158 750 0.95435 deft 7 15269 4574 205 0.95422 yolk 7 15269 4687 267 0.95045 berth 7 15269 4778 338 0.94612 putter 7 15269 4804 352 0.94527 aright 6 15795 5282 925 0.94369 sometimes 5596 431 10772 15920 -0.94179 airy 7 15269 4868 414 0.94150 capper 6 15795 5345 1013 0.93834 heller 7 15269 4934 488 0.93699 amuck 7 15269 4973 542 0.93371 heady 7 15269 4989 552 0.93310 lite 7 15269 4990 554 0.93298 punt 8 14787 4359 107 0.92967 alum 7 15269 5051 619 0.92902 bauble 8 14787 4426 132 0.92815 pellet 7 15269 5070 637 0.92792 breadth 8 14787 4554 188 0.92474 beagle 8 14787 4554 188 0.92474 erie 8 14787 4702 278 0.91926 buoy 7 15269 5176 785 0.91891 bap 7 15269 5189 802 0.91788 simp 7 15269 5203 824 0.91654 wisp 6 15795 5519 1378 0.91612 whist 6 15795 5537 1402 0.91466 ardent 8 14787 4821 372 0.91354 relationship 3880 543 10302 15572 -0.91352 beech 8 14787 4842 385 0.91275 responsibility 1157 1262 11876 16298 -0.91219 heath 8 14787 4852 399 0.91189 dewy 8 14787 4859 404 0.91159 apologize 1932 885 10730 15895 -0.91153 themselves 1117 1287 11861 16296 -0.91048 catty 8 14787 4892 433 0.90982 contra 6 15795 5575 1485 0.90961 droop 6 15795 5579 1502 0.90857 gluck 6 15795 5582 1512 0.90796 yammer 8 14787 4921 468 0.90769 affair 865 1527 22434 16425 -0.90314 cherub 6 15795 5630 1603 0.90242 inca 8 14787 5005 567 0.90167 ogle 7 15269 5385 1082 0.90084 millet 7 15269 5385 1082 0.90084 bey 6 15795 5642 1636 0.90041 creak 8 14787 5025 588 0.90039 bunt 8 14787 5032 593 0.90009 amah 6 15795 5644 1645 0.89987 whet 9 14316 4394 119 0.89912 investigation 951 1433 11692 16260 -0.89905 wrought 8 14787 5050 617 0.89862 sharon 1750 957 10540 15751 -0.89820 dietrich 6 15795 5655 1677 0.89792 outside 4260 514 10052 15284 -0.89782 dour 7 15269 5412 1140 0.89730 situation 3359 595 10107 15354 -0.89695 velour 6 15795 5660 1694 0.89688 hatter 6 15795 5665 1711 0.89585 conk 8 14787 5109 687 0.89436 opportunity 1601 1011 10526 15742 -0.89423 cooker 9 14316 4579 210 0.89358 ilk 9 14316 4620 231 0.89230 sot 7 15269 5455 1227 0.89201 experience 1767 952 10388 15638 -0.89164 batty 9 14316 4682 264 0.89029 mia 1105 1295 10838 15953 -0.88910 thomson 6 15795 5720 1826 0.88885 baroque 6 15795 5724 1831 0.88854 eth 6 15795 5725 1833 0.88842 information 3063 635 10028 15251 -0.88815 tusk 7 15269 5487 1294 0.88793 anima 7 15269 5496 1312 0.88683 realize 4114 527 9922 15110 -0.88641 vigor 8 14787 5199 820 0.88627 brusque 7 15269 5513 1349 0.88458 corker 8 14787 5234 867 0.88341 explanation 847 1545 11186 16119 -0.88337 woolly 8 14787 5237 872 0.88310 demur 6 15795 5759 1925 0.88282 coolant 6 15795 5760 1928 0.88264 peeve 6 15795 5765 1939 0.88197 . . . Somewhere in the middle of the list... . . cockpit 42 8131 6575 4544 0.23818 dummy 166 4147 4858 401 0.23814 hut 169 4103 4809 356 0.23810 home 22901 156 6450 4073 -0.23805 shorthand 28 9689 9285 13988 -0.23805 twirl 34 8949 6793 5397 0.23805 free 5433 440 6531 4368 -0.23803 aspire 25 10179 7094 6677 0.23800 sheila 352 2688 7102 6704 -0.23790 component 40 8299 8687 12538 -0.23779 pathetic 1115 1289 6749 5247 -0.23779 tune 344 2716 7109 6731 -0.23777 sped 21 10897 7282 7428 0.23775 excel 16 12021 7561 8598 0.23769 chemotherapy 23 10525 9729 14851 -0.23766 conduct 234 3426 7293 7465 -0.23750 tribal 47 7696 6456 4103 0.23749 hitch 121 4863 5421 1157 0.23745 cult 170 4091 4808 355 0.23740 envision 13 12907 7797 9526 0.23729 glib 33 9036 6819 5500 0.23729 libel 18 11532 7441 8097 0.23723 willed 39 8399 6649 4839 0.23719 lecturing 65 6604 8146 10765 -0.23718 grapefruit 65 6604 8146 10765 -0.23718 severance 36 8727 8846 12973 -0.23717 virtual 57 7018 6261 3403 0.23717 from 59972 74 6420 3973 -0.23716 fungus 58 6968 8257 11143 -0.23714 illinois 153 4326 7511 8395 -0.23713 decoration 27 9842 9351 14131 -0.23707 exploitation 17 11781 11263 16147 -0.23703 champ 144 4460 5151 745 0.23702 compartment 55 7129 8307 11307 -0.23693 iceberg 60 6862 8220 11028 -0.23685 . . . The most "stable" words... . loose 1069 1331 5516 1367 0.00106 scottie 56 7070 7258 7336 0.00106 arrival 149 4385 6584 4578 -0.00105 radioactivity 6 15795 13593 16411 0.00103 sung 65 6604 7147 6885 -0.00099 pilgrimage 9 14316 9766 14906 -0.00099 eyeball 35 8825 7702 9163 0.00095 heal 466 2260 5904 2335 0.00095 scrabble 39 8399 7596 8751 -0.00094 provoke 93 5547 6886 5784 -0.00089 iron 314 2875 6128 2976 0.00087 extortionist 7 15269 10730 15895 -0.00086 rubbish 54 7186 7299 7488 -0.00085 cavalier 28 9689 7948 10091 -0.00083 get 126849 37 3948 25 0.00082 nick 2699 704 5132 719 0.00080 integration 15 12307 8785 12813 -0.00078 pedestal 65 6604 7140 6856 0.00077 ringing 281 3078 6201 3213 -0.00071 yo 1347 1138 5429 1172 0.00071 platter 116 4967 6726 5155 0.00067 stifler 14 12603 8898 13119 -0.00066 cat 1742 960 5330 988 0.00064 relate 172 4061 6487 4214 0.00059 machismo 8 14787 10116 15370 0.00058 altitude 52 7303 7325 7605 -0.00057 fetish 43 8040 7501 8353 0.00056 ton 304 2941 6157 3068 -0.00056 twentieth 41 8212 7542 8532 0.00055 telltale 10 13925 9533 14492 -0.00054 montage 7 15269 10700 15873 0.00048 toll 113 5030 6742 5224 0.00046 fabricate 13 12907 9016 13418 0.00038 misrepresentation 6 15795 15362 16422 0.00037 buster 249 3295 6271 3433 -0.00036 primal 49 7517 7375 7824 -0.00035 alvin 65 6604 7142 6863 0.00034 tumor 225 3506 6328 3641 0.00034 book 5027 468 4937 492 -0.00032 voyage 106 5215 6799 5429 -0.00030 hug 697 1751 5720 1826 -0.00029 demon 1703 977 5349 1020 -0.00023 marietta 21 10897 8313 11330 0.00023 empty 1261 1183 5457 1234 -0.00022 bootleg 19 11306 8435 11756 0.00019 jordan 266 3167 6229 3297 -0.00019 theses 9 14316 9761 14892 -0.00014 letter 1839 925 5311 960 0.00013 cheer 550 2046 5834 2130 -0.00012 maiden 82 5896 6965 6134 -0.00010 toaster 76 6107 7016 6352 -0.00002 castle 408 2451 5977 2549 0.00001
Here is the code:
class a_word(object) :
def __init__(me, word, cnt, dur) :
me.word = word # the word
me.cnt = cnt # the word's use count
me.dur = dur # the word's shortest .wav file byte length
me.cnti = 0 # the normalized ranking of the count (low rank are frequent words)
me.duri = 0 # the normalized ranking of the duration (low ranks are short words)
me.off = 0.0 # how far off the two rankings are
pass # a_word
#
#
if __name__ == '__main__' :
import os
import re
import sys
import time
import TZCommandLineAtFile
import tzlib
sys.argv.pop(0)
TZCommandLineAtFile.expand_at_sign_command_line_files(sys.argv)
wc_fn = sys.argv.pop(0)
wcs = tzlib.read_whole_text_file(wc_fn) # lines of: "word count (wav_size (...))" - we use the shortest .wav size
wa = re.split(r"\n", wcs)
wa = [ wc for wc in [ re.split(r"\s+", ln) for ln in wa ] if (len(wc) >= 3) and (wc[0][0] != ';') ]
words = []
for wc in wa :
wc[1] = int(wc[1])
words.append(a_word(wc[0], wc[1], min([ int(ln) for ln in wc[2:]])))
words.sort(lambda a, b : cmp(b.cnt, a.cnt))
i = 0
j = 0
icnt = 0
ucnt = 0
for w in words :
if icnt != w.cnt :
icnt = w.cnt
i = j
ucnt += 1
w.cnti = i
j += 1
icnt = float(i)
words.sort(lambda a, b : cmp(a.dur, b.dur))
i = 0
j = 0
idur = 0
udur = 0
for w in words :
if idur != w.dur :
idur = w.dur
i = j
udur += 1
w.duri = i
j += 1
idur = float(i)
for w in words :
w.off = (w.cnti / icnt) - (w.duri / idur)
words.sort(lambda a, b : cmp(abs(b.off), abs(a.off)))
print "; " + time.asctime()
print "; counts=%i durations=%i unique_counts=%i unique_durations=%i" % ( int(icnt), int(idur), int(ucnt), int(udur) )
print
print "; %-30s count cnti dur duri offness" % "Word"
print
for w in words :
print " %-30s %8u %5u %6i %5u %8.5f" % ( w.word, w.cnt, w.cnti, w.dur, w.duri, w.off )
print
print ";"
print "; eof"
What I’ve learned
There are two prices for everything. The two prices are known in the stock market as “bid” and “ask” prices. The “bid” price is the highest price someone is willing to buy. The “ask” price is the lowest someone is willing to sell.
Fine.
What seems to be true is that the existence of these two prices is the reason for the existence of money. And, the existence of these two prices is the reason why the arithmetic works out so counter-intuitively in Ricardo’s trade calculations.
Why?
Well, say you and your friend each have a car. Your tastes and needs run about the same. Your cars are the same. But, for both of you, it’s time to move somewhere over the waters. You live in Metropolis. Your friend lives in Timbuktu. Who gets the better deal for their car?
You do.
Heck, your friend will be lucky to get any price above zero. You have lots of buyers available. You simply take the highest bid.
Now, you and your friend have moved. You moved to Gotham City, and your friend moved to Podunk. You’re both in the market for a car. Who gets the better deal?
You do.
You have plenty of cars to choose from. You go with the guy asking the least for his car. Your friend probably has no cars to choose from. He’ll need to pay for the trip to Gotham City to buy one.
Now, isn’t that interesting. In the big city, both the buyer and the seller got a better deal. How is that possible?
It results from the “spread” ‘tween the bid and ask prices. In the city, that spread is narrower than in the boonies. In the city, the bid price for a jalopy might be $5000 and the ask price might be $5500. In the boonies, the bid price might be $0 and the ask price might be infinite. You lost $500 in your move. Your friend? Well. It’s sad.
The conclusion from this:
The larger the market, the more efficient it is. And by more “efficient”, I mean the narrower the “spread” is.
So, that explains the existence of money. Money is a way to combine in to one big market, many markets, each for a separate thing.
The “larger market is more efficient” principle is the heft behind Ricardo’s observations.
And others things too numerous to remember.
Anyway, that’s the way I look at it.