Author: Michael Nielsen

Biweekly links for 03/21/2008

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Attentional philanthropy

One of the pleasures of SciBarCamp was several fun conversations with Mark Tovey. Mark is the editor for Worldchanging Canada. I asked Mark what Worldchanging does, and he explained it by saying that they are in the business of attentional philanthropy.

Attentional philanthrophy is very cool. What it means is that Worldchanging has built a (very big) audience at their website, and they then donate that audience’s attention to good causes; the trick in the writing is ensuring that readers find those causes interesting. I’m reminded of the avalanche of attention kiva.org (check them out!) got when Bill Clinton mentioned them on Oprah; with that one mention, Clinton and Oprah redirected millions of dollars to kiva.org. I’m obviously not in the league of Clinton-Oprah (or Worldchanging), but attentional philanthropy is something I plan to practice more consciously whenever I have the opportunity.

Biweekly links for 03/17/2008

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SciBarCamp: opening night

The SciBarCamp opening night was a place of awesome creative chaos. We decided today’s program, which starts off with sessions organized by Corie Lok, on Science 2.0, then Daniel Gottesman (“Quantum mechanics for ten year olds”), Jim Thomas and Andrew Hessel (“Synthetic Biology”), “Eva Amsen (“10 Things Everyone Should Know About Science”), plus loads more – the Saturday program is here. Should be fantastic.

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Biweekly links for 03/14/2008

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Investing in undervalued human capital: the Y Combinator model

Y Combinator (YC) is a small Cambridge-based firm that for the past few years has been carrying out a remarkable experiment. What they’ve been doing is investing money and training in (mostly) young hackers, helping them get technology companies up and running to the point where more more conventional investment processes like venture capital can kick in. Many YC funded companies have been successful, with several making their founders wealthy at an early age.

At first glance, YC may appear only of interest to business or technology people. In fact, there are broader things one may learn from the model, with applications and importance outside business and technology.

If you’re not familiar with how YC works, it goes something like this. Twice a year, YC calls for applications to be submitted, either for its Winter or its Summer programs. Applications are submitted by small teams of people (“founders”), typically in their twenties, who would like to start or have recently started a technology company. YC evaluates the applications, and the best are asked to join the YC program. Successful applicants typically receive $5k + $5k per founder to support them for three months, and are required to move to Boston (for the Summer program), or the San Francisco Bay Area (for the Winter program). All the YC teams meet together once or twice a week, to talk with each other and with the YC partners, as well as with a changing cast of expert entrepeneurs specially brought in from outside. The three month program concludes with “Demo Day”, where the founders demonstrate what they’ve built to a large group of angel investors and venture capitalists, in the hopes of sparking further interest. In return for this program, the founders give up a small percentage of their company, typically between 2 and 10 percent.

What makes the YC program successful is that YC have identified a large group of people whose talents were previously undervalued and underutilized, in large part because of their age and lack of experience. For more than thirty years, high-school geeks have played with technology, gone off to university, where they continue to play with technology, often doing astounding and innovative things, but rarely having the entrepeneurial skills or connections to turn their ideas into marketable products. At the end of it all, they go off to work for a big established technology company like Microsoft.

YC has asked a big “what if?” question: what if we gave these talented people an opportunity to build their own company, from the ground up, and gave them training in entrepeneurial skills they lack, complementary to their existing technical ability? Might it be that if we provide this training (which is relatively easy to do), then these people will create more value than if they were off working for big existing technology companies?

It is evident from the above description that this process can be abstracted away to a core unrelated to technology:

  • Identify a talented group of people who are at present undervalued, i.e., not being given an opportunity to contribute commensurate with their talents.
  • Set up a competitive program whereby people in your target group can apply for support.
  • Select the best applicants for support.
  • Help educate successful applicants, trading off the costs of the education against the value that comes from their increased probability of success.
  • Make sure you market yourself to the desired group of people, so you get the best possible pool of candidates (e.g., here and here).

What’s special about YC is the group they’ve identified: young hackers, whose lack of experience means they often have a hard time being considered seriously by existing investors such as venture capitalists. Ironically, this is in part because the venture fund model typically involves investments that are, at a minimum, hundreds of thousands of dollars. Given a choice between investing that money in a 35-year old Harvard MBA’s startup, and a team of three unshaven 21 year-old hackers, most venture partners will go for the Harvard MBA. Part of YC’s insight is that in 2008 many technology companies can be launched for just a few tens of thousands of dollars, far less than the venture funds provide.

Other organizations have adopted an analogous strategy to YC, but for a different group of otherwise undervalued people. A good example is microfinance organizations like the Grameen Bank, which provide small loans to assist entrepeneurs in the developing world. The success of the Grameen Bank indicates that investors previously underestimated the talents of the lendees to build successful businesses. An interesting social consequence common to YC and the Grameen Bank is that both empower people who are otherwise somewhat disenfranchised. (Obviously, the effect is far greater in the case of the Grameen Bank.)

This process of identifiying a talented group of people who are undervalued by the investment market is a curious one. An uncritical advocate of the free market might counter that such people shouldn’t exist – surely investors would have already tracked them down, and offered to invest. In fact, YC is a clear case where (up to now) the market has failed badly, due to the blinkered narrowness of investors. Is it more risky to offer one million dollars to finance a Harvard MBA in their new technology venture, or to fund twenty groups of talented twenty-one year old hackers, at a cost of about $50k each (including the training costs and other overheads)? My money would be on the twenty-one year olds to make a greater aggregate return, but I suspect most investors would feel much safer going with the Harvard MBA – even if they fail, it’s a lot easier to defend your choice to your peers.

What other undervalued sources of human capital might this general model be applied to? When I started to think about this question, I quickly came up with dozens of possible groups. Here’s the first few that came to mind:

  • Talented people who happen to have been born in the wrong place for their talents to flourish. The top students at (for example) the big IITs in India are incredibly talented. While India offers increasing opportunities for technology entrepeneurs, imagine the results of bringing some of the more entrepeneurial students to Silicon Valley, and helping them get set up with technology companies. Think YC with a visa program.
  • The elderly. As a society, we cut many extremely talented and active people off from contributing society, at great cost to them, and to society as a whole. It’d be great to find ways their talents could be made better use of.
  • Bright PhD students in insanely competitive and challenging academic subjects, where even extraordinary students may have trouble getting good academic jobs, and where those same students may lack the connections to find jobs outside academia that make good use of their talents.
  • My current hometown of Waterloo, Canada, is a pretty good setting for a YC style program. It has a growing startup culture, and two universities (University of Waterloo and Wilfred Laurier University) with, respectively, strong technology and business programs. As a rough indicator of student quality, in programming and mathematics competitions, University of Waterloo students are routinely competitive with the best from MIT and other top US Universities. At present, many of these students go to work for large technology companies elsewhere – the University of Waterloo is sometimes said to be Microsoft’s single largest recruiting target. Something like YC would, I think, be highly successful here, although it would need to compensate for a relative paucity of local investors.
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Creative collaboration: ideals and reality

I’ve been reading Keith Sawyer’s book “Group Genius: The Creative Power of Collaboration”, and thinking about what makes some collaborations work, and others fail.

In this post I describe two principles governing group collaboration. Both principles are obvious and self-evident. Unfortunately, and this is the point of the post, they’re often systematically disobeyed in scientific collaborations, and this may prevent such collaborations from achieving what Sawyer calls “group flow”, a state in which groups collaborate effectively, producing creative works beyond any of the individual members of the group.

Principle: Collaboration should recognize individual effort appropriately

In a jazz performance, it is for the most part transparent who is contributing what to the performance. If someone is slacking off, or trying to hog the limelight, this becomes obvious to the audience.

Science is much less transparent. There are no generally agreed upon norms governing how people are given credit in a paper, and as a result individuals in a group may not feel secure that their role will be properly acknolwedged. To be sure, in some fields there are rules of thumb – for example, in many experimental papers, the principal investigator who runs the lab in which the experiments were performed is often listed as the last author on the paper. But this is a long way short of a full and fair accounting of who contributed what.

This lack of transparency causes all sorts of problems. A common example is the “author” who was in the room when some critical breakthrough happened, but who actually contributed little, and lacks the grace to refuse authorship. Another common example is the author who contributes just enough to deserve authorship, and then goes on their merry way, leaving the bulk of the work to be done by others. Many multi-author papers are primarily the work of a single individual, yet that individual may not be distinguished at all in a long list of 5-10 (or even more) authors.

Some scientific journals, such as Nature, are beginning to address this problem, experimenting with systems whereby each author on a paper is asked to detail what they contributed to the paper. It will be interesting to see whether this creates more incentive for people to contribute in a full and fair fashion to papers on which they are authors.

Principle: collaboration should involve people with complementary skills

This is so obvious that it would seem to fall into the “well, duh!” category. In fact, institutions often systematically violate this principle on such a large scale that it becomes an accepted and almost invisible part of the institutional culture.

Exhibit A is Australian science. I’m picking on Australian science here because I know it well – similar remarks hold true in many other countries. A peculiar feature of the funding system for nearly all Australian Universities is that departments are financially rewarded for keeping their own students within a department. As a result, it’s not uncommon to go into a large research group, and discover (say) 5 PhD students, virtual academic clones of one another, having graduated from the same academic department, often within a year or two of each other, and often with essentially the same list of undergraduate courses. Not a good recipe for reaping the benefits of complementary expertise! The contrast with top American research departments is striking, with students even within a given research group often having quite heterogeneous backgrounds.

Exhibit B is the disciplinary structure of science itself. Most disciplines and subdisciplines have a canon of material, which experts are expected to understand. Unfortunately, in most fields learning the canon requires an enormous amount of time, which leaves little room for learning more individualized skills. It’s interesting to recall that the physicist Richard Feynman famously claimed not to understand either group theory or the standard integration techniques from complex analysis, two skills that are certainly canonical for particle physicists. Perhaps he spent his time learning a more individualized set of skills that made him better able to contribute in a unique way to the collaborative enterprise of science.

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New blogs

Sean Caroll and Chad Orzel both have posts up asking readers to suggest new blogs in comments.

Let me ask the same, but with an added twist: what are your favourite specialty blogs? What do you think is a really great and insightful read, but maybe in some unusual area? Know of a great blog on knitting (or Lego, or kite-flying, or marathoning, or publishing, or whatever)? Please leave it in the comments.

One thing I really enjoy about blogs is that I get to read stuff from experts on pretty much any subject. I love reading about economics (e.g., John Quiggin, Brad DeLong and Greg Mankiw), marketing (e.g., Seth Godin, HorsePigCow), writing (Confident Writing), libraries (John Dupuis, Science Library Pad, Google Librarian blog), machine learning (Machine Learning), and lots and lots of other subjects. Pretty much every subject in the world can be fascinating, providing you’re talking to the right person!

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Deference, defiance, and power

Robin Hanson has some thoughts related to my recent post about the consequences of the bias towards power:

People relate to power two ways, via deference and defiance. When we defer to power, we are indeed biased to give it too much inferential weight, but when we defy power, we give it too little inferential weight. We listen too much to the powers that we feel allied with, and too little to powers we feel allied against. To think more objectively, become less allied.

Robin’s comments implicitly highlight the fact that we may be for or against power, but we rarely ignore it. Of course, being ignored is all-too-often the fate of ideas, even very good ideas, expressed by the less powerful.

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