Game Theory in Management

Sherlock Holmes was famous for being oblivious to the purely cultural goings-on in late 19^th Century London. Whenever Watson would express dismay at Holmes being unaware of some (then-) popular trend or occurrence, Holmes would explain his tendency to avoid committing to memory any fact that had no relevance to his capability of solving mysteries, or investigating crimes. His mental acuity, he would explain, would be diminished if he were to expend energy on keeping up with trendy social goings-on. If it wasn’t relevant to his primary purposes, Holmes wanted nothing to do with it.

Meanwhile, Back In The Project Management World…

Seasoned members of GTIM Nation are well aware of my conditions for usable management information, that it be:

• Accurate, 
• Timely, but, most of all,
• Relevant!

In a way, the first two of these point to the third. Inaccurate data is not only irrelevant, but also potentially debilitating to the formulation of any usable management strategy derived from it. And, as realized information ages from actionable to historical, it clearly loses its relevance. So, much as the realtors’ axiom, that real estate is all about “location, location, location” points to location being the primary determiner of its worth, the value of Project Management information basically boils down to its relevance. This is one of the reasons I’m so put off by the risk management (no initial caps) industry. For all of their ballyhooed techniques and overwrought approaches, the product they deliver is almost always irrelevant, little more than garden variety management anxiety tripped out in Gaussian Curve jargon.

Imagine a scale, with completely irrelevant information streams on one end, and information that’s so accurate, timely, and relevant that possession of it constitutes such a competitive advantage as to almost guarantee success. I would also like to put to mind Hatfield’s Incontrovertible Rule of Management #3, which reads:

The 80^th percentile best managers who have access to only 20% of the information needed to obviate a given decision will be consistently out-performed by the 20^th percentile worst managers who have access to 80% of the information so needed.

It should go without saying, but I’ll say it anyway: irrelevant information does nothing to help obviate any decision. In fact, it may well either distract from the relevancies, or even point in the wrong direction.  

Timely, accurate, and relevant information has been known to change the course of history. At the Battle of Midway (early June, 1942), the American naval forces were outnumbered, with technically inferior aircraft (the torpedo bomber in front-line use at the time, the Douglas Devastator, was virtually obsolete) and less experienced crews. The sole advantage that the Americans had over the Japanese attacking fleet was their information. The US Navy knew beforehand virtually the entire Japanese order of battle, due to a partial breaking of their naval code. Yet this one advantage proved to be the deciding factor in the Allied victory. Now, I have used this example in previous blogs, contrasting the difference between knowing, say, the course and speed of the Japanese aircraft carriers in late May/early June 1942, as opposed to how many barnacles were attached to their hulls, to highlight the difference between pertinent and pointless information. This comparison was, perhaps, unfairly simplistic, since a barnacle-adjacent piece of data, like the course and speed of said barnacles, would be highly relevant, indeed.

So where does, say, general ledger information appear on my scale? That depends on how much the organization’s business model is based on the Asset Managers’ (invalid) axiom, that the point of all management is to “maximize shareholder wealth.” More nuanced and sophisticated business models, ones that recognize the value of PM-specific information streams (e.g., cost and schedule performance) in guiding executive decisions, will certainly make use of accounting data, but won’t base every decision on profit-and-loss considerations. Even middling portfolio management capability can’t be attained without program-wide use of Earned Value, which is (generally speaking) exclusively within the PMO’s purview. Given these parameters, I’m okay with placing balance sheets and profit-and-loss statements on the “Highly Relevant” side of my scale; but, if Earned Value and/or Critical Path Methodologies are absent from the array of Management Information Systems (MISs), then something is definitely wrong.

On the “Acutely Lacking In Relevance” part of my scale, I would place the Communications Specialists (maybe not on the extreme end, but definitely that side of the mid-point), particularly the ones who espouse the “engage all stakeholders” business. Depending on the types of scope in the Project portfolio, the Quality Management crowd’s output is probably best placed somewhere in the middle of my scale, since I have yet to see a PM seize upon an Ishikawa diagram and rush out to the construction site, shop floor, or Agile/Scrum meeting to announce a major change in technical approach.

As for risk management’s (no initial caps) place on my scale, I’m with Sherlock Holmes here – I wouldn’t want anything impertinent influencing my analysis or decisions. One would be better served deriving a business strategy from Taylor Swift lyrics.

As I have oft noted before in this blog, Dave Christensen’s work[i] on Cost Performance Index (CPI) stability led to a fascinating (well, to me, at least) conversation (debate?) about whether or not the Estimate at Completion (EAC) formula based on the CPI, namely

EAC = BAC / CPI

…could be considered reliably able to produce an EAC that was within ten points of the actual at-completion costs of a project, since the study fairly established that the CPI doesn’t vary more than ten points once the Project has passed a certain percentage complete (typically pinned at 18%, practically at 20 – 30%). The reason that I find this wonkish discussion so fascinating has to do with the way one calculates CPI. It’s simply the Budgeted Cost of Work Performed (BCWP) divided by the Actual Cost of Work Performed (ACWP). What’s BCWP? That’s an estimate of the Project’s cumulative percent complete multiplied by its Budget at Completion. Everyone seeing the pattern here? The whole shebang, which, recall, is (arguably) accurate to within ten points, is

EAC = ACWP / % Complete

…, believe it or not. Yes, arguably the most important bit of information that a PM-centric Information System can produce is available using only two easily-captured parameters. Crazy, huh? But wait: it gets better.

The same formula works for duration! Want a reliable estimate of when something’s going to be done? Divide the cumulative duration by the same percent complete figure, and you have the at-completion duration estimate.

The easy uses of this old Project Controllers’ hack are everywhere. On a long trip, and sick of hearing the kids ask “when will we get there?” Simply tell them your percent complete (based on remaining miles / travelled miles [oooh! Don’t tell them the percentage – give them the remaining miles and travelled miles figures, and let them calculate it!]), and time your trip began, and they will have their total duration. Subtracted from cumulative duration, and you have your arrival time, within ten points.

I live fairly close to a large park, and City Government has been promising an indoor swimming pool facility for over 20 years. The actual Pool Project completion data was always around seven years into the future. But, based on the above formula, I’ve known since the first days of those promises that it would never happen within the time frame promised, and, therefore, was spared the frustration of having my hopes dashed.

Did your significant other talk you into watching a movie you would not have otherwise seen? And is said movie having you wonder how much longer you will be subjected to either grotesquely overdone CGI or incredibly predictable romantic-comedy dialogue? Note when the actual movie started, and use the following table:

Like real-life Projects, you should never attempt to claim more than 90% complete until you are actually finished with the project. Resist the temptation, both in the tedious theater setting and in your percent complete estimate to your Project Controller, to add points in very small increments to give the illusion of making progress when things are really at a standstill. It’s best for both your Project’s Management Information System integrity, and your mental health. Divide the time when you noticed these occurrences in the movie into the difference between time now and when the movie actually started, and you will know the approximate duration of your ordeal.

I’ve actually seen some Earned Value training materials that use this approach to calculating when you can expect to finish painting a room (or rooms), and it’s probably fairly reliable to do so. However, DO NOT use this formula for plumbing, unless you are a professional. There’s just something about amateurs attempting to do plumbing – even if it’s just replacing a toilet float – that carries with it multiple unexpected additional jobs, or tool acquisitions. Also, this approach can be expected to lose all efficacy if applied to Projects that include children. Are kids cute? Sure. Lovable? Absolutely. Uniquely gifted with the ability to completely wreck any management science-based approach to assessing performance in general, and at-completion costs or durations in particular?

Every single day.

[i] Christensen, David & Payne, Kirk. (1991). Cost Performance Index Stability: Fact or Fiction?. Vol. Proceedings of the 1991 Acquisition Research Symposium. 12. 10.1080/10157891.1992.10462509.

I once worked with a Vice President who had a Ph.D. in statistics. On this one occasion we were attending a PM workshop of some kind, and, at the end of the day, were in the hotel/conference center’s lounge, having adult beverages and comparing notes on the paper presentations we had attended. This fellow – I’ll call him “Dave,” because that was his name – told me about his dissertation and its subsequent defense. He had developed it in the 1970s, well before personal computers were available, and mainframe computers were just being introduced to college campuses. Dave had taken a couple of computer courses, but was by no means a competent programmer. He did, however, know how to use the text editor program that the real programmers used, and how to send output to a dot-matrix printer, the kind with a tractor feed and green-and-white shaded paper. Since Dave didn’t have access to a working typewriter, he simply typed out his dissertation on the text editor attached to his college’s mainframe, and printed out the results on the dot-matrix printer.

When Dave presented his text to his faculty sponsor, the professor was awe-struck.

“You wrote your thesis on a computer!?” the professor asked.

“Yeah, I made use of the mainframe in the computer science department.”

Dave confided in me that his review committee never asked for any corrections or comment resolution. The paper sailed right through the review, and the defense was unexpectedly light on questions. It was rather plain that the aura surrounding computers of that time – fueled, no doubt, by the HAL 9000 computer from 2001: A Space Odyssey, known for having “never made a mistake, or distorted information” – had so cowed the review committee that they assumed that the resulting text was flawless.

I was reminded of this story when I saw a paper on an OpenAI project to teach a machine how to play a virtual version of hide-and-seek.[i] Once the “room” and “teams” had been set up, several million game iterations were spent rather chaotically, with iterations 0 – 2.69 million spent on seekers “learning” to chase hiders. Episodes 2.69 to 8.62 million saw the hiders using two virtual cubes to block off the two doors in the play area, and 8.62 million to 14.5 million episodes needed for the seekers to utilize an available ramp. Episodes 14.5 million to 43.4 million saw the hiders learn to stow the ramp prior to blocking the doors, rendering them inaccessible to the seekers.[ii] The AI went on to develop surprising behaviors from both the hiders and the seekers, but the above-referenced progress will do for this blog.

Now consider what would happen if a real-life version of the game environment had been created, and the two hiders and two seekers were fourth-graders (typically around ten years old). Let’s further posit that an average game of hide-and-seek would last around two minutes in such a basic setup. Even if we had fourth graders who could play this game non-stop, it would take them over 165 years to perform this number of “episodes.” I’m fairly confident that four typical fourth-graders could discover the door-blocking and ramp-utilization strategies within one day, or even one hour.

So, why is everyone so in awe of artificial intelligence? Have people in general, like Dave’s thesis review committee, become so over-impressed with the implications of AI’s capabilities that any idea that even has the trappings of being associated with it is given deference with respect to its validity?

In my book Game Theory In Management[iii], one of the “games” I evaluated is known as the Ultimatum Game. In this game, the researcher approaches two random people and informs them that he will give them $100 (USD) if Player A can propose its distribution among the two of them, and have Player B accept those terms on the first iteration. Game Theorists (not me) had “calculated” how to maximize Player A’s payoff: by proposing $99 for Player A, and $1 for Player B. The thinking was that Player B would be presented with the choice of receiving $1, or nothing at all, and would always agree with Player A’s distribution scheme.

A funny thing happened on the way to the actual distribution of the $100, however. This strategy almost never worked when tried in real-life. Perhaps put off by such an unfair distribution of unearned largess, or by other factors, the 99 – 1 strategy was almost always rejected. When confronted with the real-life results indicating a broad-based refutation of the Game Theorists’ calculated optimal strategy, many of them blamed “cultural influences” for the discrepancy. But, by pointing to something as inchoate as “cultural influences,” these Game Theorists were essentially admitting that attempting to calculate a specific strategy, even within the confines of something as basic as the Ultimatum Game, was next to impossible due to the number of contributing factors that couldn’t possibly be recognized, let alone quantified.

And so it is, I believe, with much of what presents itself as artificial intelligence. Sure, it’s fun to see how AI can generate graphic images, or even text (I won’t say literature, at least not yet), but when it comes to creating usable strategies in the Project Management world? Its proper response ought to be “I’m sorry, Dave. I’m afraid I can’t do that.”

[i] Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch, “Emergent Tool Use From Multi-Agent Autocurricula,” 17 September 2019, retrieved from https://arxiv.org/abs/1909.07528 on August 12, 2024, 19:45 MDT.

[ii] Ibid.

[iii] Hatfield, Michael, Game Theory In Management, Gower Publishing, 2012.

At a presentation I attended as a newly-minted staff member of a now-defunct major beltway bandit, the Vice President on the stage put up a chart I will never forget. It was a line graph, with the line starting high on the Y axis with a slight downward slope, until it got to about midway on the X axis, where it fell to below dramatically. This line, he explained, was the time-phased budget across all active projects, with the fall-off point at around the six month point on the X axis. What it was showing was a rather unsettling truth: if the corporation were to stop bringing in new projects tomorrow, most of us would be getting laid off in around six months.

With a brand-new wife and first mortgage, I was highly disinclined to be laid off for want of new work, so this VP had my full attention. In order to avoid this fate, he continued, it was incumbent on every single member of the staff to put considerable effort into proposing and winning new projects. Oh, and since I was now salaried, this extra effort was expected to be offered freely, nights and weekends. This last part wasn’t articulated aloud – it didn’t have to be. The corporate culture was so focused on winning new project work that technical or administrative excellence became almost an afterthought. In my view, there were essentially two classes of personnel in this organization: Group A were adept at writing winning proposals, and were golden; Group B were second-class citizens, whose employment could come to an end at any time, depending on how they were perceived by members of Group A.

Predictably enough, the corporation thrived, but its employees (especially those in Group B) were rather anxious, generally speaking. But this dichotomy did present an interesting take on the optimal business model in a heavily Project-driven organization. Consider the following payoff grid:

Let’s deal with Scenarios A2 and B1 right away. If the Project comes in on-time, on-budget (or even early and under-budget), AND attracts more work, either from the existing customer in terms of add-on tasks to the existing contract, or even a new contract, then there’s no question such a PM would be considered a success. Similarly, if the PM crashes and burns in actual contract performance, and the customer doesn’t want anything more to do with them, then that PM would be largely considered a failure. Where this payoff grid gets interesting is in the other two Scenarios.

I have often maintained that the whole point of developing a more robust PM capability is to increase the chances that the Project comes in on-time, on-budget, but the above payoff grid challenges that notion. For if, as in Scenario A1, the Project is actually completed successfully, but there’s no follow-on work, either from that specific customer or another within the same industry, what happens then? Well, the members of the Project Team would have to move to other work within the organization, charge their time to an overhead account for a (presumably) limited amount of time, or … get laid off.

Now consider Scenario B2, where the Project comes in late, overrun (maybe both), but, somehow, the PM secures more work in the same or an adjacent field. If the new work has a similar Budget at Completion (BAC) to the just-failed Project, then nobody has to be let go, the Project Team has a shot at getting it right this time, and the corporate higher-ups are happy. My previous definition of a “successful” PM notwithstanding, how could the PM in Scenario B2 be considered a failure? I had a friend and associate who was an exceptional PM, but rarely brought his Projects in on-time, on-budget. How could such a one be considered exceptional, GTIM Nation may ask? It’s because he would be brought in to Projects that were already in deep trouble in cost/schedule space, and would find a way to complete them with only mild negative variances. Essentially, he had a real talent for turning portfolio-cratering disasters into annoyances, and, for this, I consider him one of the best PMs I’ve ever known.

While performing well in cost/schedule space certainly makes it easier to bring in more work over the long term (particularly from the same customer), the key takeaway from the above payoff grid appears to be that the ultimate metric of PM success is keeping the Project work coming in the company’s front door, essentially handing the Strategic Managers (who chase market share) the tools they need for success. Ah, but if that’s the case, I’m happy to let the Strategic Managers have at it, and the answer to the question in the title would be “no.” We PMs did our part by coming in on-time, on-budget. And, if I’m needed to help the Strategic Managers win more work, well, I don’t work for free, evenings and weekends.

Any more.

In last week’s blog, I started with a quote from Eric Hoffer, specifically “Every great cause begins as a movement, becomes a business, and eventually degenerates into a racket.” I went on to discuss how this quote could be paraphrased into something more management-science-y, as in all great businesses start with a grand idea, or discovery, or insight into making a superior business model, etc., then moves into a phase where our friends, the Asset Managers, monetize everything, and then on to a phase where much of the original fire has left the macro organization, and instead it performs like it’s just there to keep existing. Working from that foundation, I want to bring in some Project Management theory, specifically in regards to the question: what’s happening to our subject organization’s business model this whole time? Does its evolution have anything to do with that long-time nemesis of the PMO, the organization’s reluctance to change in general, or accept basic PM precepts in particular?

First, consider how informed decisions are made in medium-to-large organizations. Sometimes you’ll see utter geniuses (or those who think of themselves that way) making virtually all of their choices based on their reflexes, gut feelings, prior experiences, or some combination thereof. Mostly, though, upper-level management will depend on information, whether from direct observation, members of the staff preparing and presenting it, or computer-based information streams. Of that last category, it’s likely that the main component of the diverse Management Information stream is the General Ledger – after all, the point of “all” management is to “maximize shareholder wealth,” right? So, even if the organization’s founder(s) is still passionately pursuing the original vision, by the time the movement turns into a business, and everything’s getting monetized, then the General Ledger, almost definitionally, must be the main source of management information. This being the case, the Chief Financial Officer (CFO) is likely to attain near Oracle-at-Delphi status, being the source and residence of most of the relevant data needed to make informed decisions.

Now, imagine the poor person who’s been assigned the PMO Director position. Reduced to its very core, what is this person’s message? Isn’t it that, if given just a bit of budget and organizational leverage, she can deliver an information stream, outside of the General Ledger, that nevertheless informs decisions on the optimal use of resources in pursuit of accomplishing scope? Just to be clear, this is in stark contrast to the Asset Managers’ message, which can be reduced to “this is how we can make money in the performance of scope realization.” In other words, the central question driving the development of the business model is either “How do we optimize resource allocation to make our customers happy?” or else “How do we squeeze maximum profits from customers, happy or otherwise?”

It's not a trivial distinction. It is, in fact, the determiner of how the business model changes as the organization matures, either consciously or accidentally. For if the pursuit of scope becomes the priority, then those who had been previously approaching Oracle-at-Delphi status when it comes to delivering actionable management information will experience a reduction in their utility and, therefore, prestige and organizational standing. From the Asset Managers’ point of view, those PM-types delivering the occasional insight that helps make a better decision now and again is okay, but the primary basis of the really key choices involving actual currency should be predicated on Generally Accepted Accounting Principles (GAAP). To believe otherwise is to directly challenge their most basic precept, that the point of all management is to “maximize shareholder wealth.” It’s in the very nature of the changing business model to resist significant incursions from newly-established PMOs, particularly in portfolio-level cost and schedule performance analysis – probably the most valuable contribution that a PMO has to offer.

Keep in mind all of this pertains to the organization that’s moving from the “movement” phase into the “business” phase. In the management world, organizations only stay alive as long as they make a profit. By the time the organization is moving from “business” to “racket,” the monetize-everything approach has so dominated the business model that it’s becoming more and more unlikely that any attempt at returning to a customer-focused approach – the very essence of PM – will succeed. The business model has become so ossified as to approach near-granite density. The frustrated PMO Director may come away believing that his efforts have been thwarted by a widespread reluctance to change, but it may easily have been due to a hardened business model, made so by a predictable process, entirely consistent with theories being taught in business schools across the land to this day.

So, to the newly-hired PMO Director, I would say this: Does it feel like bringing changes to your organization is like drilling through granite? There’s a reason for that.