Game Theory in Management

A staple of “green” management is that any project that tends to kill off significant numbers of animals – even animals that are dangerous, or ones nobody has ever heard of, like Great White Sharks or Snail Darters – is inherently bad. This, of course, raises the question if the converse is true, that projects that tend to save animals’ lives – particularly endangered animals – should be considered good, or at least green.

In Star Trek IV, The Voyage Home (Paramount Pictures, 1986) the officers of the recently-destroyed U.S.S. Enterprise go back in time in a Klingon star ship in an attempt to reintroduce humpback whales into the Earth’s ecosystem, since whales are extinct in the 23^rd Century. Of course, our heroes succeed, and the world’s cataclysmic end, set in motion by those ignorant, un-green humans of previous centuries, has been averted. Great news, right?

A couple of problems. First, in real life, the whales’ populations have been increasing steadily since 1950. The International Whaling Commission, as sensitive as they are to any possible injury to whales, is estimating that the humpback whale population has been increasing by 10% per year for decades, and the species is nearing its pre-hunted (“unexploited”) population rapidly. Barring the sudden onset of some widespread, massively self-destructive behavior, such as spending time trying to “Keep Up With The Kardashians,” the humpback population should certainly be robust enough to respond to any future rocky, cylindrical alien probe sent to communicate with them, but ends up filling the planet’s skies with humanity-threatening thick clouds instead.

So, why are the whales doing better? In Paul Soloman’s Public Broadcasting System-sponsored blog, “The Business Deck,” he spent a bit of space attempting to overturn the “Whale Oil Myth.” This “myth” is that, when kerosene became plentiful and cheap, it was simply no longer economical to hunt whales, whose main retail product was the whale oil that lit the streets of Europe and the eastern United States for decades.  As part of his attempt to perform this overturning, he includes the following table:

By 1850 a consumer had a choice of:

* Camphene or "burning fluid" -- 50 cents/gallon (combinations of alcohol, turpentine and camphor oil - bright, sweet smelling)
* whale oil -- $1.30 to $2.50/gallon
* lard oil -- 90 cents (low quality, smelly)
* coal oil -- 50 cents (sooty, smelly, low quality) (the original "kerosene")
* kerosene from petroleum -- 60 cents (introduced in early 1860s)

The implications here should be obvious, even by PBS analyst standards. If kerosene, which performed the function of fueling lamps better than whale oil could, was available from half to one-quarter of the cost of whale oil, then the whaling industry was doomed. Soloman goes on to offer up some truly strange analysis, such as the amount of whale oil harvested in a given year, divided by the Earth’s human population (???), in trying to assert that the availability of abundant supplies of cheap kerosene did not save the whales, but they fall comically short. Clearly, it was kerosene that replaced the demand for dead whales and their oil.

So, where did the kerosene come from? John D. Rockefeller began working in the oil business in 1866. By 1890, his Standard Oil represented 90% of all American petrochemical production. Strangely enough, Standard Oil is not known for being “green” – quite the opposite, in fact.  Oil companies in general are widely believed to be bandit-like polluters, even if a large number of dead animals can’t be laid at their doorstep.

So, which industry is causing widespread wild animal destruction? The Wildorado Wind Ranch, outside of Amarillo, Texas, sits on 16,000 acres, and generates enough electricity to meet the demands of 50,000 households, Unfortunately, the blades of the turbines tend to kill a large number of migratory birds, to the point that the farm is colloquially known as a “bird Quisenart.” How many birds is the topic of some debate; but, by contrast, the Public Service Company of New Mexico’s San Juan Power Plant, fired mostly by coal, provides power to 40 times the number of customers, without killing off a large number of any animals whatsoever. Just as with Standard Oil, PNM will never receive the credit it deserves for being “green.” They are, in fact, the target of a supposedly green advocacy group’s PR campaign, attempting to portray them as decidedly environ-unfriendly.

But I’m wondering … is that right?

Our August theme is Green Project Management, which presented quite a problem for me. While I’m a big fan of the latter, I’m highly skeptical of efforts to further the former. In short, I’m fairly sure that much of the so-called Green industry is based on bad science and what Nassim Taleb referred to as “flawed tools of inference;” yet, “going green” certainly seems to be the rage within management circles these days. I’d like to add a bit of perspective.

On November 15, 2005, the late Michael Crichton presented a talk sponsored by The Independent Institute in San Francisco, entitled “Fear and Complexity: State of Fear + Why Politicized Science is Dangerous.” One of his best (in my opinion) examples of how “scientists” and “experts” can get the notion of advancing or bettering the state of a given environment horribly wrong, and be extremely slow in not only recognizing the error, but ceasing their damaging efforts, had to do with Yellowstone National Park.

Back in the 1890s it was commonly believed that elk were endangered, so their Yellowstone populations were fed and encouraged. At the same time, predators known to attack elk were hunted or driven away from the park. What happened next was entirely predictable: the elk population exploded, to the point that, in 1915, former president Theodore Roosevelt urged a “scientific study” based on his concerns of the dramatically increased elk population. He was ignored as the Park Service continued to encourage the elk. Soon the pastures were over-grazed, driving away the deer and antelope. The elk, having consumed all available grasses, turned to aspen bark, the nominal sustenance of beavers. When the beaver population dropped, they stopped building dams, and, when that happened, the fish populations were impacted as well, since they depended on beaver dam-created pools to spawn. When the fish and beaver population dwindled, the bear population plummeted. Since bears were also predators on elk, the elk population continued to expand as yet another predator was virtually eliminated.

At the same time, lightning-caused fires were contained and put out instead of being allowed to burn. This led to massive amounts of dried pine needles and branches falling onto the forest floor, creating a layer of dense fuel close to the ground. When a large-scale fire finally broke out, as it did in 1988, the amount of fuel and its proximity to the ground led to the blaze becoming so hot that it destroyed virtually all of the organic matter in the soil, rendering it sterile.  Much of these areas still haven’t recovered.

The problem here, of course, is that the Park Service was attempting to manage a hopelessly complex system, and they either did not recognize it as such, or, having recognized it, arrogantly assumed they nevertheless knew best how to handle it.

Now, flash forward to today’s “green” initiatives. One of their more irksome manifestations is in their indoctrination of students into their take on the impacts of every-day industry (read: management) on the environment. One of my younger son’s friends attending a public high school was informed that, for every molecule of chloro-fluoro carbons that escapes into the atmosphere, one square inch of the Earth’s ozone is destroyed. I laughed out loud, but it was clear my son’s friend took this “fact” very seriously. So, I did the numbers.

There are a little more than 4 billion square inches in one square mile (4,014,489,600).  The Earth is almost 197 million (196,939,900) square miles.  Based on the public high school earth science teacher’s ratio, it would take almost 791 Quadrillion (790,613,180,375,040,000) molecules of CFCs to completely destroy the Earth’s ozone layer.  Daunting, no? So, what would it take to push into the Earth’s atmosphere that number of CFC molecules? Well, there are 7.5 * 10²⁴ molecules in 8 ounces of water. A can of car refridgerant – which, back in the 60s and 70s, contained R-13, the ultimate villain of CFC introductions, don’t you know – sells at my local AutoZone for $49.99 (USD) for 20 ounces (and thanks a heckuva lot for that, “green” managers!). If there were a similar number of molecules of CFC in the 20-ounce can than there are of H2O in 8 ounces of water, one shade tree mechanic, by mis-applying the special car air conditioner input nozzle into his 1975 AMC Gremlin, and venting the entire contents to atmosphere, could have destroyed the Earth’s ozone more than 9 million times over. According to the earth “science” teacher’s ratio, of course.

There are actually studies that show that, when you take into account the environmental impact of procuring and transporting the materials needed for construction, a big ol’ V8 Hummer will have a smaller carbon footprint over its expected life cycle than a Chevy Volt.  So, to all you Leaf drivers who refuse to get out of the passing lane when my Cadillac DeVille’s Northstar engine wants to go blasting past you – save your smug “I’m saving the environment” looks for people who don’t know better.

Time and time again, what conventional wisdom – or even “consensus” science – proclaims to be beneficial to the environment turns out to have the exact opposite impact. The overall environment is simply too complex to know with certainty what a particular course of action’s end result will be, and that includes green project management actions.

Or am I wrong?

When the US Department of Energy first came into existence, their guidance on performing project management was pretty much the same as the rigorous version used by the Department of Defense. However, it didn't take long for those requirements to get watered down via DOE Order 4700.1 and 4700.5, which implemented a so-called "graded approach." This graded approach spelled out the circumstances under which certain projects could opt out of certain aspects of the project management requirements, based primarily on the complexity and risk profile of the specific project. As soon as the new requirements came out -- wouldn't you just know it? -- a whole bunch of projects that had previously been considered appropriate for complete PM implementation were suddenly asserting that they were, actually, very simple, with low risk profiles! A veritable avalanche of arguments against projects having to comply with the complete set of PM requirements ensued, the vast majority of which were completely bogus. I was even involved with a project where the project manager sought to have his project renamed as a program, since programs, per se, were not required to obey the previous rules, only projects.

Flash forward to the Summer of 2009. Based on the release of my first book Things Your PMO Is Doing Wrong (PMI Publishing, 2008), I had been invited by PMI®’s Information Technology Special Interest Group to do a webinar on the difficulties of performing traditional project management techniques in an IT environment. The webinar, entitled “Stop Those Divorce Proceedings! Performing Earned Value Analysis in an Agile/Scrum Environment” was well-received, but I came across some disquieting factoids while I was doing my research. Of course, the frequency with which software projects come in over-budget and late is such that poor performance against their baselines had already become axiomatic. But, since 1986, when Hirotaka Takeuchi and Ikujiro Nonaka  published the seminal work that would form the basis for Agile/Scrum project management, the efforts to streamline project management to rid it of its more restrictive aspects in order to make it useful in an IT environment has led to a few “graded approach” moments which, in turn, threaten to return software projects to their days of persistent overruns and delays.

Take, for example, the aforementioned use (or lack thereof) of Earned Value Management Systems in IT projects. There is a myth, perpetrated by those who don’t like or understand project management in general and Earned Value in particular, that EV “requires” the existence of highly-accurate time-phased budgets, known in the EV world as “Planned Budget,” or, for us old-timers, the Budgeted Cost of Work Scheduled. Without these well-estimated and time-phased budgets, say the ignorant resistors, all subsequent Earned Value analysis is rendered useless. This lie is tailor-made for those who seek to avoid having to do any PM at all by asserting the Agile/Scrum approach. When, they ask, we are in the middle of a scrum, and the scope baseline has been changed and approved, does anyone have time to perform a bottoms-up estimate of the new scope? Isn’t that why Agile/Scrum was invented in the first place – to avoid software development teams having to stop work to formally reset the baselines in order to comply with some stodgy, old PMBOK Guide®-recommended requirement? Surely it’s best to eschew (actually, these Visigoths would never use a word like “eschew,” but hang with me) all Earned Value Methodologies for this work, and subsequent projects like it!

The truth here is that Earned Value does not need a highly-accurate time-phased budget to return extremely valuable management information. In fact, in those instances where the work has been woefully mis-estimated, Earned Value is the best analysis to uncover the error. Say you had a task that has been estimated to cost $100,000 (USD), but the more appropriate cost estimate would have been twice that. The task gets underway. At the end of the first reporting cycle, your project controls analyst asks your overall percent complete, and you inform her that it’s about 25%. However, your project controls analyst collects your actual costs, and they come in at $50,000. Using the traditional formula for calculating an Estimate at Completion, :

EAC = (BAC / CPI)

where BAC is the budget at completion ($100,000) and CPI is the Cost Performance Index (Earned Value divided by Actual Costs; in this example, 25,000 divided by 50,000, or 0.5), she instantly knows that this task will most likely cost $200,000 – which is what a perfectly prescient estimator would have known beforehand.

Okay, but what does this have to do with Agile/Scrum, and costing the change that was introduced in the middle of the last Scrum? The previously shown formula can be algebraically reduced to:

EAC = ACWPcum / % Complete

where ACWPcum is the cumulative amount spent on the task. In the example, $50,000 divided by 25% also produces the $200,000 amount. Simply ask the Task Manager to estimate his percent complete based on the expanded scope, and divide that figure into the altered task’s cumulative actual costs, and you have the new estimate, ready to plug into the cost baseline.

This is but one example, but there are many others where traditional PM techniques may be compromised safely, and others that cannot. How to differentiate? For that, the insightful reader will want to order my recently-released, must-have book, Game Theory in Management, and I look forward to y’alls’ comments.

As a continuation of the Olympic-sized meme and my blog from last week (“Pathologies in our Decision-Making”), I want to explore the notion that large populations are easier to predict than smaller ones. I first became aware of this idea as a boy, when I was reading Isaac Asimov’s Foundation trilogy. For those of you who are unaware of this classic of science fiction, in a future Milky Way Galaxy mankind has spread throughout, and an inter-stellar government of sorts is set up on the planet Trantor. One Hari Seldon invents a way of calculating how future events will unfold, and names it the Risk Management Special Interest Group from PMI®. No, just kidding. He names it “Psychohistory,” and, as one might imagine, many people are very interested in what Hari and his associates “know” is going to happen in the future.

Now, the way Psychohistory’s mathematical processes are (barely) described, one could not possibly predict the behavior of a single gas molecule. But, get a bunch of them together in a container, and suddenly their behavior becomes quite predictable. Since, at this point in the future, the number of humans in the galaxy is in the trillions, the macro-future can be calculated, even if the micro-future can’t be known with certainty.

One of the fascinating (and intellectually honest) things about the Foundation Trilogy is how the Seldon Plan is undone. A mutant arises, one who can telepathically change people’s minds. He quickly rises to power, and very nearly destroys the entire basis for inter-stellar civilization. The reason Psychohistory could not be used to see his rise to power coming is because he was a mutant, and such mutations were outside the model that Psychohistory used.

Certainly one of the drivers behind this bigger-is-easier-to-predict bizarro-world narrative lies with the notions of the statisticians, who attempt to use their data to calculate the odds of future events actually occurring. Small sample sizes, you see, are given to large variances. The larger the sample size, generally speaking, the more it assumes the familiar shape of the Bell Curve, should it be graphically represented. So, if some statistician were to, say, use the tools of statistics and probability to predict the outcome of this November’s presidential election, it just won’t do to ask a few dozen people at the campus of the University of California, Berkley, whom they would vote for (actually, the University of California anywhere). Our statistician would simply have to throw in an occasional meat-eating church-goer, if he wanted an accurate prediction.

But even with a good sample, there are (at least) two major problems with predicting the future based on data that captures past behaviors: the limiting effects of mathematical models, and network theory.

Notice how Psychohistory was undone because of an event that occurred that was outside of its model. Any attempt to limit possible behaviors, strategies, or tactics from any given population is extremely vulnerable to occurrences of the highly improbable, as Nassim Taleb describes brilliantly in his book The Black Swan, the Impact of the HIGHLY IMPROBABLE (Random House, 2007). But what about the notion that the sheer size of the sample somehow mitigates the impact of highly improbable (i.e., ”outliers”) events? This butts up against network theory, and Metcalfe’s Law.

As I insightfully discuss in my recently-released, must-have book Game Theory In Management, Metcalfe’s Law shows that while, say, two telephones have one connection between them, six phones have 15. In other words, the larger the network, the more powerful it is. Ironically, the larger networks become, the more susceptible they are to cascading events, also known as the Butterfly Effect, stated colloquially as “if a butterfly flaps its wings in Brazil, does that cause a hurricane in Texas?” Put another way, seemingly small variations in few (or singular) nodes in a large network can have catastrophic effects on a large number of nodes that are nominally far away, or non-intuitively connected. In other words, the larger the sample size (or network), the more difficult it is to predict its behavior, even regarding cataclysmic events.

Essentially, the only way the risk management aficionados can assert any calculated insight into the unfolding of future events is if they (conveniently) ignore both Black Swan theory and Metcalf’s Law, and hope that no one in their epistemological snake-oil audience knows about them.

Or have read this blog.

The title of this piece is a phrase turned by Nassim Taleb, in his best-selling book The Black Swan, The Impact of the HIGHLY IMPROBABLE (Random House, 2007). He discusses several such pathologies, but his strongest (in my opinion) arguments and condemnations are saved for managers and analysts who rely on the fields of probability and statistics. As I relay in my recently-released, must-have book, Game Theory in Management, Taleb goes so far as to say (on page 355 of the paperback version) “This proves that everything relying on ‘standard derivative,’ ‘variance,’ ‘least square derivation,’ etc. is bogus.”

Proceeding from his very well-argued points, I would like to draw the conclusion that virtually all of what passes for modern Risk Management theory is invalid, and ought to be abandoned.

Gantthead wanted its bloggers to take on Olympic-sized issues in July, and Risk Management is pretty darn huge. A Google search of “Risk Management Consultants” returned over 90M hits on July 14, 2012. Risk Management is one of nine chapters in PMI®’s hallowed Guide to the Project Management Body of Knowledge®. There’s even an ISO Standard for it.

To be clear, risk management does have a place in project management, but it’s much smaller than advertised.  Analysis methods Decision Tree and Monte Carlo simulation can provide a reasonable estimating parameter for calculating how much in funds reserve a given project should have, or identify appropriate targets for insuring risks. Past that, RM’s claims are pretty much overblown, and the axiomatic “80% confidence interval” is right out.

Consider the Drake Equation, often invoked to defend (and attract funds for) that fool’s errand, the Search for Extra-Terrestrial Life, or SETI. The formula looks like this:

N = N * fp ne fl fi fc fL

Where N is the number of stars in the Milky Way galaxy; fp is the fraction with planets; ne is the number of planets per star capable of supporting life; fl is the fraction of planets where life evolves; fi is the fraction where intelligent life evolves; fc is the fraction that communicates; and fL is the fraction of the plant’s life during which the communicating civilizations live. As Michael Crichton pointed out in his lecture “Aliens Cause Global Warming” (Caltech Michelin Lecture, January 17, 2003) nobody has any idea what any of these parameters might be. Even the first one – the number of stars in the Milky Way – could be anywhere in between 10 billion and 40 billion. For truly experienced risk managers, that’s a range of thirty billion, and that’s the parameter we have the best shot at knowing! The Drake Equation is nothing more than an invitation to speculate, placed in pseudo-scientific terms. As Crichton himself put it, “An equation that could mean anything means nothing.”

Now consider the formula for calculating a contingency fund using a single-tier Decision-Tree analysis:

Cn = BAC – [(E1$ * E1%) + (E2$ * E2%) + (En$ * En%)]

Where Cn is the contingency budget, BAC is the budget at completion, E1$ is the cost of possible event one occurring, E1% is the odds of event one occurring, all the way through event n, which is the last possible event included in the analysis. Where do the existence of (and cost/schedule estimates for) these “events” come from? Usually a risk analyst working with a subject matter expert, or the owner of the particular cost account or work package being analyzed. Do I have to say it? The Decision-Tree analysis, like the Drake Equation, is simply an invitation to speculate, with such speculations almost certainly being chocked-full of the analysts’ cognitive biases.

But you’ll never hear risk managers admitting as such. They love asserting that they can, to a certain degree, quantify the future events in a given project. And, of course, knowledge of the future is pure management gold, making any plausible claim of being able to capture such information extremely attractive. But, just as medieval alchemist tried to combine common materials with lead to create literal gold, risk managers seek to combine common information streams into a reliable narrative of how the future of a project should be expected to unfold. Of course, they can’t possibly, and no amount of statistical jargon can change that fact.

One last little stinger example: after reviewing the techniques in Project and Program RISK MANAGEMENT, A Guide to Managing Project Risks and Opportunities (PMI Publishing, 1976), I estimate that there is a 72% chance that the majority of the techniques in Project and Program RISK MANAGEMENT, A Guide to Managing Project Risks and Opportunities (PMI Publishing, 1976), will be considered as laughably obsolete as medieval alchemy within 45.2 years, with an 80% confidence interval. How did I come up with my variables (such as, how does one evaluate the comparable laughability of misguided medieval pseudo-scientific pursuits)?

Trust me – I’m an expert.