An article in the current edition of the Economist describes Alfred Marshall’s original observation of geographic clusters of activities. They describe four main logics for clustering:
First, some may depend on natural resources, such as a coalfield or a harbour. Second, a concentration of firms creates a pool of specialised labour that benefits both workers and employers: the former are likely to find jobs and the latter are likely to find staff. Third, subsidiary trades spring up to supply specialised inputs. Fourth, ideas spill over from one firm to the next, as Marshall observed.
However, there are also costs to being in a cluster such as higher rent or transportation costs associated with distances to customers or suppliers. The burst of communications and computing power should make it easier since natural resources are less important and workers can live farther away from their offices.
It hasn’t worked this way. Pools of human capital continue to drive clustering as people prefer to work near where they live. Very small distances can make a big difference. The article goes on to describe clusters within clusters in the Bay area for specialized knowledge.
The patent system is “a real chaos”. Its faults were laid bare yesterday in an extensive New York Times article, which quickly reached the “most emailed list” (The Patent, Used as a Sword; and see Melissa Schilling’s review). But the same article also hedged by reminding us “patents are vitally important to protecting intellectual property”. But is intellectual property really essential for innovation? For an answer, look just a little past commercial software and you will see vast open collaboration without patents or copyright. Wikipedia, an open initiative, answers many of our questions. Open source software such as Linux and Android power most commercial websites and mobile devices, respectively. In myriad forums, mailing lists and online communities, users contribute reviews, provide solutions, and share tips with others. Science has been progressing by enlisting thousands of volunteers to classify celestial objects and decipher planetary images. Innovation without patents is real. Researchers estimate that open collaboration and user innovation bring more innovation than than the patented kind. Our legal and commercial system can do more to encourage it.
A terrific paper by Cormac Herley, Microsoft Research, came out entitled, “Why do Nigerian Scammers Say There are from Nigeria.” It turns out that 51% of scam emails mention Nigeria as the source of funds. Given that “Nigerian scammer” now make it regularly into joke punch-lines, why in the world would scammer continue to identify themselves in this way? The paper was mentioned in a news item here, if you want the executive summary version but, really, I can’t imagine readers of this blog not finding the actual paper worthwhile and fun (it contains a terrific little model of scamming).
In a nutshell, the number of people who are gullible enough to fall for an online scam is tiny compared to the population that has to be sampled. This creates a huge false positive problem, that is, people who respond in some way and, hence, require an expenditure of scammer resources but who ultimately do not follow follow through on being duped.
As the author explains, in these situations, false positives (people identified as viable marks but who do not ultimately fall for the scam) must be balanced against false negatives (people who would fall for the scam but who are not targeted by the scammer). Since targeting is essentailly costless, the main concern of scammers is the false positive: someone who responds to an initial email with replies, phone calls, etc. – that require scammer resources to field – but who eventually fails to take the bait. Apparently, it does not take too many false positives before the scam becomes unprofitable. What makes this problem a serious issue is that the size of the vulnerable population relative to the population that is sampled (i.e., with an initial email) is minuscule.
Scammer solution? Give every possible hint – including self-identifying yourself as being from Nigeria – that you are a stereotypical scammer without actually saying so. Anyone replying to such an offer must be incredibly naive and uninformed (to say the least). False positives under this strategy drop considerably!
UPDATE: Josh Gans was blogging about this last week over at Digitopoly. He’s not convinced of the explanation though. To the extent there are “vigilante” types who are willing to expend resources to mess with scammers, the Easy-ID strategy could incur additional costs. As an interesting side note, in discussing this with Josh, he at one point suggested the idea that when legit firms come across scammers, they should counterattack by flooding them with, e.g., millions of fake/worthless credit card numbers (setting of something like a false positive atom bomb). Just one snag: US laws protect scammers from these kinds of malicious attacks.
Freek’s latest post on confirmation bias notes that intellectual commitments can bias which research findings one believes. The tone of the post is that we would all be better off if such biases didn’t exist, but there is definitely a tradeoff here. Greater objectivity tends to go with lower intensity of interest in a subject. (Disinterested and uninterested are correlated, for those old-timers who remember when those words had different definitions.) That’s why you often find that those with strong views on controversial topics–including those with minority or even widely ridiculed opinions–often know more about the topic, the evidence, and the arguments pro and con than “objective” people who can’t be bothered to dig into the matter. Other than partisanship, the only thing that will get people interested enough to seriously assess competing claims is a personal stake in the truth of the matter. (And in all cases, Feynman’s admonition that the easiest person to fool is yourself should be borne in mind.)
Historians of science of all stripes, from romanticists like Paul de Kruif (author of the classic The Microbe Hunters) to sophisticated evolutionists like David Hull in Science as a Process, have reported that intellectual partisanship motivates a great deal of path-breaking research. “I’ll show him!” has spawned a lot of clever experiments. Burning curiosity and bland objectivity are hard to combine.
But how can such partisanship ever lead to intellectual progress? Partisans have committed to high-profile public bets on one or another side of a controversy; their long-term career and immediate emotional payoffs depend not directly on the truth, but on whether or not they “win” in the court of relevant opinion. The key to having science advance is for qualified non-partisan spectators of these disputes be able to act as independent judges to sort out which ideas are better.
Ideally, these adjacent skilled observers would have some skin in the game by virtue of having to bet their own research programs on what they think the truth is. If they choose to believe the wrong side of a dispute, their future research will fail, to their own detriment. That’s the critical form of incentive compatibility for making scientific judgments objective, well-described in Michael Polanyi’s “Republic of Science” article. If, for most observers, decisions about what to believe are closely connected to their own future productivity and scientific reputation, then the partisanship of theory advocates is mostly a positive, motivating exhaustive search for the strengths and weaknesses of the various competing theories. Self-interested observers will sort out the disputes as best they can, properly internalizing the social gains from propounding the truth.
The problem for this system comes when 1) the only scientific interest in a dispute lies among the partisans themselves, or 2) observers’ control over money, public policy, or status flows directly from choosing to believe one side or another regardless of the truth of their findings. Then, if a false consensus forms the only way for it come unstuck is for new researchers to benefit purely from the novelty of their revisionist findings–i.e., enough boredom and disquiet with the consensus sets in that some people are willing to entertain new ideas.
An earlier post described the sclerotic impact of excessive regulatory documentation requirements on real-estate development projects. it turns out that the private sector isn’t the only victim of this tendency:
- The Pentagon got concerned that it might be suffering from hyper-cephalization–too many studies and reports on every topic.
- The Pentagon commissioned a meta-study to estimate the costs of all the studies and reports.
- The Government Accounting Office performed a meta-meta-study saying that the meta-study wasn’t performed correctly according to existing rules and standards.
I think we all know what the logical response to the GAO meta-meta-study is…
The current issue of McKinsey Quarterly features an interesting article on firms crowd-sourcing strategy formulation. This is another way that technology may shake up the strategy field (See also Mike’s discussion of the MBA bubble). The article describes examples in a variety of companies. Some, like Wikimedia and Redhat aren’t much of a surprise given their open innovation focus. However, we should probably take notice when more traditional companies (like 3M, HCL Technologies, and Rite-Solutions) use social media in this way. For example, Rite-Solutions, a software provider for the US Navy, defense contractors and fire departments, created an internal market for strategic initiatives:
Would-be entrepreneurs at Rite-Solutions can launch “IPOs” by preparing an Expect-Us (rather than a prospectus)—a document that outlines the value creation potential of the new idea … Each new stock debuts at $10, and every employee gets $10,000 in play money to invest in the virtual idea market and thereby establish a personal intellectual portfolio Read the rest of this entry »
The “dynamic capabilities” literature, I think, is a bit of a mess: lots of jargon, conflicting arguments (and levels of analysis) and little agreement even on a basic definition. I don’t really like to get involved in definitional debates, though I think the idea of a capability, the ability to do/accomplish something (whether individual or collective), is fundamental for strategy scholars.
Last weekend I was involved in a “microfoundations of strategy” panel (with Jay Barney and Kathy Eisenhardt). One of the questions that I raised, and find quite intriguing, is the question of how we might “grow” a capability. The intuition for “growing” something, as a form of explanation, comes from simulation and agent-based modeling. For example, Epstein has argued, “if you didn’t grow it, you didn’t explain it” (here’s the reference). I like that intuition. As I work with colleagues in engineering and computer science, this “growth” mentality seems to implicitly be there. Things are not taken for granted, but explained by “growing” them. Capabilities aren’t just the result of “history” or “experience” (a common explanation in strategy), but rather that history and experience needs to be unpacked and understood more specifically. What were the choices that led to this history? Who are the central actors? What are the incentives and forms of governance? Etc.
So, if we were to “grow” a capability, I think there are some very basic ingredients. First, I think understanding the nature, capability and choices of the individuals involved is important. Second, the nature of the interactions and aggregation matters. The interaction of individuals and actors can lead to emergent, non-linear and collective outcomes. Third, I think the structural and design-related choices (e.g., markets versus hierarchy) and factors are important in the emergence (or not) of capabilities. Those are a few of the “ingredients.”
I’m not sure that the “how do you grow a capability”-intuition is helpful in all situations. However, I do find that there is a tendency to use short-hand code words (routines, history, experience), and the growth notion requires us to open up these black boxes and to more carefully investigate the constituent parts, mechanisms and interactions that lead to the development or “growth” of capability.