Later this week, I’m going to argue that the Fed’s recent rate cut is at best an exercise in futility and at worst a mistake.  [another editorial note:  turns out I was wrong, at least on the second part.  exercise in futility perhaps, mistake, no.  see my next post.] First, it’s time to lay the groundwork:

The Creation of Free Debt¹

In the early days of the bail-out debate (way back in the first week of October), Robert Reich gave a talk at the Commonwealth Club in which he, rightly, laid part of the blame for the origins of the current economic crisis at the feet of the Clinton Administration (of which he was a part from 1993 through 1997, as Clinton’s first Secretary of Labor). Regardless of where your politics lie, it’s pretty clear that the roughly 1998-vintage annulment of venerable Glass-Steagall (which among other things created a separation between investment banking and commercial banking activities in the US) was the first in a series of major errors which eventually resulted in today’s crisis.

Add to this something Reich didn’t touch on: the Clinton administration perpetrated the second error later in its watch, when it agreed with numerous Wall Street giants that credit default swaps (the “big bad” CDSs we keep hearing about these days) ought not to be regulated. Why CDOs should have been (and must now be) regulated will be the subject of still another post, but for now let’s stipulate that CDSs are opaque derivative instruments (i.e., not only not transparent but also based on inscrutable “if…then…else” risk structures which, for lack of inspection, can mask huge exposure to global externalities) and that the practice of “netting”²  (in which an investor hedges by purchasing two CDSs with opposite and mutually-exclusive pay-off circumstances, thus “netting to zero” his exposure regardless of which of the two outcomes takes place) has exposed the entire economy to systemic risk of counterparty default. Why? Because netting (offsetting hedges) don’t work if one of the two contracts in the hedge blows up because one of the insurers—think AIG—is suddenly unable to pay. And if everyone is a counterparty to everyone else, well, you get the picture….  (My friend Paul Sheehan, who at one time was in charge of derivatives oversight at money center banks as an examiner for the NY Fed and now runs an Asian event-driven hedge fund, points out that AIG wasn’t actually defaulting;  it was merely subjected to an avalanche of “collateral calls” by counterparties who insisted that AIG set first $20 billion, then $40 billion and then $60 billion aside as collateral against their CDS obligations.)

Enter into this brewing perfect storm the High Priest of Economic Growth, Alan Greenspan, who, along with the Bush Administration, feared that the 2000/2001 bubble bursting and the tragedies of 9/11 might conspire to create a Depression. Greenspan’s Fed—amid much aplomb from the neo-supply-siders of the Bush administration—cut the Fed Funds rate so precipitously and so aggressively (down to 1.00% in early 2003) that, as Reich points out, real interest rates were negative for perhaps a year or two. I took a look at the data and produced the chart at the top of this post, and to be specific, real interest rates were negative for nearly 11 quarters (2.75 years) between 2003 and 2005.  (see figure above)  And, as others have pointed out, the problem wasn’t just negative real interest rates per se, but the fact that such low Treasury rates drove the world of investors who were looking for “safe” fixed income securities to look elsewhere—to CDOs.³

Is it any wonder that everyone and his brother who could support even a moderate credit rating borrowed like there was no tomorrow?(Don’t even get me started about the Ponzi scheme perpetrated by the rating agencies, the bond issuers and their syndicators.)Of course not. If you were a public company CFO from 2003 to 2005, you were crazy not to borrow: it was free! And what did you do with all that money?  You couldn’t invest it into real operations because you couldn’t scale that fast, and neither your business nor the economy wouldn’t support it anyway.  (That was one of many hints:  if your business can’t consume more debt without doing something off the reservation, perhaps you have no business drawing down that debt in the first place!)  And so you gamble with it.  You create demand for new and interesting securities with which to sate your demand for returns on debt you have no business generating in the first place.  So you overheat demand for CDOs, and you create an irresistible temptation for the bankers to create more and more of them.  Commercial banks did it. Investment banks did it.  Even insurance companies did it.

Thus Clinton Administration policies and Bush Administration policies created the perfect storm:

If you have been thinking about the ramifications of what the Fed did earlier this week, you can see where this is going. But that will have to wait for the next post(s)….

1. Inflation data from http://inflationdata.com/inflation/Inflation_Rate/HistoricalInflation.aspx, Fed Funds data from http://www.the-privateer.com/rates.html.  Graph by the author.
2. The best explanation of both the system risk from netting and CDSs in general I’ve come across comes from the absolutely super This American Life/NPR News collaboration “Another Frightening Show About the Economy”, itself a follow-up to an earlier and equally excellent collaboration cleverly titled “The Giant Pool of Money”
3. credit to “The Giant Pool of Money” for explaining this so clearly.

It was a pleasant surprise to discover that my American Airlines flight this evening from JFK to SFO is one of those graced with in-flight Internet service, courtesy of GoGo Inflight Internet (and a nice revenue-sharing deal with American Airlines, no doubt).  It’s an even more pleasant surprise to report that the service is superb.  I just tested my bandwidth and came up with a surprising 2.4Mb downstream (haven’t done an upstream test, but I just emailed a 12Mb file to a colleague who got it reasonably quickly).

To put it mildly, I suspect these services will have an immediate and profound impact on my personal inflight productivity.  There are some silly For one thing, it will probably make “offline availability” a little less important for some of my company’s internal enterprise applications (NetSuite comes to mind, as does Sharepoint).  Moreover, I’m in the middle of two negotiations, and it’s refreshing not to go dark on them for five hours while I fly across the country.  Of course this service opens up all sorts of interesting etiquette questions.  For example, my seat-mate and I exchanged cards and “nice to meet you” emails while aloft (total “send button to new mail bing-bong on other computer” time of 15 seconds, including two Exchange servers and two VPNs—impressive).  Is the protocol to add each other to LinkedIn after the flight or during?  And what about VOIP calls?  (I’m pleased to report that GoGo says it’s blocking VOIP, a decision of which I’m heavily in favor.)

Of course, let’s not forget the side benefits.  I’m listening to my “Office” Pandora stream (zero hiccups, credit both Pandora‘s excellent Flash app and GoGo) and tracking my flight location:

My Flight Location: American 177 on 9/22, in real time

I’ll have to check the traffic back home before I land….

So, we’re back on the air.  Stay tuned….

(click for the full mosaic, 1,300+ books in all!)

Yes, that’s a geeky thing to do. But it highlighted a few things about my changing “digital existence” that I thought were worth reporting:

So Much Data
First and foremost, all of this data (the books, the covers, and even the photo I turned into the mosaic) were available with a few minutes worth of work. Admittedly, I had previously spent hours scanning the ISBN bar codes on my books (conveniently when packing my books in order to move to my new apartment). But think about the amount of data available to me for very little investment: the titles, authors, and graphic images of 1,300 some-odd books, along with their associated meta-data (length, ISBN, etc.). When I was in school (ending in the mid ’90s), gathering and manipulating this sort of data was certainly possible, but doing so was the domain of database experts, programmers, and the like. So I became one of those, mostly because I saw the computer as a tool which would facilitate information manipulation of a nature never previously possible−or indeed imagined.
Trumbull College, My residential college at Yale, for example, had a library boasting some 5,000 works. Its card catalog was positively ancient and poorly maintained. Estimates for the workload involved in cataloging it and keeping it up-to-date were so substantial that the (volunteer) project never got off the ground. A mere fifteen years later, my catalog is not only mostly up-to-date, but it contains all manner of “rich content” that a card catalog could not muster: images of the covers, other books by the same author, publication history, and of course the meta-data: reviews, social/popularity information, and even feedstock for inference and recommendation engines.

Community Creativity
Then there is the accessibility of the inspiration. LibraryThing cleverly suggested the mosaic and linked to David Louis Edelman‘s post in which he created a similar mosaic. Call it community scrapbooking, community arts and crafts, or simply community creativity, but this sort of cross-country “we all trade inspiration” is unusual, to say the least. To be sure, historically artist communes and even local arts and crafts fairs historically provided fodder and inspiration for our individual creativity, but this is a different kind of inspiration: it is both more instantaneous (I got the idea late last night; got a full night’s rest; and woke up and produced the mosaic before breakfast this morning) and more eclectic (David is a computer programmer and Science Fiction author in the Washington, DC area; I am a technology company CEO in Silicon Valley).

But Silos—Still
Unfortunately, it’s not all wine and roses. LibraryThing is the site I’ve always used to catalog my books, but recently Visual Bookshelf has won many converts, mostly because they have embraced the Facebook Platform API and have created a Facebook application. Since some 500 of my friends are on Facebook, and since many of them are avid readers, Visual Bookshelf has already netted me 40 some-odd “reading buddies” (which I define as other people I am friends with on Facebook and who have Visual Bookshelf profiles). An 8% cross-over rate isn’t bad, especially when you consider that Visual Bookshelf is only one of hundreds of Facebook applications. (And, for that matter, it’s one of the least annoying, since it doesn’t spam the hell out of your friends.) Here, for example, is my bookshelf, as displayed on Facebook, and what my friends are reading:

Unfortunately, I cannot synchronize my book activity on Visual Bookshelf with my LibraryThing account. Visual Bookshelf finally implemented a LibraryThing import feature, but it’s unidirectional. Likewise, Facebook makes it nearly impossible to export friend information (going so far as to display email addresses as images to foil screen scrapers and other brute force export tools). So I’m stuck maintaining two databases and importing one to the other, potentially over-writing or losing information each time I do so.

Of course, I’m not the only one who has noticed this problem, and it is but one example of the growing “problem” of social networking data living in proprietary silos. Such well-known Web 2.0 commentators as Om Malik have even gone so far as to propose that social networking features will end up getting built into most desktop and web software, much the same way as the Cut/Copy/Paste mechanism has become a de facto paradigm standard. But that will only work if the core social networking information (who is who and who knows whom) does not remain the proprietary information of, e.g., Facebook. Technologies from the simple XFN to the ambitious OpenSocial are supposed to fix that, but OpenSocial appears almost to have been promulgated by Google to compete with Facebook, and it will be a chilly day in the netherworld before Facebook adopts it. More recently, the DataPortability Working Group has been graced by the participation of Facebook, Google, Microsoft, and others (or at least representatives from those companies). But until something concrete develops, we early adopters will continue to enjoy the benefits of So Much Data and Community Creativity, but only if we’re willing to put up with duplicate data, lost data, and the other assorted horrors of manual synchronization.

All told, the information revolution continues in directions we never could have anticipated. Here I am trading notes with friends I haven’t physically seen in over a decade, enjoying better book recommendations from the wisdom of my friends (and the crowds) than I do by poking around my local bookstore, and finding a nice Sunday morning arts and crafts project inspired by a Washington, DC science fiction author whom I’ve never met.

Now if only I didn’t have to keep three copies of it all!

**GRINCH UPDATE 12/18: Some militant copyright holder has filed a DMCA Takedown request with YouTube, so this wonderful piece of (IMO) fair use parody was temporarily unavailable. Fortunately, Matt Hempey has created version 1.1, which went online this evening**

**GRINCH UPDATE 12/18: Some militant copyright holder has filed a DMCA Takedown request with YouTube, so this wonderful piece of (IMO) fair use parody was temporarily unavailable. Fortunately, Matt Hempey has created version 1.1, which went online this evening**

As I poked around YouTube to see what other bits of cynical commentary I could find, I found that the group which created this little gem, The Richter Scales, has been busy singing their unique sharp commentary for a while. Check out this equally funny piece (which hasn’t quite gone viral—34,000 views since September) about the sub-prime debt melt-down:

This particular one strikes a chord (sorry ) because the song on which it is based, “There’s a Fine, Fine Line (Between Love and a Waste of Time)” from Avenue Q, was written by my friend and college classmate, Bobby Lopez, who just might just be the youngest composer to win a Tony Award for best score, which he and collaborator Jeff Marx shared in 2004 for Avenue Q. The world is, indeed small.

And it gets smaller.

With a little more poking, I discovered that The Richter Scales are based right here in San Francisco. They’re quite the motley crew of former a cappella singers from various universities. Naturally, since my alma mater has something of an a cappella “problem” (15 singing groups on one campus will do that), I figured I might find a few, and, sure enough, there’s friend Nils Erdmann, a year ahead of me and a member of the elite Senior mens group the Yale Whiffenpoofs. A cappella singing was maybe the highlight of my undergraduate experience, and my group, Out of the Blue, is still going strong—some would say stronger, now that that my voice is but a distant memory!—and celebrating its 20th Anniversary this year. With some 200 or so alumni, there will be a run on hotel rooms in New Haven for the event….)

And now it comes full circle: A cappella singing seems to be somewhat addictive for those of us who indulged in it in college, and just the other night, an old friend from school asked me if I’d sing background on a project her band was working on. Little did I know the evidence would be caught on tape—complete with a street-side serenade and the resultant reaction from our Mission crowd:

And now for the clincher: The Richter Scales have quite the sense of humor (as witness their great “I Hate A Cappella”), but they’re even tougher on the Tech industry. Earlier this year, they were nominated for a CARA award (by CASA, the Contemporary A capella Society of America, who else?) for “I’ve Got Mail”.

Enjoy.**If the Grinch lets you.**

For various reasons, Facebook has become my social network of choice. In the gross network size sumo match, it wins handily (55 million users at last count), and Facebook keeps growing in my consciousness, so I got curious: Just who is using these sites? Is the demographic changing?

For starters, I was convinced I could predict the likelihood of a person being a Facebook member based on their age. Could I? You betcha! It took some digging, but after a couple of days of running searches and compiling statistics, I had solid data supporting the hypothesis. (See below for methodological notes.) Here, for example, are the gross number of Facebook members who voluntarily associated themselves with specific graduating classes at four top US universities:

So the number of Facebook members increases as college graduation year increases. Not terribly surprising, but I was somewhat surprised by the “spread” between Harvard and Princeton. According to this first cut, it appeared that Princeton graduates are less likely to adopt Facebook if they are older, whereas Harvard graduates are somewhat more likely to do so. Once I corrected for the size of the undergraduate populations of the respective schools, the spread shrank significantly:

There is still a difference between the universities, but the trend is considerably tighter. (For those wondering why the lines stop earlier for Harvard than for Princeton, and why they don’t reach as high, see the methodological notes.) Those who like to look closely at charts will notice that the trend is really surprisingly monotonic: there just aren’t that many later years which have lower membership than earlier years. Taking the average makes the trend even more obvious:

Now it gets interesting. These data pretty much prove that Facebook usership is seeing exponential demographic growth: the exponential regression shown above (dotted white line) is by far the most accurate regression for these data (r²>0.99; nothing else comes close).

As an aside, note that members counted in the statistics above graduated college before Facebook, or any of the social networking sites with any real following, launched. This is particularly interesting because it points to some further areas of research: what is causing this adoption? Is this a “sideways” look at Metcalfe’s Law in action?

So age does matter, if you want to find someone on Facebook!

Next question: what can we make of the differences between adoption rates at different universities? It turns out there are some trends there as well. For starters, I broadened the focus beyond the admittedly minuscule sample of the four top US universities above. Arbitrarily, I chose to expand the list to include a few state universities of much larger size, as well as another top private university (MIT). Here are the total number of members of Facebook who have voluntarily associated themselves with their university networks, along with the respective universities’ undergraduate and total student populations, for comparison:

This chart tells us two important things: one, on a gross numbers basis, graduates and students at the University of Michigan would seem to be experiencing the greatest network effect value on Facebook, followed by Harvard and UCLA. This isn’t altogether surprising, however, given the relative sizes of the student populations at Michigan and UCLA. But correct (normalize) these data for size of student population, and an entirely different trend jumps out:

What do these charts say? If you correct for size of undergraduate population, Stanford University students and graduates are far more likely to be members of Facebook than are the students of these few other universities (59.3% more likely than nearest “competitor” Princeton, and 9.5 times more likely than UCLA students). Perhaps this is to be expected from a university in the heart of Silicon Valley, a mere few minutes’ drive from the venture capitalists who fund so much of the social networking industry. But the trend is even more interesting when one corrects for the overall student body size: then Princeton University is the hands-down winner, outstripping nearest competitor Stanford by 42.2% and besting UCLA by 2.7 times.

At first blush, the answers to this second question would seem to fly in the face of the first: how can Princeton students and graduates be so much more likely to be users of Facebook while still lagging behind the other schools in “older” graduate adoption? As you have no doubt surmised, the difference lies in the activity of the current student population. What Princeton loses in “older” graduate adoption of social networking technology it more than makes up in adoption by its current student body. Or to put it another way, if you meet someone on Facebook, and she is from Princeton, she’s likely to be younger than that fellow you met from Harvard!

Methodological Notes:

All of the data in this posting were collected either by using Facebook’s Friend Finder or reviewing the “Network” home pages for each of the various universities. Due to result size limits imposed by Facebook, presumably for performance reasons, queries by class year and university which return more than 500 listings report “of over 500 found.” By paging through the results, it is possible to get beyond 500 and reliably spot results up to about 550 or so hits, but in no case will Facebook display the 551st hit. Therefore, in all the datasets, any value reported above 500 was treated as an “overflow” and was not calculated into averages.

There are several potential problems with the above techniques used to quantify class membership. First, not all members of an undergraduate class are of the same age. Second, Facebook does not reliably distinguish between undergraduate and graduate class membership when searching (it does, to some extent, when reviewing an individual’s profile). Third, Facebook does not distinguish between students, faculty and university staff when calculating the size of its Networks. Especially for the larger universities—with larger staffs—there is likely to be some impact from this particular source of error. Finally, affiliation with class years and universities is strictly optional, so there is undoubtedly selection bias present in these figures. Of particular note is that there are undoubtedly some people who affiliate with a University network but intentionally choose not to divulge their graduation year.

Those of us who live near the Hayward Fault, which runs to the East of Silicon Valley, have known for a while that we are “overdue” for a quake (with apologies to the Wizard of Odds—yes, of Odds—for flirting with the Gambler’s Fallacy). [Aside: anyone remember that great '80s Bond film with the man-made Silicon Valley earthquake?]

When the quake hit, I was having dinner with two colleagues from work, one of whom had been around during the much more serious Loma Prieta quake in 1989. That one measured about 7.1 on the Richter Magnitude Scale. Most people know that the Richter scale is a logarithmic scale, but many don’t realize that the scale actually provides two different output values: the physical displacement at the fault location goes up by a factor of 10 (i.e., the log is to the base 10) for each single unit of increase, but the energy released goes up by a factor of 32 for each single unit increase. Thus the ’89 Loma Prieta quake was approximately 181 times more energetic than today’s quake.

A truly astonishing amount of information was immediately available about today’s quake, thanks to automated processing and reporting of seismic events. For example, here’s a map of all the recent earthquakes in California from the USGS. (The large blue rectangle is, of course, the quake in question. Note the smaller aftershocks.)

The detailed report on the quake confirms that the sophistication of automated earthquake analysis is truly impressive. Want to know whether there’s a tsunami risk? Read the Tsunami Message from WCATWC. (No risk.) Or how many people in your neighborhood felt it? Read this map. (84 in Mountain View.) Or what the ground looks like above the epicenter? Check this out:

This is all particularly interesting to my colleagues and to me because the mathematics used to locate earthquakes (by seismic triangulation) are pretty much identical to the mathematics use at ShotSpotter to locate gunfire (by acoustic triangulation). Both techniques are fundamentally based on the difference in time of arrival of a transient signal (the seismic or acoustic wave) at sensors located in different places. Based on this difference in time of arrival (also known as TDOA, or time difference of arrival), a series of hyperbolae can be plotted, and the intersection of these hyperbolae will be the origin of the transient. Why hyperbolae? Hyperbolae are “curves of constant difference in distance” between two points, or foci. Wolfram has an excellent article explaining them. So, geometrically, if you have two different points F₁ and F₂, then there is a hyperbola is the set of all points whose distance from F₁ and F₂ always differs by a specific constant, which we can call k. In the diagram below, for example, the difference between the distances r1 and r2 will equal k, as will the difference between the distances r3 and r4.

The physical interpretation of this k is straight forward: it is the difference (in time) between when someone standing at F₁ and someone standing at F₂ would hear a noise which originated somewhere on the hyperbola.

If you have only two points (F₁ and F₂ ), then you have a single hyperbola. But if you have three points (F₁, F₂ and F₃ ), then of course you have three hyperbolae, reflecting the difference in time of arrival at the three points (F₁/F₂ , F₂/F₃ , and F₁/F₃ ). This diagram, from Suruj Dutta’s site explaining the technical underpinnings of location-based services, shows how cell phone triangulation works and gets it mostly right, although it only shows two of the three hyperbolae:

Moreover, the reflections and “echoes” caused by different geologic layers of the earth are quite similar to the reflections and echoes caused by the complex urban terrain in which ShotSpotter systems are deployed.

I finally lost patience with the limitations of Blogger and switched to WordPress. Along with the switch came a complete visual redesign, as well as the requisite re-posting of my prior posts and some ancillary dusting and clean-up. For those who don’t know it, WordPress is a superb blogging engine, built on PHP and MySQL (and often hosted on a web server running Apache). It’s a testament to the maturity of the tools today that I was able to get WordPress running on my Windows XP laptop as a test environment (which meant running PHP and MySQL, but using Microsoft’s IIS as the web server) in about two days (minor MySQL learning curve for me, since it was my first experience), and then that I was able to get the whole stack up and running on my hosting provider, BlueHost, in about half a day.

Building themes and functionality beyond the “out of the box” stuff in WordPress requires a working knowledge of CSS, XHTML and some PHP. I had the first two, but this was my first real exposure to PHP. So, off to the bookstore I went, and came home with O’Reilly’s Learning PHP 5. I read it, as I usually do with computer books, in one evening

Both novels come from unlikely sources: Michael Crichton, the well-known popular novelist of borderline science fiction thrillers and creator of the long-running ER, and Richard A. Clarke, the now-ostracized Clinton and Bush Administration cyberterrorism expert. Crichton’s Next is actually the second of his technology-gone-wild novels, the immediately prior one being Prey, which failed for all the reasons Next doesn’t: where Prey was really a “shoot ‘em up” movie stuffed into a novel whose plot centered around a company developing autonomous nanotechnology “bots,” Next is a collage of multiple plots–almost devoid of action–gleefully intertwining one bio-engineered protagonist with another. Next holds together better than one might expect it would without Crichton’s signature action and adventure: for all the action sequences in Jurassic Park and Sphere, Crichton is a thoughtful and well educated man (M.D. from Harvard), and Next presents the dangers of human-created interspecies gene mixes (for example, pets which have glow-in-the-dark genes from fireflies inserted in their DNA; parrots who are smarter than most five-year-old humans) clearly and convincingly.

By contrast, Richard Clarke’s Breakpoint is a narrowly-focused thriller in the classic “secret government agency v. the world” genre. Unlike Crichton, Clarke is a bit clunky when he describes his characters: he just doesn’t have Crichton’s twenty years of practice developing a light and almost comic descriptive touch. But he does a superb job of painting our countries vulnerabilities to cyber attack. The book opens with simultaneous attacks on seven of the eleven Internet fiber-optic beachheads on the US East Coast. Not one of them is protected by anything other than a fence. And in today’s just-in-time, zero inventory world, nobody has the inventory to replace the millions of dollars in equipment hidden in these utterly unguarded brick beach shacks scattered along the beaches of remote areas. Or consider the millions of SCADA sensors placed throughout the power grid system, most of which haven’t seen security upgrades in 15 years. These are just the beginnings of our vulnerabilities.

Both books are engaging, distracting, and ultimately frightening. Their intent is to wake us up to a stark reality: in the next fifteen years, bioengineering and network software will both mature as rapidly as computer technology has over the past twenty years. We’re simply not ready for the implications of those evolutions. The RIAA is still fighting its late ’90s battle with Napster–and artists like MC Lars are poking fun at them for still being so far behind. Our society simply isn’t ready for the onslaught of ethical and environmental dilemmas. We’re still focused on the battles of the past (still debating the ethics of abortion while GM crops are planted without a second thought).

Although I seriously doubt that either Crichton or Clarke will ascend to the literary pantheon the way Orwell did after 1984 (he died a year later), these two modern novelists are no less insightful. One would expect this from a man like Clarke, who spent enough years in the White House to know that fiction often carries the day over fact, but it is perhaps a bit surprising coming from Crichton, who is certainly smart, but otherwise doesn’t have a reputation as a serious thinker. Regardless of their credentials, both Crichton’s and Clarke’s books raise issues we have to confront before the technology progresses beyond our ability to cope with its ramifications.