Economists have long understood the corollary concept of Coase's ceiling, a point above which organizations collapse under their own weight -- where hiring someone, however competent, means more work for everyone else than the new hire contributes. Software projects often bump their heads against Coase's ceiling: recall Frederick P. Brooks Jr.'s seminal study, The Mythical Man-Month (Addison-Wesley, 1975), which showed how adding another person onto a project can slow progress and increase errors.

What's new is something consultant and social technologist Clay Shirky calls "Coase's Floor," below which we find projects and activities that aren't worth their organizational costs -- things so esoteric, so frivolous, so nonsensical, or just so thoroughly unimportant that no organization, large or small, would ever bother with them. Things that you shake your head at when you see them and think, "That's ridiculous."

Sounds a lot like the Internet, doesn't it? And that's precisely Shirky's point. His new book, Here Comes Everybody: The Power of Organizing Without Organizations, explores a world where organizational costs are close to zero and where ad hoc, loosely connected groups of unpaid amateurs can create an encyclopedia larger than the Britannica and a computer operating system to challenge Microsoft's.

Shirky teaches at New York University's Interactive Telecommunications Program, but this is no academic book. Sacrificing rigor for readability, Here Comes Everybody is an entertaining as well as informative romp through some of the Internet's signal moments -- the Howard Dean phenomenon, Belarusian protests organized on LiveJournal, the lost cellphone of a woman named Ivanna, Meetup.com, flash mobs, Twitter, and more -- which Shirky uses to illustrate his points.

The book is filled with bits of insight and common sense, explaining why young people take better advantage of social tools, how the Internet affects social change, and how most Internet discourse falls somewhere between dinnertime conversation and publishing.

Shirky notes that "most user-generated content isn't 'content' at all, in the sense of being created for general consumption, any more than a phone call between you and a sibling is 'family-generated content.' Most of what gets created on any given day is just the ordinary stuff of life -- gossip, little updates, thinking out loud -- but now it's done in the same medium as professionally produced material. Unlike professionally produced material, however, Internet content can be organized after the fact."

No one coordinates Flickr's 6 million to 8 million users. Yet Flickr had the first photos from the 2005 London Transport bombings, beating the traditional news media. Why? People with cellphone cameras uploaded their photos to Flickr. They coordinated themselves using tools that Flickr provides. This is the sort of impromptu organization the Internet is ideally suited for. Shirky explains how these moments are harbingers of a future that can self-organize without formal hierarchies.

These nonorganizations allow for contributions from a wider group of people. A newspaper has to pay someone to take photos; it can't be bothered to hire someone to stand around London underground stations waiting for a major event. Similarly, Microsoft has to pay a programmer full time, and Encyclopedia Britannica has to pay someone to write articles. But Flickr can make use of a person with just one photo to contribute, Linux can harness the work of a programmer with little time, and Wikipedia benefits if someone corrects just a single typo. These aggregations of millions of actions that were previously below the Coasean floor have enormous potential.

But a flash mob is still a mob. In a world where the Coasean floor is at ground level, all sorts of organizations appear, including ones you might not like: violent political organizations, hate groups, Holocaust deniers, and so on. (Shirky's discussion of teen anorexia support groups makes for very disturbing reading.) This has considerable implications for security, both online and off.

We never realized how much our security could be attributed to distance and inconvenience -- how difficult it is to recruit, organize, coordinate, and communicate without formal organizations. That inadvertent measure of security is now gone. Bad guys, from hacker groups to terrorist groups, will use the same ad hoc organizational technologies that the rest of us do. And while there has been some success in closing down individual Web pages, discussion groups, and blogs, these are just stopgap measures.

In the end, a virtual community is still a community, and it needs to be treated as such. And just as the best way to keep a neighborhood safe is for a policeman to walk around it, the best way to keep a virtual community safe is to have a virtual police presence.

Crime isn't the only danger; there is also isolation. If people can segregate themselves in ever-increasingly specialized groups, then they're less likely to be exposed to alternative ideas. We see a mild form of this in the current political trend of rival political parties having their own news sources, their own narratives, and their own facts. Increased radicalization is another danger lurking below the Coasean floor.

There's no going back, though. We've all figured out that the Internet makes freedom of speech a much harder right to take away. As Shirky demonstrates, Web 2.0 is having the same effect on freedom of assembly. The consequences of this won't be fully seen for years.

Here Comes Everybody covers some of the same ground as Yochai Benkler's Wealth of Networks. But when I had to explain to one of my corporate attorneys how the Internet has changed the nature of public discourse, Shirky's book is the one I recommended.

This essay previously appeared in IEEE Spectrum.

Now I’ve been “accused” of being a quant in the past (hi rybolov!) but in reality the only dogs I have in this fight are the model and the application of scientific method - and really, ethically speaking, I have to be tied to the latter while applying the former.

And I see a false dichotomy in this whole Quant vs. Qual thing.  We, as a profession, tend to create a political divide between the two which, if it even exists, I’d say is based more on our ignorance rather than our expertise.  After all, we are the profession that regularly multiplies across ordinal scales and uses wonderful models like R=VxTxI.   As someone  learning to deal in probabilities and rationalism, I have to recognize that this discussion is really just about the act of observation using different metrics of measurement.

But how we’re going about observing does not change the fact that there is measurement based on observation.  So if I’m working with you I can easily turn your qualitative scale into a quantitative one, and vice-versa.  Yes, Shrdlu, if we had the time, even your most seemingly Qual things could be Quant! (This flexible world view, btw, is an outcome of that new-fangled Bayesian thing).

COGNITIVE BIAS A-PLENTY

But back to what Rich is saying there about information security and risk - and he isn’t/won’t be the only one saying these sorts of things - we should try to understand what’s really going on rather than get caught up in the emotional hurricane.  Our profession suffers several forms of cognitive bias.  The nature of our jobs and what we do can cause us to be focused on the outcome and not the quality of the decision at the time it was made.  We want to bring in things from other professions that are useful, but at times we do view things outside our profession with false correlation to our own (unfortunately for those who write these sorts of articles, financial risk is completely different than operational risk).  We also have the tendency to focus on negative outcomes without acknowledging the positive outcomes (For example, I hear that Alan Greenspan’s new firm is up a couple of $billion in all this mess since he joined them, short sellers are doing quite well - must be because they have qualitative models or something -grin-).  The effect of these biases are compounded by the facts that proper correlation takes more work than we usually give it, and rational thought is not that easy when there’s a witch-hunt mentality.

What also floats in water? (link to Youtube)

WHAT SHOULD WE BE THINKING ABOUT?

So as you and I read opinions that seem to be the polar opposite of irrational exuberance (and there will be plenty between now and the election) we’ll have to ask ourselves, “what really failed here?”  At the risk (pun) of over-simplification:

• Was There an Error on the part of Probability Theory?

After all, Probability Science like all other fields of knowledge is always “advancing” as they say.  So perhaps probability theory is wrong somehow?

I’m personally disinclined to put the blame here, primarily because I would think that there would be evidence from other fields (like Quantum Mechanics) that something is amiss waaaaay before it hit a field like economics.

• Was There Error In The Model Used to Determine Risk?

Some people who understand real estate valuation and complex derivatives and financial risk want to put the blame here.  It’s a little too early to tell, but one thing is for sure - Financial risk is so different from operational risk I couldn’t begin to hazard an opinion on the subject.   But it would seem that this is really somewhere we might look.

• Was There Error In The  Scale Used (Quantitative vs. Qualitative)?

Honestly?  I find it extremely difficult to understand how this could be the source of financial ruin.

• Was There Error on the part of the Decision Maker?

What if all of the above were just fine, and the decision maker chose short term gain over long term stability?  What if this was (to simplify the matter greatly) a choice of “heads” over “tails” and the coin landed on tails?  What if the model represented the right risk (probability of negative outcome vs. positive outcome), but the complex derivative was sold to someone else who had poor “risk management” (ability to make a good decisions)?

Now I have no clue about complex derivatives, and I’m oversimplifying to be sure - chances are like most things, there are several problems that helped create the primary cause. But it seems to me that as we go into incident response mode for the economy, it’s more helpful to do so in a rational, logical manner.

OTHER THINGS WE MIGHT WANT TO CONSIDER

Consider the Source
Some authors (who I think tend to exploit outcome and hindsight bias,and then combine those with indirect ad hominem attacks in order to sell their books), are actually putting forth arguments against the use of analytics.  The source of this is a current epistemic debate between those who believe that only falsification is certain, and those who maintain that neither proof nor falsification are certain, there are only probabilities.    So before you go believing any “quadrants” of usefulness on faith - I encourage you to understand what is at the heart of the discussion.

We All Have to Live In The Real World
The sun will rise tomorrow, and someone will try to find the source of the problem and do a better job.  Now chances are, they’ll be doing it in a quantitative manner.  Chances are also that at some point their models will fail and we’ll need to build new ones.  And this will happen whether the field is cosmology, economics, meteorology, information security, or professional baseball.

WHAT ABOUT YOU, ALEX?

I’m far from certain and subject to change, but these days I lean towards Robin Hanson & MIchael Lewis w/regards to placing blame.

The reason is psychological. And it's the same dynamic when it's a security vendor trying to sell its products or services, a CIO trying to convince senior management to invest in security or a security officer trying to implement a security policy with her company's employees.

It's also true that the better you understand your buyer, the better you can sell.

First, a bit about Prospect Theory, the underlying theory behind the newly popular field of behavioral economics. Prospect Theory was developed by Daniel Kahneman and Amos Tversky in 1979 (Kahneman went on to win a Nobel Prize for this and other similar work) to explain how people make trade-offs that involve risk. Before this work, economists had a model of "economic man," a rational being who makes trade-offs based on some logical calculation. Kahneman and Tversky showed that real people are far more subtle and ornery.

Here's an experiment that illustrates Prospect Theory. Take a roomful of subjects and divide them into two groups. Ask one group to choose between these two alternatives: a sure gain of $500 and 50 percent chance of gaining $1,000. Ask the other group to choose between these two alternatives: a sure loss of $500 and a 50 percent chance of losing $1,000.

These two trade-offs are very similar, and traditional economics predicts that the whether you're contemplating a gain or a loss doesn't make a difference: People make trade-offs based on a straightforward calculation of the relative outcome. Some people prefer sure things and others prefer to take chances. Whether the outcome is a gain or a loss doesn't affect the mathematics and therefore shouldn't affect the results. This is traditional economics, and it's called Utility Theory.

But Kahneman's and Tversky's experiments contradicted Utility Theory. When faced with a gain, about 85 percent of people chose the sure smaller gain over the risky larger gain. But when faced with a loss, about 70 percent chose the risky larger loss over the sure smaller loss.

This experiment, repeated again and again by many researchers, across ages, genders, cultures and even species, rocked economics, yielded the same result. Directly contradicting the traditional idea of "economic man," Prospect Theory recognizes that people have subjective values for gains and losses. We have evolved a cognitive bias: a pair of heuristics. One, a sure gain is better than a chance at a greater gain, or "A bird in the hand is worth two in the bush." And two, a sure loss is worse than a chance at a greater loss, or "Run away and live to fight another day." Of course, these are not rigid rules. Only a fool would take a sure $100 over a 50 percent chance at $1,000,000. But all things being equal, we tend to be risk-adverse when it comes to gains and risk-seeking when it comes to losses.

This cognitive bias is so powerful that it can lead to logically inconsistent results. Google the "Asian Disease Experiment" for an almost surreal example. Describing the same policy choice in different ways--either as "200 lives saved out of 600" or "400 lives lost out of 600"-- yields wildly different risk reactions.

Evolutionarily, the bias makes sense. It's a better survival strategy to accept small gains rather than risk them for larger ones, and to risk larger losses rather than accept smaller losses. Lions, for example, chase young or wounded wildebeests because the investment needed to kill them is lower. Mature and healthy prey would probably be more nutritious, but there's a risk of missing lunch entirely if it gets away. And a small meal will tide the lion over until another day. Getting through today is more important than the possibility of having food tomorrow. Similarly, it is better to risk a larger loss than to accept a smaller loss. Because animals tend to live on the razor's edge between starvation and reproduction, any loss of food -- whether small or large -- can be equally bad. Because both can result in death, and the best option is to risk everything for the chance at no loss at all.

How does Prospect Theory explain the difficulty of selling the prevention of a security breach? It's a choice between a small sure loss -- the cost of the security product -- and a large risky loss: for example, the results of an attack on one's network. Of course there's a lot more to the sale. The buyer has to be convinced that the product works, and he has to understand the threats against him and the risk that something bad will happen. But all things being equal, buyers would rather take the chance that the attack won't happen than suffer the sure loss that comes from purchasing the security product.

Security sellers know this, even if they don't understand why, and are continually trying to frame their products in positive results. That's why you see slogans with the basic message, "We take care of security so you can focus on your business," or carefully crafted ROI models that demonstrate how profitable a security purchase can be. But these never seem to work. Security is fundamentally a negative sell.

One solution is to stoke fear. Fear is a primal emotion, far older than our ability to calculate trade-offs. And when people are truly scared, they're willing to do almost anything to make that feeling go away; lots of other psychological research supports that. Any burglar alarm salesman will tell you that people buy only after they've been robbed, or after one of their neighbors has been robbed. And the fears stoked by 9/11, and the politics surrounding 9/11, have fueled an entire industry devoted to counterterrorism. When emotion takes over like that, people are much less likely to think rationally.

Though effective, fear mongering is not very ethical. The better solution is not to sell security directly, but to include it as part of a more general product or service. Your car comes with safety and security features built in; they're not sold separately. Same with your house. And it should be the same with computers and networks. Vendors need to build security into the products and services that customers actually want. CIOs should include security as an integral part of everything they budget for. Security shouldn't be a separate policy for employees to follow but part of overall IT policy.

Security is inherently about avoiding a negative, so you can never ignore the cognitive bias embedded so deeply in the human brain. But if you understand it, you have a better chance of overcoming it.

This essay originally appeared in CIO.