Game Theory in Management

In an article from Readers’ Digest Treasury for Young Readers, you are shown how to construct an Hexapawn robot. Hexapawn is a game played on a nine-square board with, as one might expect, six chess pawns. The pawns move as they do in chess, and start on rows 1 and 3. The object of the game is to advance a pawn to the last row, capture all of your opponent’s pawns, or else put him in a position where he cannot move. The robot part of it has to do with twenty-four matchboxes, some maps, and colored beads. Little maps of every possible position are drawn up and placed on the tops of the matchbooks. Colored arrows indicate each possible move from that position, and corresponding colored beads are placed in the matchboxes. You then “teach” the robot to play by playing game after game of Hexapawn, and removing the colored bead from the appropriate matchbox that corresponds to the last move of all losing games. After about eleven or so games, the robot becomes perfect, and cannot be beat.

Before I go on to challenge outright the tons (literally) of research and writing that have gone into modern quantitative analysis in business, I want to discuss another game: the Ultimatum Game. This game has the game manager approach two subjects, and makes the following offer: to give them $100 (USD) on the condition that Subject B agrees to the first plan that Subject A articulates to split the money. If Subject B does not agree to Subject A’s plan, then neither person receives anything.

Game theorists attempting to determine Subject A’s best strategy for maximizing their payout calculated that the best proffered plan would be for A to receive $99, and B to receive $1, on the theory that B would rather receive $1 than nothing at all. But a funny thing happened to Subject A as he was preparing to deposit his $99: that plan was almost always rejected in actual experiments of the Ultimatum Game. There were actually instances where a 50/50 split, or even splits where Subject B received more than Subject A, were rejected. After having reviewed the data from the experiments, game theorists tended to chalk up the dramatic differences between their theoretical expectations and real-world results as owing to “cultural” factors, or else Subjects B not acting in a rational manner. Nothing could be further from the truth.

Consider the calculated/expected outcome’s implications. If a stranger approached you and, say, a friend with whom you just happened to be walking down a sidewalk, and presented the Ultimatum Game’s rules, and your friend offered up the 99 – to – 1 split, does that not imply that your friend was 99 times more worthy of unearned largesse than you? And – the value of a single dollar bill being what it is – wouldn’t it be worth it to forgo the $1 in order to reject the implication? We haven’t even touched on Subject B’s willingness to punish Subject A for being greedy, or arrogant, or dozens of other reasons why the experimental data was so at odds with the theoretical projections.

Which brings us to the problems with quantitative analysis in business as it is currently taught in the nation’s universities. The free marketplace is an extremely complex environment (it may even qualify as chaotic – there’s really no way of knowing). And yet, the most basic analysis tactics put forth in the current literature treat it as if it’s relatively simple, and can be captured mathematically. For example, the decision on whether or not you should close your business when it is losing money is supposed to be predicated on whether or not your revenues exceed your fixed costs, rather than just your total costs. Umm …yeah, what if next week you are to learn of the award of a contract that you bid, where you estimate a 50% chance of winning, and that work would put you back into the black, and in a big way? Or of three such proposals? Of course, the kind of information that your general ledger can offer up can’t possibly capture that, and is, really, comically incapable of making available the definitive quantitative analysis that would support that decision, one way or the other. The asset managers are simply turning to their version of the Hexapawn robot, and retrieving the colored bead that tells them what to do, not realizing that the game they are playing is no where near confined to a nine-square board. And, when their so-called quantitative analysis is proven wrong, they can simply deflect blame on to cultural factors, or players acting irrationally. Hey, guys – it’s the free marketplace! Nobody acts in a way that you can predict, or calculate – in other words, the world is, by your definition, irrational, and will always be that way.

Must I say it? The notion that the general ledger can possibly inform the decision of whether or not to stay in business is pseudo-intellectualism of Cecil B. DeMill proportions (I’m actually hoping that Cameron uses this quote in his teaser on the web site). And that is business intelligence’s  fatal flaw – the arrogant premises from which the quants proceed.

There is a legend that, in 1815, in the immediate aftermath of the Battle of Waterloo, Nathan Rothschild had learned of its outcome via carrier pigeon. But it’s what he did with that intelligence that fascinates: the morning after the battle, Rothschild attended to his usual station at the London Stock Exchange, looking anxious. He immediately began selling off his English holdings, assets that were sound and provided returns only as long as the government that backed them was solid. Instantly the rumor spread, “Wellington lost! Rothschild knows!”, and a sell-off commenced. Once these futures had plummeted in price, Rothschild had his agents quickly buy them back, at dramatically reduced rates. He made a killing that day.

Again, note his tactic – he didn’t use his advanced intelligence to buy up English futures, which were sure to increase in value once the Brits knew that Napoleon was no longer a threat. Instead, he behaved as if the English futures were worthless, inspiring panic in those who were watching him for advanced intel. It was their reaction which led to Rothschild’s ability to make far, far more than had he acted on his intelligence directly.

I said that this is a legend, and more than a few historians have rejected the story in its entirety. But it does go to illustrate not only advanced use of business intelligence, but out-and-out manipulation of the same. Rothschild broke no laws, and would not have even if he pulled this stunt in today’s far more stringent business law environment.

Such may or may not have been the case with Enron’s behavior during the California Energy Crisis of 2000 – 2001. As I discussed in my must-have book, Enron’s advanced use of business intelligence, coupled with their ability to manipulate the same, led to eye-popping profits in a relatively short period of time. Much of the analysis of the crisis places the blame for the difficulties solely on the malfeasance of Enron, but I tend to disagree with those analysts. I believe the clumsy attempts from the California legislature to pass laws to control the way the power companies operated within the state provided the perfect environment for organizations that knew how to handle business intelligence, as well as manipulate it, to thrive. The laws were byzantine in nature, but Enron quickly found formulaic approaches to maximize their profits within that regulatory environment. These formulaic approaches, or “manipulation strategies” as the Californians liked to call them, were essentially cartage schemes that could be invoked almost instantaneously whenever a certain set of parameters – current load, power line availability, prices on the out-of-state market – manifest. And then, well, it was just a matter of watching the profit meter spin at dizzying speeds. Yes, Enron did play fast and loose with the untimely shut-down of plants for “maintenance,” or the priority renting/leasing of major power lines. But I still blame Sacramento for the fiasco – they set up a business intelligence game, and simply couldn’t play at the same level as their private sector counterparts.

The common thread in these two stories is that a certain level of deception was involved. I would venture to say that in virtually every instance of an advantage in business intelligence leading to dramatic managerial success, the element of deception is present. Now, I understand that that makes many people uncomfortable, but such ones need to understand that the business intelligence component of management science more resembles the game of poker than it does chess. In chess, all of the pieces are visible to both players, as well as their possible moves. There is no deception in chess. Ah, but in poker, there’s deception all over the place. The poker player who bids high on good hands and low (or even folds) on all bad hands will not win very much money at the game. There simply have to be times where he bids up losing hands (“bluffing”), or bids down winning hands for the express purpose of preventing his opponents from realizing and then acting on a known pattern of bidding behavior, and taking advantage of it.

Science, on the other hand, can brook no deceit. By definition, science seeks to test hypotheses in an empirical setting in order to discover truth. In those instances where any form of deception does enter in to the evaluation of a theory, it renders the science fraudulent, as in the behavior of University of East Anglia Climactic Research Unit in its support of the anthropomorphic global warming hypothesis.

Since the pursuit of advanced business intelligence contains an element of deceit, it more closely resembles a game than it does management science, and should be approached on that basis. If only someone out there had written a book that deals with game theory in management – oh, wait, I did.

This story appears in a beloved book I read as a child, Reader’s Digest Treasury for Young Readers:

Yet even Dr. Bell (the person after whom Arthur Conan Doyle modeled Sherlock Holmes) sometimes made mistakes. Luckily, he also had a sense of humor when people asked him to give examples of his skill as a detective, he liked to tell this story: One day he and his pupils were examining a patient in a hospital bed. “Aren’t you a musician?” Dr. Bell asked him.

“Aye,” admitted the sick man.

“You see, gentlemen, it is quite simple. This man has a disease of the cheek muscles, from too much blowing on wind instruments. We need only ask him, and he will admit it. What musical instrument do you play, my man?”

The man got up on his elbows. “The big drum, doctor!”

I like to remember this story whenever I explore the epistemology of management information streams, since it is so easy to equate perceived project success with factors that may have been entirely incidental to the remembered project’s actual success. I once was working to set up the cost and schedule performance systems on a major project that was headed by a manager who insisted that my team provide a report he termed a “swim lane chart.” What he actually wanted was a PERT chart, sorted by performing organization. I could see the utility – the various performing teams were in a column to the left, and the activity boxes that appeared to their right represented the scope for which they were responsible.

“Okay” I offered, “we can do it, but we’ll need to start with a Work Breakdown Structure. Then, we’ll need the Organizational Breakdown Structure, so that we can cross-reference them into a Responsibility/Accountability Matrix, or RAM. Once we have that, we can load the information into the critical path software, and generate your report.”

“I don’t want to do any of that stuff. I just want a swim lane chart.”

“I know you want the chart, but we can’t get there without the RAM.”

So he had me removed from the project.

This guy just knew that his so-called swim lane chart was the key to managing the project to a successful outcome, and wasn’t going to listen to any of that project controls nonsense about how to get there.

I’m sure my readers have many similar stories. Some manager or exec has a particular project management artifact that serves as their security blanket, and will brook no challenge to its efficacy. Their attachments to these talismans can reach a zeal rarely seen outside of religious institutions or sports bars. And, when these superfluous information streams become institutionalized, the amount of managerial folly they generate can become extraordinary. Just look at the number of U.S. Government agencies who insist that projects have a risk management system.

It doesn’t stop with the risk management crowd, either – they’re just the more irksome of the bunch. Virtually every attempt at quantitative analysis in business or management requires the use of some subjective variable, variables that really cannot be known to within the boundary triggers for making decisions. For example, in comparing the Return on Investment (ROI) among competing prospective projects, the anticipated rate of return can only rarely be estimated to within double-digit accuracies. However, the decision on which projects to pursue are almost always made by single-digit margins. It’s as if the organization is making a decision to select a subcontractor based on the estimation of who has the fewer Capricorns on staff, and attacks anyone who doesn’t acknowledge that as an appropriate basis for making the decision as hopelessly ignorant.

Of course, there are ways of objectively determining the validity (or lack thereof) of the competing management information streams, but how to do so would take an entire book, which, fortunately, I happened to write.

I remember an English class I took when I was doing my undergraduate work, where the professor quipped that there is no record of a written language prior to around 3600 B.C., but at around that time there was a veritable explosion of languages recorded in various media.

“So, theoretically, the story of the Tower of Babel could be true” I suggested.

He chortled condescendingly as only professors of the humanities from state universities can, but essentially agreed with my point.

Some years later I was watching a documentary on ancient Babylon, where some archaeologists had unearthed a rudimentary battery, and dated it to around 3500 B.C.  The analyst being interviewed speculated that, had mankind pursued that technology back then, we would today be hopping galaxies in starships. Of course, he had no way of knowing that, but the speculation was fascinating. Could it have been another hint that the story of the Tower of Babel was not completely allegorical?

For those who are not familiar with Genesis 11, all of the people had moved to the valley of Shinar, and decided to launch a very big project, indeed. They were going to build a city for themselves that included at least one tower that would reach to the heavens. God came down, saw it, and said “If as one people speaking the same language they have begun to do this, then nothing they plan to do will be impossible for them. Come, let us go down and confuse their language so they will not understand each other.” (NIV). Well, that did the trick: they stopped building the city, and scattered themselves all over the Earth.

What we have here is an example of the Almighty derailing a project. Of course, we humans are pretty good at doing the same thing, but I think it’s instructive to observe how He did it. He confused their language. Why would this spell automatic doom for the Tower project? While the designers, engineers, and laborers no doubt spent a lot of time using their common language to make crude jokes and compare the statistics of ancient sports teams, it’s obvious that critical information could no longer be understood by the project team, making coordinated work or progress against desired scope impossible.

Don’t misunderstand – I do not agree with the so-called communications experts who insist that, if we could just perfect our methods of communication, the management world would be all glitter and unicorns. But I do believe that, should pathologies in the business model creep into the performing organization, the earliest place it will manifest will be the avenues of communication.

I define “office politics” as those instances where members of the organization behave in a manner that is inconsistent with (or even contrary to) the stated goals of that organization, but benefit them personally. Since that definition would also include theft, let me hone it down a little further: they do so by manipulating the information streams on which the organization depends.

Take the favorite tactic of the Maccoby archetypical Jungle Fighter: they magnify their rivals’ (virtually everybody around them) failings, while minimizing their own. They amplify their accomplishments, while trivializing their rivals’. If the organization does not depend on the information shared informally among team members, this is fairly insignificant . However, if this information stream is acted upon by management, then the poorest decision-makers advance at the expense of the truly talented members of the project team.

Consider also those who tamper directly with the management information stream. Our friends, the accountants, and risk managers leap directly to mind. By vastly overstating their techniques’ efficacy, and advancing them, they elbow aside more legitimate sources of valid management information, significantly increasing the chances that poor management decisions will be made.

Relevant, timely, and accurate information is the life-blood of any organization, and its reliable conveyance is often easily manipulated by the project-killers in your ranks. And such ones will rarely self-identify by wearing hockey goalies’ masks.

As I’m sure most (if not all) of my regular readers know, many psychologists believe that, when we dream, we are experiencing our subconscious selves attempting to communicate with our conscious selves, but in a visual language which is difficult to understand in our waking existence. The images can be primordial, powerful in connotation, but irrational in delivery. And, when we experience recurring dreams – or, painfully enough, recurring nightmares – there’s a natural tendency to try to unravel the message and, potentially, act on it.

So, what are the most common project management nightmares? Well, clearly, there’s the over-budget, late-schedule, but not knowing about it until it’s too late one. Then there’s the customer-pushing-extra-scope variety, leading to the most common project performance killer, the dreaded scope creep. There are many others, and yet I must ask: what are these nightmares trying to tell us?

Consider our information streams. They are the real-world data feeds, what we see on the surface, and the information that leads us to act, to make decisions. As managers, the information we seek is analogous to what we see, hear, smell, and touch in our waking existences. When we seek irrelevant information, and get it, and then make decisions based on it, then the nightmares start to become realities. Why? Because there’s a project management reality out there, that’s not being perceived, and threatens to become an inescapable outcome … and management is oblivious to the imminent disaster looming. The information streams an organization chooses is a clear indicator of its true character, as well as its chances for success.

Take the organization that eschews earned value analysis. This is most often the case where our friends, the accountants, have successfully pushed the fraudulent idea that the general ledger can provide all relevant cost information to decision makers. But there are many instances where the organization elects to nix any EV system because they simply do not want to know how their projects are performing in cost performance space. Why would any organization not  want to know this? Because it’s directly tied to responsibility. Organizations that seek to obscure who or what was responsible for successes or failures, and, instead, substitute narratives bereft of facts, cannot abide even the most basic of earned value management systems. Organizations seeking to deflect responsibility will resist information streams that accurately reflect cost and schedule performance, and will repeatedly encounter the way-over-cost, way-past-schedule nightmare.

Risk management is another conceit, that the real world of project performance will attempt to penetrate with frightening feedback. Bad things happen on projects – it’s a fact of management life. How the project team responds is the difference between success and failure. So, what if a risk analyst predicted that a given bad thing might hit the project? Did it alter the team’s response at all? If yes, then, yeah, it was a good idea to spend that money on the risk analyst. However, in my experience, the risk people rarely – if ever – proscribe a course of action that wasn’t already on the project team’s roster. In other words, they accurately worried for you, Mr. /Ms. PM. And it bought you exactly nothing. The anti-PM nightmare talisman that you bought from the risk management types is literally worthless.  Sorry.

And what about those who attempt to “manage” their schedule via milestone lists? These invariably list project objectives (again, don’t tell me, right? In a spreadsheet?), and pull “status” by asking their principals if they anticipate that they will accomplish their milestones on-time, a little late, or very late/miss altogether. The answer is, invariably, that they will certainly meet their schedule dates … until they don’t. This is also reflective of an organization that has no intention of respecting responsibility for project performance and is, instead, situating itself for convenient scapegoating.

Are these a few of your PM recurring nightmares? The interpretation lies within identifying the business model pathologies that have wormed their way into your organization. And that exercise, dear readers, is never without pain.