Let me explain. Suppose that Andy and Benny can also make clay. Andy makes 8 kilos of clay per minute and Benny can make 6 kilos per minute. We say that Andy dominates Benny in the production of clay and play dough. If Andy needed both play dough and clay, the intuitive thing for him to do is to produce both products himself since he can produce both more “efficiently”.

That’s not so. The theory of comparative advantage says that “given all other goods”, in this case, clay, Benny is actually comparatively more efficient at producing clay than Andy. Hence, there’s a benefit for Andy to produce only play dough and trade with Benny for clay. This is because when Andy produces 8 kilos of clay, he could have produced 10 kilos of play dough in that minute. So his opportunity cost of producing one kilo of clay is 1.25 kilos of play dough. However, when Benny makes 1 kilo of clay, his opportunity cost is only 0.67 kilos of play dough, so it’s cheaper for Benny to produce play dough than it is for Andy.

Benny’s ability to produce clay at a lower opportunity cost than Andy means that he has a comparative advantage at producing clay. Assuming of course that in our imaginary economy, play dough and clay sell for the same amount of money, then it is better for Benny to make the clay because his opportunity cost is lower. Similarly, Andy should keep producing play dough.

You could apply this to life and say that everyone should do exactly what they’re good at: if maths is your best subject in school, you should become a mathematician, and if you were better at sports than anything else then you should become an athlete. On the face of it, this sounds like a good rule of thumb to follow, “do what you do best”, especially now that there seems to be an economic theory that proves you should. However, it makes the naive assumption as I did in the Andy and Benny example that everything that anyone does has the same value. That’s never the case. Instead of saying “do what you do best”, the theory of comparative advantage applied to life should say “do what makes you produce the most value”.

It doesn’t sound nearly as catchy as the naive rule of thumb, and value is subjective and can mean anything from economic value, to personal value, to social value, but it is something to think about when making decisions. Life is a series of decisions after all, but I’ll leave that thought for another post.

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• Getting to where they are is stressful. Your work can become your life.
• If success means having influence, people are going to criticize you for decisions you make, especially if those decisions affect them.
• People tend to congregate with others who have similar interests and social statuses, but not many people get to be best selling authors, so it might be harder to find real friends.
• No one else is doing what they’re doing, their future is more uncertain and there’s more pressure on them to make the right choices

However, most extremely successful people seem pretty happy, and the group as a whole are probably a lot happier than those working 80 a week in an investment bank, so it makes me wonder, why did we get that impression in the first place. The suicide rate is highest among high stress professions like dentistry and law, if there was a quantifiable measure of depression that could be applied to those professions and if we grouped the highly “successful” people in a group and measured them, I suspect that they won’t be any more unhappy than the most stressful professions out there.

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One of the main events of the evening was speed dating with employers where each student got to spend five minutes with each employer. So what does a computer science student say to an employer who is looking to hire photo voltaic or mechanical engineers? What do they say to me?

The conversations were varied. I’ll make up some fake names, not to protect the innocent, but I honestly can’t remember all of their names.

John and Steve didn’t know what a computer science students did, so I tried to explain. That is when I realized that my degree is so broad that even I wasn’t sure exactly what computer science is all about. A computer science student will rarely continue to pursue the maths and science behind computing, many won’t even study much of it. Some of us focus on low level software such as operating systems, while others are interested in abstract subjects such as artificial intelligence and formal verification. A large number of us will end up working 9-5 in the IT department of a multinational corporation, a few of us will go into R&D, many more will change careers completely. It was like describing an arts degree.

One of the first things that Bill said to me was “I’ll be honest with you, we don’t really hire any computer science people”. Direct and honest, I liked that. It’s something that we both knew from the beginning, and getting that out of the way let us move into a more interesting conversation. Others like Jeff tried unsuccessfully to see where I might fit in, “well, we have an IT department, we have about 12 IT people [out of a company of hundreds of engineers] working to help our engineers.” It was nice of him, but unnecessary. I mean, we both know that there’s a 0% chance that I’ll be asking for a graduate job there, so why beat around the bush? It’s social courtesy to try and be nice and leave a sense of hope, but in this case, doing so only made it awkward.

It was clear early on that I was not a match for any of these companies, and knowing that was the chance to turn the conversation around the other direction and ask the employers a lot of questions rather than talking about myself. It was much more interesting to hear about their experiences, especially in managing engineers and other technical people. Since all of them have engineering backgrounds they could go into a lot more detail about what their companies did than an HR or worse a recruitment firm person who will only give a superficial talk about how they encourage creative thinking and foster young minds in a global professional environment, and other jargon from their mission statement.

I had mentioned that I prefer to work for smaller startup companies rather than large multinationals to Glenn. Take some risks while I’m young, and be an entrepreneur. Glenn advised me otherwise. “Work for a big company first and learn how it works,” he advised, “it’s very hard to go from small company to big company, but very easy the other way around”. He has a good point, working in a large company can give me a much broader view of how businesses work, I’ll probably have a much bigger and powerful network to be able to bootstrap a startup company. However, I don’t necessary agree given the likely circumstances. It’s too easy to get too comfortable living on a stable salary. By the time I have enough experience and big enough network to bootstrap a new venture, it might be too late, I probably wouldn’t give up a stable job and take that kind of risk if I’m say, 30 or over.

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I wrote an article about it on Beta, check it out.

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Experience is the thing you get after you needed it

This quote stuck in my mind because it somehow ringed “true”, like I’ve experienced it

The dictionary define experience as

a general concept comprises knowledge of or skill in or observation of some thing or some event gained through involvement in or exposure to that thing or event

Which is a mundane and objective definition, but if you think about it, the two definitions are quite similar. The first one is a little more restricted in that it requires you to have “needed” the experience where as the latter applies more generally to all experience.

The second quote would have never stuck in my mind. It’s distant,  emotionless, and contains no personal pronouns. Understanding the definition requires you to imagine an arbitrary “thing” or “event” and then imagine ones involvement. The first one stuck because it used “you” which immediately draws up first hand personal memories, and those memories are more readily available. It requires less thinking to understand, and a brain like mine likes simple things.

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It will be a fortnightly publication full of tech related interviews, articles, puzzles and more all written by volunteers at the University of New South Wales. Check it out sometime.

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Unlimited IT’s carrier pigeon backed data transfer reached speeds of 1 gigabyte per hour while transferring to a location 100km away. That’s a speed of about 270KB/second.

The idea to use homing pigeons to transfer information is not new. Ancient Egyptians were the first known civilization to be using carrier pigeons 3000 years go. The Roman’s used carrier pigeons to aid them in war over 2000 years ago. The use of carrier pigeons became most prominently known when their use became an important part of World War I and II.

On April fools day 1990, D. Waitzman of Cambridge Massachusetts described an internet protocol using carrier pigeons to transfer data in RFC 1149. This protocol later became known as IP over Avian Carriers (IPoAC).  On April fools 1999, Waitzman improved the protocol in RFC 2549. In 2001 however, a Linux User Group took the idea a little too seriously and implemented the protocol using pigeons to transfer data over a distance of 5km, before Unlimited IT of South Africa implemented this protocol as a marketing stunt.

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Placing words:
1. Words made in Scabbler must be non-scrabble-words. I.e. they can not appear in the Scrabble dictionary.
2. Words made in Scabbler must be well formed, that is, they must sound like a real English word.

Scoring points:
1. Same rules as scrabble
2. with the additional rule that non-scrabble words that anagram to scrabble words receive a multiplier of 1+the number of anagrams found at the time of placing a word.

Scabbing points:
1. A player can scab the points away from another player by contesting the non-scrabble-wordness of another players word by looking it up in the dictionary. If it turns out to be a real word, the contester receives the value of that word in points. If the word turns out to be a non-scrabble-word, then the contester loses half the value of that word in points.

That’s it. Happy gaming!

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Ada
Ala
Ana
Anna
Ara
Ava
Bib
Bob
Eve
Gog
Hannah
Laval
MGM
Malayalam
Nan
Ono
Otto
Salas
Tet
Tevet
Tut
aha
bib
bob
boob
civic
dad
deed
deified
did
dud
eke
ere
eve
ewe
eye
gag
gig
hah
huh
kayak
kook
level
ma’am
madam
minim
mom
mum
non
noon
nun
oho
pap
peep
pep
pip
poop
pop
pup
radar
redder
refer
rotor
sagas
sees
seres
sexes
shahs
sis
solos
stats
tat
tenet
tit
toot
tot
wow

The perl script that found them

map {print; print “\n”} grep {chomp; (lc($_) eq reverse lc($_)) && /../} <>

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What are the most common anagrams? In other words, if you took all the words in a dictionary and grouped all the anagrams together, which groups would be the biggest?

Here are the 100 most common anagrams

stael tesla least slate stale steal tales teals
carets caster caters crates reacts recast traces
pares parse pears rapes reaps spare spear
keats skate stake steak takes teaks
ingres singer reigns resign sering signer
east ates eats sate seat teas
palest pastel petals plates pleats staple
elsa lesa ales leas sale seal
post opts pots spot stop tops
alger elgar glare lager large regal
mensa amens manes manse means names
drapes padres parsed rasped spared spread
andes danes snead deans saned sedan
caret cater crate react recta trace
castor castro croats actors costar scrota
bates abets baste beast beats betas
lapse leaps pales peals pleas sepal
restrain retrains strainer terrains trainers
emits items mites smite times
ester reset steer terse trees
cretan canter nectar recant trance
easts tessa asset sates seats
lois oils silo soil soli
pores poser prose ropes spore
reins resin rinse risen siren
elam elma male lame meal
tosca ascot coast coats tacos
arts rats star tars tsar
moran norma ramon roman manor
ernest sterne enters resent tenser
darin indra randi drain nadir
drano norad ronda adorn radon
arced cadre cared cedar raced
eros rose ores roes sore
drawer redraw reward warder warred
idlest listed silted stiled tildes
angered derange enraged grandee grenade
capers crapes parsec recaps scrape
aster rates stare tares tears
enos eons noes nose ones
sabre bares baser bears saber
mister merits miters remits timers
parleys parsley players replays sparely
artiest artiste attires iratest tastier
miles selim limes slime smile
mabel mable melba amble blame
paste pates septa spate tapes
spears parses passer spares sparse
slater alerts alters salter staler
arno aron nora oran roan
enlist inlets listen silent tinsel
earp pare pear rape reap
alerting altering integral relating triangle
aridest astride staider tardies tirades
dearths hardest hatreds threads trashed
levi evil live veil vile
hares hears rheas share shear
dale leda deal lade lead
leno leon noel olen lone
seder deers deres reeds sered
glaser glares lagers larges regals
cesar acres cares races scare
astor sarto roast sorta taros
ares ears eras sear sera
deltas lasted salted slated staled
kendra danker darken narked ranked
persist priests spriest sprites stripes
abel bela elba able bale
elanor elnora lenora lorena loaner
mates meats steam tames teams
salem selma lames males meals
dniester inserted nerdiest resident trendies
tangiers angriest gantries ingrates rangiest
dorset sorted stored strode trodes
parts sprat strap tarps traps
patel leapt petal plate pleat
sahel hales heals leash shale
seton stone notes onset tones
beard debra bared bread debar
aden dane dean dena edna
claimed decimal declaim maliced medical
sung gnus guns snug
rites tiers tires tries
reduces rescued secured seducer
evan neva nave vane
leary early layer relay
latiner latrine reliant retinal
earth harte hater heart
cortes corset escort sector
asp pas sap spa
construe counters recounts trounces
artsy satyr stray trays
ate eat eta tea
past pats spat taps
esters resets serest steers
depart parted petard prated
boers bores robes sober
piers pries ripes spire
neil nile lien line
ambled bedlam blamed lambed

This is the words list used to create it. The full list of groups of anagrams can be found here, and the perl script used to generate that list below.

map {
   chomp; s/'s//; tr/A-Z/a-z/; s/[^a-z]//;
   $a = join '', sort split //;
   $words{$a} = ($_) if !defined($words{$a});
   $str = $_;
   push @{$words{$a}}, $_ if !grep $_ eq $str, @{$words{$a}};
   }<>;

for $w (sort {
      $#{$words{$b}} <=> $#{$words{$a}}
   } keys %words) {

   for(@{$words{$w}}) {
      print "$_ ";
   }
   print "\n";
}

Some of the words in this list are pronouns so you could say it’s cheating, but it’s the only word list I have at the moment.

Observations

This is an interesting group:

angered derange enraged grandee grenade

Which is the odd word out?

The word ‘anagram’ has no anagrams, what a pity.

logarithms is an anagram of algorithms

secretion is an anagram of erections

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