After all this hardware was installed, an even larger problem was tuning the AGS. In 1988, when we accelerated polarized protons to 22 GeV, we needed 7 weeks of exclusive use of the AGS; this was difficult and expensive. Once a week, Nicholas Samios, Brookhaven’s Director, would visit the AGS Control Room to politely ask how long the tuning wouldcontinue and to note that it was costing $1 Million a week. Moreover, it was soon clear that, except for Larry Ratner (then at Brookhaven) and me, no one could tune through these 45 resonances; thus, for some weeks, Larry and I worked 12-hourshifts 7-days each week. After 5 weeks Larry collapsed. While I was younger than Larry, I thought it unwise to try to work 24-hour shifts every day. Thus, I asked our Postdoc, Thomas Roser, who until then had worked mostly on polarized targets and scattering experiments, if he wanted to learn accelerator physics in a hands-on way for 12 hours every day. Apparently, he learned well, and now leads Brookhaven’s Collider-Accelerator Division.
We love small businesses. We love entrepreneurs. Do we love them too much? The Economist thinks that this may be the case, reminding us that our liking may have more to do with ideology (or self-adulation) than with economic reality.
Small is not Beautiful: Why small firms are less wonderful than you think
I’m seeing more and more work using Mechanical Turk as a subject pool. Here’s another piece discussing some of the features, advantages and problems with Mechanical Turk – Rand, D (2011), The promise of mechanical turk: how online labor markets can help theorists run behavioral experiments, Journal of Theoretical Biology.
Combining evolutionary models with behavioral experiments can generate powerful insights into the evolution of human behavior. The emergence of online labor markets such as Amazon Mechanical Turk (AMT) allows theorists to conduct behavioral experiments very quickly and cheaply. The process occurs entirely over the computer, and the experience is quite similar to performing a set of computer simulations. Thus AMT opens the world of experimentation to evolutionary theorists. In this paper, I review previous work combining theory and experiments, and I introduce online labor markets as a tool for behavioral experimentation. I review numerous replication studies indicating that AMT data is reliable. I also present two new experiments on the reliability of self-reported demographics. In the first, I use IP address logging to verify AMT subjects’ self-reported country of residence, and find that 97% of responses are accurate. In the second, I compare the consistency of a range of demographic variables reported by the same subjects across two different studies, and find between 81% and 98% agreement, depending on the variable. Finally, I discuss limitations of AMT and point out potential pitfalls. I hope this paper will encourage evolutionary modelers to enter the world of experimentation, and help to strengthen the bond between theoretical and empirical analyses of the evolution of human behavior.
The title of this post is from the opening line of this article: McGowan, D. 2011. The Tory Anarchism of F/OSS Licensing. University of Chicago Law Review.
The article goes against current academic wisdom (Lessig et al) and argues that freedom actually gets restricted in open source licensing — specifically the freedom of authors (rather than users). An interesting piece, worth reading. Here’s the abstract:
This Article uses the example of free and open-source software licenses to show that granting authors relatively strong control over the modification of their work can increase rather than impede both the creation of future work and the variety of that work. Such licenses show that form agreements that enable authors to condition use of their work on the terms that matter most to them may give authors the incentive and assurance they need to produce work and make it available to others. Such licenses may therefore increase both the amount of expression available for use and the variety of that expression, even if enforcement limits the freedom of downstream users. These facts give reason to oppose recent decisions that make license terms harder to enforce through preliminary or permanent injunctive relief.
Grant McCracken summarizes an Economist post that argues that big companies are better at innovation than small ones (well, he discusses both sides).
But theory says that small companies are actually the winners.
Economists have long wrestled with this, the “diseconomies,” problem: why do smaller organizations outperform large ones? (Todd Zenger’s 1992 Management Sci piece summarizes this work nicely.) Schumpeter indeed went both ways on this (Dick Langlois discusses the “two Schumpeters”-thesis a bit here). But yes, large organizations seemingly have the resources, complementary assets, access to talent etc to outperform small organizations. But small organizations still outperform large ones.
Large organizations have lots of problems (I’ll spare the references, for now). They
- mis-specify incentives,
- suffer from problems of social loafing (free-rider problem),
- engage in unnecessary intervention, etc.
And, if large organizations had such an advantage, why not take this argument to the extreme and simply organize everything under one large firm? That, of course, was one of Coase’s central questions. Obviously the organization-market boundary matters and there are costs associated with hierarchy.
Sure – there are lots of contingencies, caveats and exceptions [insert example from Apple or 3M]. And, definitions matter [what exactly is “small” versus “large”]. But on the whole, the theory says small companies win in the innovation game.
It’s the end of the year and many profs are writing letters of recommendation. Here are a few links. First, know that there are differing codes (also some discussion at orgtheory on this). And, biases (e.g., linked to the attractiveness of the student) may play a role in receiving a positive recommendation (but don’t worry, being attractive isn’t always a good thing – sometimes it’s a disadvantage). In short, the signal from letters of recommendation is hard to read. Here’s a piece on the Big 5 personality characteristics and letters of recommendation. Here’s a paper that says letters of recommendation are helpful for medical school admission. This paper says no. And, no, I haven’t read all of the above papers (they were published in journals of varying quality) – I just quickly searched google scholar for various papers related to letters of recommendation.
And an off-topic, end-of-year bonus tip: if you’re already behind on grading student papers – the ol’ staircase method can quickly fix things.