There are plenty of stories you can tell that are pretty compelling about this.  For example there are towns in China today that make only buttons, and other towns that make only copies of classic oil paintings, etc. etc.    Everybody knows that Silicon Valley has something in the water that means that only there can you do high-tech.

But recently I’ve gotten to wondering, maybe this isn’t true.  Cities, as a economic model, lost much of their competitive advantage with the introduction of the phone (which undermined

the manager’s need to be on site), electricity (which undermined the requirement for power/coal to be delivered via flat water), and modern transportation (which undermined the requirement to walk to work).   Once those all set in the city centers dissipated.  And thus, the golden age of American cities ended.  Is that process over?  I think maybe not – the way that the internet has enabled distributed work is a good example of the ongoing process.

How might we measure this?  Apparently Paul Krugman suggested some time ago the seemingly obvious idea that you just compare “the sum of the absolute differences in industry shares of employment between the two regions.”  (page 12)  And in 1998 this paper was published with this chart that shows the historical trends in regional specialization in the US.   Notice first the bottom line – retail trade; all regions have some retailing so that isn’t concentrated.   Then notice the top one; apparently the least urban activity is the one that is trending toward increased regional specialization.  I certainly didn’t expect that.

What leaps out at me here is the way the rise and fall of regional specialization in manufacturing appears to be so correlated with the gold-age of American cities.

I think I was right at the time.  Wrong about my choice of cities; San Francisco beat Boston – a lot.  I remain unconvinced that we know exactly why though.   These days I still think it is good advice to the young, go someplace where there is an existing network of people doing what your interested in.  But, that said, I think it’s less critical than it used to be.   And, I’m totally confused about that agriculture trend line.

Here’s another power-law.

Apparently the distribution for the interval between epileptic seizures is a power-law.  The long intervals are rare.  There are lots processes that give rise to a power law distribution.  The proposed model in this case involves a cascading failure in the  network of neurons.

I don’t think this next paper has undergone peer reviews, but in  this paper they look at the interval between a serial killer’s murders.    That’s a thin data sample.   The interval between his crimes is power-law distributed.

Interesting example of impulse control, will power power, etc.

I wonder if we have other data on brainstorms?   Brainstorms of a less horrific kind:  interval between blog posts, emails, phone calls, code commits, sales closed, etc. etc.   What if we took the top hundred poems Robert Frost wrote and measured the interval between their creation?    Would we see that as power law distributed?  With enough data we could probably look into the effect of coffee on brainstorms.

The vertical axis on this chart is a measure of how likely you are to have an income that differs from that of your parents.  The horizontal line is the usual measure of income inequality.  So if you live in the UK or the US I recommend picking good parents.

So we now know that income inequality has high social costs, or to say it in a more technical way inequality is negatively correlated with social welfare.  I don’t know that that would surprise most people.    A society where the lower classes are more distant from the upper classes is going to have greater social stress – at least I don’t see that as surprising.

But what else is income inequality correlated with?    There is a very scary possibility:  That inequality drives greater economic growth.  Not hard to make up an insta-theory for that: e.g. that the social gradient drives people to strive, and this drives significant economic growth.

That would be really horrific.   A Hobson’ choice: pick one economic growth v.s. social well being.  Societies that grow faster carry their social norms carried along with them.  They become the standard.

Which is why it’s a very important question.  And you’d think there would be libraries full of research on this question.  There is not.  Pick your insta-theory for why that is.   As far as I can tell the data seems to suggest that growth and inequality are largely related.  But, it is very frustrating not to be sure.

Writing about Amazon: “Organizing into services taught teams not to trust each other in most of the same ways they’re not supposed to trust external developers”

Or writing about a fundamental truth: “Accessibility has an evil twin who, jilted by the unbalanced affection displayed by their parents in their youth, has grown into an equally powerful Arch-Nemesis (yes, there’s more than one nemesis to accessibility) named Security. And boy howdy are the two ever at odds.”

But then getting to Google’s deepest fundamental tragic flaw: “one last thing that Google doesn’t do well is Platforms” … “product is useless without a platform”   …  ”.Google+ team took a look at the aftermarket and said: “Gosh, it looks like we need some … Let’s go contract someone to, um, write some… for us.”  … “Any teams that have successfully internalized the notion that they should be externally programmable platforms from the ground up are underdogs” … “The problem is that we’re a Product Company…”

ok, nevermind.  But,  I think it’s shockingly exactly right and in the long run it’s not a bad reason to short Google.

If I’ve peak’d your interest read the whole thing.

I suspect this says something about Hal Varian’s status in the senior leadership.  I presume he understands this.  But maybe not how fundamental it is.

This is the log-log graph of the top 50 banks based their assets.  The vertical axis is dollars; i.e. there are four banks with over a 1e12 or a Trillion dollars in assets.

Income is never distributed uniformly.  What matters is how severe that becomes.   Since what the top 10% want from their tax payments is fundamentally different from what the other 90% want this leads directly to class warfare.  Denying that isn’t insane.  The top 10% don’t need the government to provide: health, schools, etc. etc. — at least not outside their increasingly isolated enclaves.  Which in turn is why your schools, your healthcare suck, your safety net suck.

The negotiation literature is full of examples where the negotiations fail, in spite of the existence of a deal to be had, because the two parties reveal information along a path that leads to a lousy outcome.  The simplest example is negotiating over price.  The rule of thumb is that once each side has bid a price the only possible outcome, short of a great deal of negotiation, is to split the difference.  The rub here is that each party has a space of acceptable deals and the challenge of the negotiation is to both discover if they overlap and then give that find a good point to close on.  But the moment one side reveals anything about his space of acceptable outcomes the other side will adjust what he reveals.

The following illustration is taken from a paper that suggests a way, using cryptography, that two parties might check if they have found common ground without actually revealing anything about the ground that is acceptable to them.   The paper frames the match making as a romantic problem (Romantic Cryptography (pdf) in the Journal of Craptology).  The two sides want to know if they love each other without revealing their love.

The balance beam below tries to solve this problem.  The players place tokens on the right side.  These tokens are identical to the eye, but are either light or heavy.  In the drawing the heavy tokens have a dot shown in their center.  A player who loves the other player places a heavy token on the scale.  Once both players have placed their tokens a pin is removed and  if the scale falls they both love each other.

You could use this in price negotiations.   You using a spinner to suggest a price and then both sides play a round; repeating until a mutually acceptable price is discovered.   It could also be used to make all kinds of self revealing games; say with a deck of cards that ask questions.

Of course in this story the device, the scale, is acting as the middleman.  The paper sketches out how to do this kind of thing with cryptography.

I wonder if there is a simple way to cobble together a scale and tokens like this out of stuff likely to be lying about.  The need does arise.   Say for example when a team wants to know if they all think it’s time to cancel the project, delay the release, switch database engines, fire the project manager, hire this guy, …

The paper goes on to describe variation on the technique using transparencies.

Each player is provided with two transparencies, a yes and no vote, that look similar the ones above.  If the two yes votes are overlaid you get a heart.  Otherwise you get nothing.   The first approach requires some carefully made tokens and scale that works just os.  This technique requires preparing the transparencies for each round; but they can be manufactured before hand into a deck for use during the negotiation.  It’s fun to note that a similar technique can be used so the players can distinguish if which of their cards are yes or no.

They point out that there is a problem with lying.  If the game reveals both sides love each other it’s still a problem that they might say “Oh, just kidding!”    Placing a bond or playing the game in a group can help to put a price on that behavior.  There is somewhat different problem that raising a question reveals a lot, as in the example “Do you think we should fire Bob?”   Some of that can be addressed by creating a swarm of random but oft raised questions.

I gather that it is well known that if you look at the ads in a given newspaper of magazine the ads are targeted toward the high end of their readership. Hence the ads for thousand dollar watches in the New York Times.

Possibly selling into the high end of the distribution is so lucrative that you don’t really use advertising as most of us think of it. The selling channels are different; you give talks at Ted and get booth space at the superbowl.

Which makes me wonder how different the ads are if my IP address is in 10028 (Upper East Side of Manhattan, avg income $227K/year) v.s. 10454 (South Bronx, avg. income $18K/year).  There must be advertisers who have figured that out.

Update: via @gully this cartoon is great too, I’ve added one frame here as an illustration