Antibodies

For some reason, the most important facts fly in and glue themselves to the back of my brain when I'm skimming and thinking "interesting but not essential," which means the information comes back to me later but I don't have the reference. So I'll just put this out there and perhaps someone will know where it comes from.

I recently read that psychopathy is attached to the X chromosome, and as a result about 10% of men are psychopaths and only about 1% of women are psychopaths (Although I'm going to guess that the 10% number also includes antisocial personality disorder). To clarify, a psychopath is not automatically a serial killer; psychopathy is characterized by lack of empathy and concern only for one's own benefit regardless of any costs to others. If this sounds like some of the bosses you've had, it's because the power hierarchy attracts psychopaths, so the higher you go, the more there are (there apparently exist studies showing the higher percentages of psychopaths in upper management).

Currently it is thought that psychopathy is not a curable condition, although antisocial personality disorder might be. From the standpoint of organizational dynamics, it doesn't really matter: these are poisonous people and you have to get them out of your organization as soon as possible. Even better, the structure of your organization should discourage them from joining in the first place.

Current organizations are set up so that the pain of a poisonous person is displaced, which ends up encouraging the retention of that person. The manager, whose pain happens when there is a vacancy and throughout the process of hiring, has negative incentive to make changes (more hiring pain) and is often distanced from the day-to-day pain of dealing with the poisonous person. The manager has incentives to say "let's wait and see." In addition, we must now have due diligence and a paper trail before firing someone, so after you make a hire you must often wait a year or more before firing. All this time the poisonous person is damaging the team, lowering morale, reducing productivity, and driving team members away. By the time the poisonous person is fired, the destruction is typically irreparable.

This happens in other places than just the corporation. A community might be developed by artists and free-thinkers and become a very interesting and desirable place to live; it becomes a cultural center. Because of our structure, the underlying mechanism is still money, and so people who are attracted (for good reason) to the culture are able to buy their way in using money rather than by contributing more culture, diluting and eventually ruining the original culture. The moneyed cannot see the difference; it still seems like a cool place to them because they don't understand the logic of culture, only the logic of money. The culture creatives are reduced to fighting the changes (which takes time and energy away from creating more culture) or flight to a new undiscovered cultural center, as yet un-invaded by the unintentional destroyers. As a result, the culture creatives can only achieve so much before the destructive forces set in.

The human body begins working to expel or destroy poisonous invaders the moment it recognizes them. This comes from natural selection; those that dawdled died out. The current organizational structure, on the other hand, seems to actively impale itself on whatever can destroy it. Some who have been involved in startups would argue that this happens the moment the organization is forced to "grow up" by the addition of "proper business managers" who institute things like the Human Resource (not "person," but "human resource") policies that create the long delays in removing toxic individuals.

Perhaps we can learn from the structure of living organisms (possibly, groups of discrete organisms). In a sense, we mimic that structure by having policemen and HR people dedicated to controlling problematic individuals, but that doesn't seem to work so well -- it's a big deal to invoke those agents and they always seem to apply overwhelming force to a problem. Perhaps, instead of giving a few individuals the singular job of control, it should be a role that most or all members of a community assume (after all, we are not components of a single living organism, but a community of discrete organisms). At Burning Man, experienced participants know to help indoctrinate newbies into the mores of the culture.

The correction might even come indirectly. In Open Spaces Conferences, this happens via the Law Of Two Feet, which says that if something isn't working for you, it's your job to get up and go try to find something that does work for you. Notice that you don't sit there and try to figure out why something isn't working for you or how to fix it; ultimately the reason isn't important. If the reason happens to be that someone is misbehaving, the effect is the same -- no one is trapped to suffer the misbehavior. In addition, people know that they don't have some kind of arbitrary power to misbehave (as in: I'm your manager or I can't be fired quickly) -- they know people will leave if it isn't working for them, which implicitly discourages misbehavior (for those who, unlike psychopaths, are able to control themselves).

I'm not suggesting this as a solution. I'm only saying that "the next organizational structure" definitely needs a solution to the problem of toxic individuals and the best one will be (I think) subtly enforced by the community rather than being imposed (or avoided) by the traditional power hierarchy. Also note that an individual that is toxic in one situation might bloom in another, and the best structure will not throw someone away just because they don't fit into a particular role; such a person could be a tremendous source of wealth in the right role (witness the story of Brad Bird collecting "the worst team members" at Pixar which resulted in their string of mega-hits). Someone who is a change agent can cause discomfort and be labeled "toxic" because they are disrupting the status quo for a good reason; ejecting such people will ultimately destroy the company by stultifying positive changes. But somehow, the truly poisonous individuals must be removed as rapidly as possible, and the ideal organization will not even be attractive to such people.

I don't know how to do this yet, but I'm hoping that the solution could be as subtle and elegant as The Law of Two Feet.

The Black Swan, Summarized: Becoming a Skeptical Empiricist

This completes my coverage of the book, and follows my previous musings on this important tome.

The map is not the territory, or: All models are wrong, some are useful -- and some are very damaging. The most dangerous thing to do is pretend that, because some things hardly ever happen, they never happen. Assuming that an approximation is the truth will eventually bring disaster.

For Taleb, one of the biggest offenders is the Gaussian distribution (the bell curve). I would add the field of genetics and evolution -- misapplied we got eugenics and "the survival of the fittest" (a phrase Darwin apparently never used) which have justified monstrous behavior (or maybe the monsters would have found some other way to justify what they did if these weren't available).

The book is a challenge -- it is hard to let go of your (albeit wrong) certainties and nerdist "knowledge" of rules. But once you do, the world expands, and it begins to feel liberating to be free of the naive models and the boxes they put you in.

Sometimes he is overly harsh -- which I understand; the battles he has fought has made him carefree about expressing his criticisms (for example, on page 336 he called the TED conference, something I get tremendous value from, "a monstrosity that turns scientists and thinkers into low-level entertainers, like circus performers"). And yet, I would rather have his often-entertaining harshness than for him to be polite and inoffensive.

He has bursts of engaging writing -- the best is usually when he is being completely honest about himself, or venting his rage on the stupidity of model-following sheep -- mired in rather a lot of mediocrity. Were he a better writer, the book would be half the size and riveting throughout. And yet, the payoffs in struggling through vastly outweigh the extra verbiage.

One glaring example of poor writing: He uses terms before introducing them. There are numerous places where he'll use a term or phrase as if he assumes you know what it means, then much later in the book he'll introduce it as if it's the first time you're seeing it.

One of the main messages of the book is this: You can't just look at probabilities; you must also factor in the impact of a particular event. By including impact, you change the things you take seriously. An event with very low probability is not considered important in our current view of the world simply because it is unlikely to happen. But in the Black Swan world you factor in the impact, and an event with very low probability but very high impact is considered important.

Taleb never did define the term "luck," although he used it rather freely. Although this disappointed me, I think I eventually came up with my own definition, one I suspect he would disagree with. I think luck is not just what happens to you, but what you do with what happens to you. Someone might win or inherit a windfall, which is a lucky happenstance, but then they can choose to spend it on a jetski, pay off debt, invest in the market (a gamble, but with a potential upside), or invest it in themselves by taking some classes or getting some training. Choosing the last option has a far higher probability of paying off in the long term, and although it's typically a slower payoff than the stock market, the market can also take from you while improving your skills is something that can't be taken away. Once you do this, you have a much higher "chance" of getting more money, with which you can leverage your previous investment. So how much is really "luck," and how much is the result of waiting for and -- more importantly -- leveraging the results of happy accidents?

Passages I marked:

• (Page 64) "The narrative fallacy addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently, forcing a logical link, an arrow of relationship, upon them. ... counter to what everyone believes, not theorizing is an act ... it takes considerable effort to see facts (and remember them) while withholding judgment and resisting explanations ... it is largely anatomical, part of our biology, so fighting it requires fighting one's own self."

I know from memorizing lines for plays that one of the easiest tricks is to create a kind of story with pictures (our brains prefer the visual). I wonder if meditation might be the gentle act of turning off the narration, albeit without any hope of really turning it off, but just guiding the mind to quietness every time it begins to narrate, as you guide a child to calm itself down.

• (Page 155) "Furthermore, they encountered shocking hostility towards their empirical verifications. 'Instead [statisticians] have concentrated their efforts in building more sophisticated models without regard to the ability of such models to more accurately predict real-life data,' Makridakis and Hibon write."

Apparently it's possible to be in denial so much that you demand that the map be the territory.

• (Page 155) "When an economist fails to predict outliers he often invokes the issue of earthquakes or revolutions, claiming that he is not into geodesics, atmospheric sciences, or political science, instead of incorporating these fields into his studies and accepting that his field does not exist in isolation. Economics is the most insular of fields; it is the one that quotes least from outside itself!"
• (Page 183) "To borrow from Warren Buffet, don't ask the barber if you need a haircut -- and don't ask an academic if what he does is relevant. ... the problem with organized knowledge is that there is an occasional divergence of interests between academic guilds and knowledge itself ... In academia a tenured faculty is permanent -- the business of knowledge has permanent "owners." Simply, the charlatan is more the product of control than the result of freedom and lack of structure."
• (Page 185) "So if people make inconsistent choices and decisions, the central core of economic optimization fails. You can no longer produce a "general theory," and without one you cannot predict."
• (Page 188) "... what the philosopher Nelson Goodman called the riddle of induction: We project a straight line only because we have a linear model in our head."
• (Page 210) "'There are some people who, if they don't already know, you can't tell 'em,' as the great philosopher of uncertainty Yogi Berra once said. Do not waste your time trying to fight forecasters, stock analysts, economists, and social scientists, except to play pranks on them. They are considerably easy to make fun of, and many get angry quite readily. It is ineffective to moan about unpredictability: people will continue to predict foolishly, especially if they are paid for it, and you cannot put an end to institutionalized frauds. If you ever do have to heed a forecast, keep in mind that its accuracy degrades rapidly as you extend it through time. If you hear a 'prominent' economist using the word equilibrium or normal distribution, do not argue with him; just ignore him, or try to put a rat down his shirt."
• (Page 221) "Of the five hundred largest U.S. companies in 1957, only seventy-four were still part of that select group, the Standard and Poor's 500, forty years later. Only a few had disappeared in mergers; the rest either shrank or went bust."
• (Page 251, in reference to the Gaussian "Bell" curve) "I have an epistemological problem with that, with the need to justify the world's failure to resemble an idealized model that someone blind to reality has managed to promote. ... To me it is frequently (nay, almost always) the users of the bell curve who do not understand it well, and have to justify it, and not the opposite."
• (Page 267) with caveats, he likes these "popular science" books "that summarize the research in complex systems":

• Mark Buchanan's Ubiquity
• Phillip Ball's Critical Mass
• Paul Ormerod's Why Most Things Fail

• (Page 268) "In the absence of a feedback process you look at models and they that they confirm reality. ... the world, epistemologically, is literally a different place to a bottom-up empiricist. We don't have the luxury of sitting down to read the equation that governs the universe; we just observe data and make an assumption about what the real process might be, and "calibrate" by adjusting our equation in accordance with additional information. As events present themselves to us, we compare what we see to what we expected to see. It is usually a humbling process, particularly for someone aware of the narrative fallacy, to discover that history runs forward, not backward. As much as one thinks that businessmen have big egos, these people are often humbled by reminders of the differences between decision and results, between precise models and reality. What I am talking about is opacity, incompleteness of information, the invisibility of the generator of the world. History does not reveal its mind to us -- we need to guess what's inside of it."
• (Page 283) "Locke's definition of a madman: someone 'reasoning correctly from erroneous premises'."
• (Page 288) "Can someone explain to me why I should care about subatomic particles that, anyway, converge to a Gaussian? People can't predict how long they will be happy with recently acquired objects, how long their marriages will last, how their new jobs will turn out, yet it's subatomic particles that they cite as "limits of prediction." They're ignoring a mammoth standing in front of them in favor of matter even a microscope would not allow them to see."
• (Page 289) "The same context specificity leads people to take the escalator to the StairMasters, but the philosopher's case is far, far more dangerous since he uses up our storage for critical thinking in a sterile occupation. Philosophers like to practice philosophical thinking on me-too subjects that other philosophers call philosophy, and they leave their minds at the door when they are outside of these subjects. ... We should be far less tolerant of philosophers, with their bureaucratic apparatchiks closing our minds. Philosophers, the watchdogs of critical thinking, have duties beyond those of other professions."
• (Page 303) "Nerd Knowledge: the belief that what cannot be Platonized and studied does not exist at all, or is not worth considering. There even exists a form of skepticism practiced by the nerd." (This is the best definition of "nerd" that I have seen. It emphasizes the nerdish classification of knowledge as simply "right" or "wrong" without any ability to question or analyze. I remember my own nerd phase which, although it lasted an unfortunately long time, is fortunately in my distant past. Or so I believe).
• (Page 309) "People say things in person they would never put in print." (This is a strong justification for having conferences).
• (Page 311) "Empiricism is not about not having theories, beliefs, and causes and effects: it is about avoiding being a sucker, having a decided and preset bias about where you want your error to be -- where the default is. An empiricist facing series of facts or data defaults to suspension of belief (hence the link between empiricism and the older skeptical Pyrrhonian tradition), while others prefer to default to a characterization or a theory. The entire idea is to avoid the confirmation bias (empiricists prefer to err on the side of the disconfirmation/falsification bias, which they discovered more than fifteen hundred years before Karl Popper)."
• (Page 312) "The exact opposite of redundancy is naive optimization. I tell everyone to avoid attending (orthodox) economics classes and say that economics will fail us and blow us up (and, as we will see, we have proofs that it failed us; but, as I kept saying in the original text, we did not need them; all we needed was to look at the lack of scientific rigor -- and of ethics). The reason is the following: It is largely based on notions of naive optimization, mathematized (poorly) by Paul Samuelson -- and this mathematics contributed massively to the construction of an error-prone society. An economist would find it inefficient to maintain two lungs and two kidneys: consider the costs involved in transporting these heavy items across the savannah. Such optimization would, eventually, kill you, after the first accident, the first "outlier." Also, consider that if we gave Mother Nature to economists, it would dispense with individual kidneys: since we do not need them all the time, it would be more "efficient" if we sold ours and used a central kidney on a time-share basis. You could also lend your eyes at night since you do not need them to dream."
• (Page 313) "The same idea applies to debt -- it makes you fragile, very fragile under perturbations, particularly when we switch from the assumption of Mediocristan to that of Extremistan. We currently learn in business schools to engage in borrowing (by the same professors who teach the Gaussian bell curve, that Great Intellectual Fraud, among other pseudosciences), against all historical traditions, when all Mediterranean cultures developed through time a dogma against debt. ... "Happy is he who owes nothing." Grandmothers who survived the Great Depression would have advised the exact opposite of debt: redundancy; they would urge us to have several years of income in cash before taking any personal risk -- exactly my barbell idea of Chapter 11, in which one keeps high cash reserves while taking more aggressive risks but with a small portion of the portfolio. Had banks done that, there would have been no bank crises in history."
• (Page 319) "Our psychology conspires: people like to go to a precise destination, rather than face some degree of uncertainty, even if beneficial."
• (Page 325) (A discovery that surprised Taleb) "And I had missed something central: living organisms (whether the human body or the economy) need variability and randomness. What's more, they need the Extremistan type of variability, certain extreme stressors. Otherwise, they become fragile. That, I completely missed. Organisms need, to use the metaphor of Marcus Aurelius, to turn obstacles into fuel -- just as fire does."
• (Page 329) "Our aversion to variability and desire for order, and our acting on those feelings, have helped precipitate severe crises."
• (Page 343) "... the latin poet Lucretius, who did not attend business school, wrote that we consider the biggest object of any kind that we have seen in our lives as the largest possible item ..."
• (Page 347) "The Black Swan is the very first attempt (that I know of) in the history of thought to provide a map of where we get hurt by what we don't know, to set systematic limits to the fragility of knowledge -- and to provide exact locations where these maps no longer work."
• (Page 350) "If we need data to obtain a probability distribution to gauge knowledge about the future behavior of the distribution from its past results, and if, at the same time, we need a probability distribution to gauge data sufficiency and whether or not it is predictive of the future, then we face a severe regress loop. This is a problem of self-reference akin to that of Epimenides the Cretan stating whether or not Cretans are liars. ... a probability distribution is used to assess the degree of truth but cannot reflect on its own degree of truth and validity."
• (Page 352) "Less than 0.25 percent of all the companies listed in the world represent around half the market capitalization, a less than minuscule percentage of novels on the planet accounts for approximately half of fiction sales, less than 0.1 percent of drugs generate a little more than half the pharmaceutical industry's sales -- and less than 0.1 percent of risky events will cause at least half the damages and losses."
• (Page 354) "At the time of writing, the stock market has proved much, much riskier than innocent retirees were led to believe from historical discourses showing a hundred years of data. It is down close to 23 percent for the decade ending in 2010, while the retirees were told by finance charlatans that it was expected to rise by around 75 percent over that time span."
• (Page 362) "The Modelers' Response: We know all that. Nothing is perfect. The assumptions are reasonable. The assumptions don't matter. The assumptions are conservative. You can't prove the assumptions are wrong. We're only doing what everybody else does. The decision-maker has to be better off with us than without us. The models aren't totally useless. You have to do the best you can with the data. You have to make assumptions in order to make progress. You have to give the models the benefit of the doubt. Where's the harm?"
• (Page 367) "... for psychological comfort some people would rather use a map of the Pyrenees while lost in the Alps than use nothing at all. ... They would prefer a defective forecast to nothing."
• (Page 368) "... people do not realize that success consists mainly in avoiding losses, not in trying to derive profits. ... Positive advice is usually the province of the charlatan. ... Linked to this need for positive advice is the preference we have to do something rather than nothing, even in cases when doing something is harmful. ... in many instances it was better -- and wiser -- to have no models than to have the mathematical acrobatics we had."
• (Page 369) "The very term iatrogenics, i.e., the study of the harm cause by the healer, is not widespread -- I have never seen it used outside medicine."
• (Page 370) "You cannot do anything with knowledge unless you know where it stops, and the cost of using it."
• (Page 371) "Avoid optimization; learn to love redundancy. ... Redundancy (in terms of having savings and cash under the mattress) is the opposite of debt. ... Overspecialization also is not a great idea."
• (Page 373) "Do not confuse absence of volatility with absence of risk."
• (Page 374) "What is fragile should break early, while it's still small. Nothing should ever become too big to fail."
• (Page 374) "No socialization of losses and privatization of gains. ... We got ourselves into the worst of capitalism and socialism. In France, in the 1980's, the socialists took over the banks. In the United States in the 2000s, the banks took over the government. This is surreal."
• (Page 375) "It is the asymmetry of the bonus system that got us here. No incentives without disincentives: capitalism is about rewards and punishments, not just rewards."
• (Page 375) "Compensate complexity with simplicity."
• (Page 376) "... an economic life closer to our biological environment: smaller firms, a richer ecology, no speculative leverage -- a world in which entrepreneurs, not bankers, take the risks, and in which companies are born and die every day without making the news."

A Trust-Based Organization

I have declared elsewhere that "Trust is the first law of the Internet." Physically, the Internet uses a "connectionless protocol," and we've seen the negative effects of that ability to be disconnected: If there's no trail, people can behave badly with almost no consequences. You don't have the illusion of safety from a physical location; you must be able to trust before you can do business.

This morning I was having a conversation with a friend about fundraising, and all the negative experiences we've had with fundraisers, hitting you up on the street or calling you during dinner, annoying you and wasting your time and making you less inclined to give. On top of that, many organizations use their non-profit status as a device, because most people have come to believe that non-profit automatically means "do-good." It turns out that non-profit is just a classification of a business type; you can't buy stock and it doesn't pay out any kind of dividend -- it doesn't create profit. This doesn't mean, however, that it is fiscally well-behaved. If a non-profit does fundraising, it uses part of its money to finance that fundraising, and to pay for the staff, etc. A non-profit may decide that it needs to pay its senior executives very well, for example. So perhaps only 6% of the money you think you're giving for schools in poverty-stricken parts of the world might actually get there (via, in the worst case, some third-party organization, so the fundraiser becomes nothing but trickery), while the rest goes -- legally -- into the pockets of the organization that convinced you to give the money. It's "not stealing" in the same way that credit-card interest rates are "not usury."

Even when you give money to a certifiably good cause, there can be all sorts of unpleasant unintended side-effects. Non-profits will sometimes raise money by selling their lists to other non-profits, so you'll start getting cards, letters and phone calls from new organizations as your "reward" for donating. Even if this doesn't happen, the original organization will expect you to donate again, so you'll keep hearing from them. You may choose to ignore the missives (if you're lucky enough not to get phone calls), but you feel bad about the wasted resources.

The thing is, I want to give money to good causes (for my own personal definition of "good," naturally). And if I could do it without all the hassle I would definitely give more -- and there's the business opportunity. I'd love to see an organization which specializes in discovering the best non-profits (and weeding out all the fakers and crooks), reviewing them, summarizing the way they do business (how much of the donations go to organizational overhead vs. the actual work), finding the exceptional ones, categorizing them, presenting information about their projects to you, and using metrics the way Amazon and Netflix do, to help you discover new organizations that you might like to donate to. And perhaps most importantly, this organization would insulate you from all fundraising efforts by keeping you anonymous, and also giving you a way to decline fundraisers by saying that you donate through this service.

I've seen a couple of attempts to help. BoingBoing.net publishes an annual charitable giving guide of non-profits that it likes. The Berkeley Center for New Media has its Donation Dashboard, an experimental project that attempts to link you to appropriate non-profits.

I could imagine it working something like stock websites, so that you could decide a monthly amount that you want to donate and periodically go in and tune how you're doing the donations, finding new organizations etc. So you'd do it when you were in the mood, not because you get a phone call at dinner time.

Of course, the organization I am proposing would have to be a paragon of trustworthiness, itself. It would be handling a lot of money. It would need to be very transparent. Ideally it would have enough clout to (for example) get rid of most of the overhead of credit-card transactions (possibly by shaming banks and credit-card organizations into giving them up, or by forming their own banking institution), and lower or eliminate the transaction costs of each individual donation. From my perspective, I don't want to have to think about those things; I'd like to just be able to make a monthly bank transfer, and get the appropriate tax deduction form at the end of the year (the organization would make sure that everything was in fact tax-deductible, so that the IRS would automatically trust a form from them). The organization should also help make it easy for businesses to donate by taking the administrative load.

Do this for me, and I'll give more. The current system is just too much trouble.

Tilling & Waiting

Companies that don't suck to work for are so rare that I've gone through most of my life just not believing that they exist. I've only recently discovered that occasionally someone gets it right -- usually just for awhile; HP, for example, went from one of the best companies to work for to one of the worst. From my new perspective I shrug this off as inevitable, given the structure and goals of virtually every company out there.

It's frustrating, though, because I keep thinking that there must be some way to change the way we see things, turn something upside down the way we do with Open Spaces, that would suddenly create a company that's not only  great to work for but that you know will continue to be great to work for, because there's no way that hiring a new CEO or getting a new boss or coworker would suddenly make it go to hell the way current companies do.

It's even more frustrating because all I'm talking about is an idea. Not something that requires venture capital or a breakthrough in technology. I'm just sitting here tilling the mental ground, waiting for some kind of deeper understanding, some mental shift that will allow the idea to pop into my head.

Self-Value, Revisited

At this year's Java Posse Roundup, as with previous iterations, a number of discussions arose around business issues. My hot-button suggestion of hour-banking came up, and one gregarious young man kept poking at the idea. Fortunately I was able to suppress my annoyance at this poking, because he eventually came up with an interesting variation, inspired by Agile Methods.

The biggest reaction to hour-banking was the lack of weighting of hours. I suggested, because we can't know what anyone's true value will be to the company, that we simply say that all hours are equal. If I am CEO and you are somewhere in production (for example), the industrial-age approach is to say that a CEO-hour is hundreds of times more valuable than someone at the bottom of the hierarchy. While no one except those in senior management think this is reasonable or fair, many seemed to dislike the idea that one of their hours would not be worth more than one of someone else's hours.*

The new idea (also suggested in the discussion group) was to "value the task, not the person." In Agile planning, tasks are given points to indicate how long they might take, based on the current velocity of the team (as measured in the most recent iteration) and a guess as to how complex a task might be (this can be adjusted when new data is uncovered).

One benefit is that tasks can now be weighted according to other factors. If a particular task is considered especially useful or valuable it can be given more weight. If a task is considered tedious or undesirable (such as cleaning the kitchen or the bathroom), it can be weighted upward until the points make it desirable.

Note, however, that while task-weighting adds another dimension to the idea of hour-banking, it doesn't address many of the misgivings that people have -- in particular, the strange idea that hours or points can expand upward without limit, so you don't have the (I think illusory) "certainty" of being able to say that you own exactly n percent of the company, thus the value of your hours/points can shift over time (note that, if the net effect of the new-style organization is to produce much greater value within the company, this is a good thing).

I do not present this idea as a final endpoint, so please do not react too strongly. It is instead a talking point, an attempt to break out of what is literally rows of boxes (the spreadsheet and accompanying thought patterns). Try to let your mind go free and allow yourself flights of "what if?" Whatever we come up with should have the following goals:

• It should help reduce the need to get outside funding (i.e. to sell the future of the company to someone who happens to have money, and who invests not because they care about the company but because they want to make more money). Those who invest (themselves, not just their money). in the company should be those who have a stake in the company itself, not just its profits.
• To add to the previous point: the system should help you invest yourself in the company, not just your money/time/ideas/skills -- and that will only work if you see some kind of reasonably fair ownership in compensation for that investment. I don't think that cutting up the zero-sum pie up front produces that reasonable fairness.
• It should favor relationships over transactions. By "transaction," I mean something that occurs in (approximately) an instant. Purchasing a product can be seen as transactional, if the company has nothing more to do with the customer after that, or relational, if it is the beginning of a long-term connection with a customer (to a short-term thinker, transactions can seem like the cheaper and more profitable approach, but it has been shown again and again that creating a long-term customer base produces much greater benefits). Starting a company can be seen as a transaction -- because I start a company, I own most or all of that company. As a result, I can control everything, make all the big decisions and get all the benefits, which sounds like a good deal for me in short-term thinking (plus, I can fire everyone else and still get profits). But in doing so, I remove all incentive for everyone else in the company to invest in anything except what will get them money and power in the organization. There is no long-term relationship, we are not a "family" and those poor fools who believe that clever rhetoric are eventually in for a surprise, and such a company is not interested in innovation or the growth of its (actual) assets -- the people that work there (the industrial-age organization is designed to see money as the one true asset and employees as replaceable albeit troublesome cogs; witness the rush to offshoring if you doubt this).
• It should resist our training towards the creation of power hierarchies.
• I'm sure there are other goals that should be included in this list -- if you have ideas, please include them in the discussion group (scroll to the bottom of this page for details).

Consider the possibility of creating the new-business-structure equivalent of the Agile Manifesto -- something that says what we want to produce while trying to (temporarily) stay out of the trap of judging the feasibility of implementing such a thing.

* There were also a number of declarations of why it couldn't possibly work, some invoking "human nature" (although those arguments did not acknowledge or factor in the possibility that "training" might account for some of what we ascribe to "human nature," a term I find vague and unhelpful) and others citing legal and accounting reasons that would prevent anything other than industrial-age organizations. Another issue some folks had was the idea that hours were not a fixed percentage like a stock sale represents, but instead were an ever-expanding pool.

The Limits to Knowing Our Limitations

In reference to my musings on Occam's Razor, a reader forwarded a link to the TED talk David Deutsch: A new way to explain explanation. Deutsch presents what could be thought of as a variation of Occam's Razor: a good explanation should not be easily changeable when new data arrives. There should be, in other words, some kind of commitment to your explanation. In scientific terms, something has got to be disprovable, and you must put a stake in the ground and take a bit of a risk (of being wrong).

I haven't decided what to think about that, but what did bother me about Deutsch's presentation is his repeated use of the word Truth with a capital 'T.' This is the same thing I have heard throughout the years when people talk about the magical truth somehow embedded in mathematics: "math doesn't lie" and other such rubbish. Indeed, while studying physics as my undergraduate major, I remember the first time I was confounded by a physics professor "throwing away higher-order terms" during a derivation because such terms apparently made a negligible contribution to the result. If math is truth, how is it you can throw bits of it away?

Another puzzler was a professor starting with a representation of matter as billiard balls and springs and deriving the specific heat "within an order of magnitude." Why so much error, once we entered the realm of math?

It turns out that math, in this case, is just a representation of a model which, if that model is accurate enough, allows you to make some reasonable predictions. There is nothing particularly truthful about math, although it does a pretty good job of keeping track of things. Pure math can seem truthful, but -- unsullied with data from the real world -- it can only be truthful about math itself, and thus tells you very little.

I am no longer puzzled about things like throwing away higher-order terms. That all makes sense; we are just making approximate models of the world. What puzzles me is this near-religious faith in math that people seem to have. They may indeed know something that I don't, but I suspect they are just blindly ascribing capital-T Truth to math without considering that the math is only an approximate representation of a model which is only an approximate representation of some data points -- a story that we have made up about the world. Once you start believing this, it's an easy step to creating spreadsheet forecasting models and believing those.

This seems to be the fundamental point Taleb is trying to make in The Black Swan, and was summed up succinctly to me at one workshop I took, as an aside from one of the other participants: "All models are wrong. Some are useful." Taleb seems to go further to say that most models are delusional, because humans made them up and we ignore things we don't like and overestimate our own competence and can't stop ourselves from making up stories and like things very simple and straightforward.

Taleb gives an example of calculating the motion of balls on a pool table (page 178):

"If you know a set of basic parameters concerning the ball at rest, can compute the resistance of the table (quite elementary), and can gauge the strength of the impact, then it is rather easy to predict what would happen at the first hit. The second impact becomes more complicated, but possible; you need to be more careful about your knowledge of the initial states, and more precision is called for. The problem is that to correctly compute the ninth impact, you need to take into account the gravitational pull of someone standing next to the table... And to compute the fifty-sixth impact, every single elementary particle of the universe needs to be present in your assumptions! ... Now, consider the additional burden of having to incorporate predictions about where these variables will be in the future. ... Note that this billiard-ball story assumes a plain and simple world; it does note even take into account these crazy social matters possibly endowed with free will."

Even a system which starts out as calculable rapidly becomes nonlinearly incalculable.

Taleb gives another example, of a sine wave. If you zoom into the very beginning of the sine wave, it appears to be a straight line. Without any further data points than those at the beginning, you are completely justified in thinking it is a straight line and you may even get some useful predictions that way. But only for awhile. After a short time, the curve will assert itself, and you might even correct your model by turning it into a curve, but eventually even that will be wrong.

Ultimately, if you try to assert that you know the capital-T Truth about something you'll almost certainly be wrong. But if you take instead Taleb's prescription and become a "skeptical empiricist," you'll always focus on finding something useful to do with the data and avoid as much as possible creating a model -- unless it turns out to be helpful. If you do create a model, you will be ready to throw it away at a moments notice by not ascribing any special qualities to it -- most especially Truth. This is why the Agilist approach of collecting data and using that data only to estimate what can be accomplished in the next iteration works well. This approach intuitively understands the limitation of the billiard-ball problem: just because you can make a pretty good approximation of what happens next, doesn't mean you have a spreadsheet formula that tells you what happens any time after that.

To understand this means you can keep out of the trap of linear thinking. We love the idea that we can predict the future, because it makes us comfortable: No Black Swans for us! Therefore, we will predict the future, and it will be a simple, sensible and linear prediction. After some happy spreadsheeting, we convince ourselves we are safe, at the expense of any connection with reality.

A Small Step Towards the Future of Education

Khan Academy

This is today's equivalent of putting flash cards on the computer, back when computers were new. Nothing particularly revolutionary about seeing a mini lecture on the web instead of seeing it in the classroom. Eventually, education via the web will look very different, but...

• This was accomplished by one person, self-funded, with off-the-shelf tools and using the existing infrastructure that was freely available (an example of the benefits of cheap experimentation).
• So many people find this valuable because he's good at it. He didn't have to be vetted by academic institutions or climb some ladder in order to be "allowed" to make these videos (again, cheap experimentation).
• There are a lot of people who don't feel comfortable asking for help, going to study sessions where there are smarter or more aggressive students to contend with, etc. The hurdle is very low for these lectures, which help develop student confidence to the point where they can interact more comfortably in classroom situations.

How many more situations have already been enabled by current technology, that we just haven't been able to see yet?

We Are Bad At Predicting

And yet we stubbornly continue to insist on predictions. Worse, when our predictions fail we are somehow able to ignore the failure, and go on to predict again (sometimes even more ambitiously than before). Entire industries (financial advisers, for example, and a large segment of banking) are built on predictions despite solid evidence, studies and repeated experience showing that these industries are worse than the average person than predicting the results they are supposed to be expert in. Religions have been founded on mistaken predictions -- after the predictions failed. Harken back to Y2K. I -- with background and experience building embedded systems -- was told in no uncertain terms that all the embedded systems would fail, and anything containing an embedded system would wreak havoc. I tried to explain that many embedded systems had no idea what time it was, and even more had no care for the date, but to no avail. January 2, 2000 dawned and those soothsayers had mysteriously vanished -- or at least their memories of such certain predictions were as if never spoken.

It's a strange thing; if you think about your own predictions, you'll have a moment of clarity where you briefly realize that yes, you too suck at predictions. But then the instant passes, like one of those dreams that seem so clear and obvious when you wake up but which fade until it seems crazy, or you simply don't remember it. And you're ready to jump headfirst into the next prediction. Even if we do remember our failed predictions, we are easily certain that we've learned from those failures and will definitely do better next time.

We delude ourselves into thinking we are significantly more competent than we are. This is called the Dunning-Kruger Effect (Bob Sutton has a good analysis). A hierarchy with its associated mum effect amplifies this delusion. Could this be an effect of our school system, which discourages mastery for mediocrity? Since very few ever experience the process of mastery, hardly anyone learns how dumb they used to be, and thereby never grows by becoming more conservative about assuming their smartness the next time. As it turns out, the more learned you become about a topic, the more conservative you are about declaiming your expertise. Those who know the least are the most certain that they know a lot.

In The Black Swan, Nassim Nicholas Taleb describes the patterns of delay in human projects and ventures (page 159):

"On the 79th day, if the project is not finished, it will be expected to take another 25 days to complete. But on the 90th day, if the project is still not completed, it should have about 58 days to go. On the 100th, it should have 89 days to go. On the 119th, it should have an extra 149 days. On day 600, if the project is not done, you will be expected to need an extra 1,590 days. As you see, the longer you wait, the longer you will be expected to wait."

Note the effect this (should) have upon sunk-cost thinking, especially where it concerns scheduling.

Taleb goes on and on about our lousiness at prediction and our ignorance about our lousiness at prediction. It seems like we should either:

1. Stop trying to predict altogether, or
2. Shorten our prediction cycle enough to start getting accurate feedback about our predictions. Once we discover a short enough cycle to be useful, work within that constraint.

Agile programming methodologies use approach #2, constantly using the previous cycle as feedback for the next cycle, so as to constantly correct for effects like Dunning-Kruger. Part of the brilliance of Agile is that it doesn't try to discover or explain why we are bad at predictions -- it simply creates a technique to dynamically compensate. And, in line with approach #1, it doesn't try to predict anything when it doesn't have meaningful data on which to base that prediction. Instead, it takes an empirical approach: let's try an iteration, and see how fast we go. Based on that, we have (some) data which will help us make a better guess the next time around, and so on -- in essence, a Bayesian approach to project scheduling. Except that it isn't scheduling, it's estimated-guessing, based on actual experiments.

This is very frustrating if you're stuck in a hierarchical structure. In order to make a decision about whether a project should happen or not, a manager needs to know how much it will cost and when it will be finished. Even if that manager understands the realities of project development -- and the way that Agile tries to work within those realities -- a manager higher up the ladder, schooled in MBA-thinking, will insist on knowing what the cost and schedule will be. How else can you plug it into a spreadsheet and make a decision about whether it's worth the risk? It matters not that we are shown time and again that such predictions are sheer self-delusion, the steps learned at business school are (A) calculate how much surplus resources we have available and (B) decide how to distribute those resources to best ensure new innovations by minimizing the risk/reward ratio.

It's hard to argue against such logic, especially if the person you're arguing with believes that it's possible to reliably predict things like cost and schedule. If you could -- and how hard can it be, really? -- then you can take the results and plug them into some formula and everything is predictable. The compulsion to turn the spreadsheet crank is so great that managers are reduced to begging for numbers, any numbers, just take a guess -- in order to feed the machine of predictional delusion.

Why do we do this? Because we want things in our lives -- at least, some things -- to be consistent. Sure, some spice on top of the consistency is also necessary, but without that consistency we get uncomfortable fast. This is, I think, the reason that my suggestion of changing the way we get paid was met with so much discussion, and consternation. It's possible for me to even conceive of the idea because I grew up with business uncertainty -- my father was a general contractor who regularly had gaps between projects -- and as a consultant I've had long experience with financial uncertainty, and always try to be at least somewhat prepared for Black Swan events. So I have latitude for some degree of experimentation.

In the U.S., we are not trained this way. We are trained to ignore Black Swan events; indeed, we even ignore the more likely events such as getting fired or laid off. Instead, we base our lives on getting a regular income. A car salesman will convince you not to ask what it costs, but instead to ask, "What are the payments?" Most people don't want a job; they need a job because they need a regular paycheck. They need the predictability of that regular paycheck, even if it isn't really very predictable. Even if it is fraught with Black Swans, the system is founded on predictability and we'll do anything (especially: ignore evidence) to believe in that predictability.

Questions

The fundamental questions seem to be (so far):

• What is "risk?"
• What is "value?"

And perhaps most importantly:

• How do we balance our need for consistency (of income, and perhaps other things) with our need to explore, experiment and take risks?

The old model, which pushes as far as possible in the direction of consistency, certainty, and optimization of things it assumes will be the same for a long time, worked for the industrial age. Things could stay the same for quite awhile, so there was little need to take risks by trying new things.

In the information age, even old, unchanging business are disrupted by the efficiencies presented by computers and the machines they enable. Your business may be perfectly fine, but new computerized tools allow your competitors to become more efficient so your optimized business model falls behind.

What was once a conservative approach (don't risk change) now becomes the risky approach (don't change: you lose). What was once value (consistency of manufacturing output) now becomes the small part of the equation (creative companies pull ahead).

The Black Swan (or: Everything You Know is Wrong)

The author Nassim Nicholas Taleb doesn't just have a confusing name. Is he Muslim? (No). Could he be trying to befuddle us with strange thoughts? Indeed he is, but not with any kind of religious thinking. Quite the contrary. His primary thesis is that "highly improbable events" (epitomized by the extremely unlikely appearance of a Black Swan) are so rare that we factor them out of our thinking and pretend they don't exist, but when they do happen they have such an impact that we get blown out of the water.

A deeper issue is that, by ignoring Black Swan events, our entire view of the world, and reality itself, is deeply distorted. Because we want things to be consistent, we pretend that they are, and create a worldview that is mostly illusory.

These ideas are especially important when it comes to business. This would be an excellent first book for MBA students, followed by The Management Myth. I can't imagine an MBA factory doing anything of the kind, because it would undercut most of their own credibility, but if they did it would equip students properly to be skeptical of just about every pronouncement that the management industry puts out, and perhaps we could develop some actual science in that benighted field.

I'm only about a third of the way through the book, but the concepts are important so I want to get the early ones down while they're fresh.

He begins by indirectly explaining what the scientific method is really about. Many people develop the belief that science is about truth and certainty, and so are shocked and appalled when they discover that science never really proves anything with certainty, and that science changes its mind when faced with new information ("See, you can't trust them to know the truth!"). In fact, the basis of science is doubt: If you come up with an experiment and draw a conclusion from it, other scientists must be able to reproduce your findings. Even more important, a theory can never be proved, but it must be disprovable in order to be valid. For a scientist, "true" actually means "so far, no one has disproven it." And if someone does, it doesn't mean the previous theory was patently wrong -- after all, it did conform to the data that was collected up until now -- but that it wasn't big or complete enough to encompass the new data (and this might require a big shift like quantum mechanics or string theory or some other such paradigm change -- but not something like Intelligent Design, which simply ignores existing data).

As an aside, I'll point out -- although Taleb doesn't -- that science doesn't really explain anything. It merely creates models that represent the world and which predict behavior. But the model (even those cast into mathematics) doesn't present a truth any more than "primitive" animist views of the world did: both create models that conform to the data available to the modeler.

Taleb does, however, explore how we are "... explanation-seeking animals who tend to think that everything has an identifiable cause and grab the most apparent one as the explanation." There is apparently strong research that shows that our impulse to make up stories about why things happen is our ground state -- it's what we do naturally, and it takes an effort to turn off our story-making impulse (perhaps meditation is turning it off). For some reason this point is haunting me -- the thought that my brain is constantly spinning tales describing how event B must be causally linked to event A. I've also seen people start creating stories that begin to disconnect from events; over-storying might be one path to madness.

This has fed into my recent pondering of Occam's Razor -- or, as it turns out, the typical misperception of Occam's razor, roughly: "the simplest explanation is the right one." This tool can be helpful when trying to create a model, as long as you are conscious that you are only creating a map and not describing the truth of the territory. But to assume that simplicity is equivalent to correctness is (fortunately) a misunderstanding of the Razor (I do not think that anyone explained this when I learned about it, which suggests that the teachers also misunderstood).

The third mind-twisting concept Taleb has thrown at me (so far) is called "the problem of silent evidence." We make up stories based on the evidence that survives, but a lot of evidence -- perhaps most of it -- vanishes uncollected. And we tend to gravitate towards evidence that we can get our hands upon, which amplifies this. Taleb gives the example of the Phoenicians who allegedly invented the alphabet and produced no literature; we are told they used the alphabet only for recording transactions. As it turns out, they wrote a lot but upon a perishable variety of papyrus, and the writings became silent evidence. Historians worked with the evidence they had, and came to the conclusion that the Phoenicians were a "merchant race." Just because the evidence isn't available, Taleb points out, doesn't mean we can assume it doesn't exist. There is probably vastly more silent evidence than what we can discover -- we must be reticent to draw conclusions based only on the evidence that survives.

An excellent example of this is In Search of Excellence, the book that made Tom Peters a demigod in the world of business consulting. This book is a perfect example of choosing (rather arbitrarily, it seems) what evidence shall be important and what shall be silent. Because of the huge success of the book, a number of subsequent studies seem to show that the principles of excellence in the book are somewhere between questionable and deeply flawed, and yet the book continues to influence hordes of managers. We like a good story.

On page 105, Taleb says: "... people who fail do not seem to write memoirs, and, if they did, those business publishers I know would not even consider giving them the courtesy of a returned phone call ... Readers would not pay $26.95 for a story of failure, even if you convinced them that it had more useful tricks than a story of success. The entire notion of biography is grounded in the arbitrary ascription of a causal relation between specified traits and subsequent events. Now consider the cemetery. The graveyard of failed persons will be full of people who shared the following traits: courage, risk taking, optimism, et cetera. Just like the population of millionaires. There may be some differences in skills, but what truly separates the two is for the most part a single factor: luck. Plain luck."

The book is filled with wry tongue-in-cheek references, and in many places Taleb has dangled, unexplained, this concept of luck. I'm hoping that this is foreshadowing and that he will spring upon us an analysis of this mysterious word.

Roundup 2011 Summary

"If this conference was a company it would be the greatest one in the world to work for." For me, that was the best comment during the closing session. Interestingly, we created a number of product-like results during the week, and a number of successful coding experiments happened, including Amazon Beanstalk for web app hosting (very impressive) and exploration of Docbook and associated tools.

It just keeps getting better. It seems a bit magical, but what I really think is happening is that the trust level continues to rise, and the more that everyone believes that they are free to try things out, the more experiments happen. With experiments come innovation. And, unlike most companies who are primarily concerned with consistency (of product and profit) and see experimentation as a threat, experimentation for the conference doesn't have a downside -- it's good for everyone, and a failure doesn't impact profitability but is a positive learning experience. Last year, for example, we "failed" by trying to over-engineer the progressive dinner night. We assumed there were too many people to fit into one house so we should break them up into groups and move them around like chess pieces. And from that we learned that groups will take care of themselves and that people like to stay together. Plus, it allows a house, once visited, to close up and join the group. As a result, this year's progressive dinner was deemed to be the best ever.

One seemingly small but significant change was making "official" afternoon space on the sign-up sheets. I realize now that I've been resisting this in previous years because the afternoons are "supposed to be" the off-time for outdoor winter activities or general recovery. But people kept wanting to hold tech events then, and over the years we've had more and more of them. Only after putting the space on the sheet did I realize I had been unconsciously violating my professed maxim of "doing whatever you want all the time." The desire to control is a subtle one.

What we ended up with became spontaneously called "coding rodeos" (a logical choice keeping with the theme of Posse and Roundup, plus we have ranches all around and Gunnison even has a rodeo). I participated in several of these myself to the point where I only skied once during the conference. Despite the reduced outdoor activities by many, the idea of moving the Roundup away from winter was flatly rejected -- there's something about the magic of being in the snowy mountains that makes it special regardless of whether you ski. The challenge of getting here also produces a filtering effect -- if you're just looking for training or an ordinary conference, you're unlikely to invest the effort.

Many more people stayed over Friday night than before; word has gotten out that the Roundup doesn't really end on Friday afternoon, it just gets less formal. In fact, we had at least two coding rodeos Friday afternoon, and there was a very popular game of "Settlers of Catan" organized at the conference hall. Both Friday and Saturday night there were group dinners at restaurants and Saturday morning a bunch gathered at McGill's for breakfast. Saturday became an actual ski day for a number of folks.

Group houses -- an idea that spontaneously arose during the second Roundup -- continue to be very popular, and one of the problems with this year's late announcement was that it was more difficult to set up and get into group houses. We're going to get better about this.

Although it is not actually a proceedings like you might see from a more formal conference (or even a more formal open-spaces events), you can get an idea of what happened and see some of the ideas we captured at the official conference site (which anyone attending the conference can edit, much like a wiki but without the messiness of some earlier wiki systems).

We have reserved the conference hall for a summer event, July 25-29. This started because Dick Wall wants to come mountain biking in the summer (Crested Butte has legendary mountain biking, and there's a pretty strong story indicating that mountain bikes themselves were invented here), and then everyone piled on. Right now we have the Scala Camp, a Flex Jam and Joe Nuxoll wants to have some kind of design event which I have tentatively titled "Design it Right." Because there are often afternoon thunderstorms in the summer, we'll do outdoor activities in the morning and tech stuff in the afternoons and evenings. This is a less formal event than the Roundup; it's a collection of casual workshop-like things where you choose what you're working on and work at your own pace in a group-learning experience. At this point things are subject to change, but if this at all interests you, you might want to mark your calendar and float the idea by whomever needs to agree to it. Announcements will be made when we have a more formal description and we're ready for signups.