Game Theory in Management

Since the very definition of Project Management involves a unique or novel product or service, the appropriate level of demand for PM services is often highly variable, even in those organizations with projects making up a strong majority of their contract backlogs. This variability is in strong contrasts with the level of demand for our friends, the accountants, due to the fact that no company can function without paying taxes or meeting payroll, among the myriad other general ledger functions performed by them. The demand for general ledger expertise pretty much parallels the overall size of the organization in a given business sector, with its boundaries being not hard to ascertain. If the books don’t balance, or the profit-and-loss statement contains clear errors, or the taxman reviewing the organization’s returns demands an immediate audit, it’s safe to say the company needs more accounting talent, immediately. If the company’s overhead rates are so high that it’s no longer competitive, and none of the lower-boundary symptoms have manifested, it might be a good idea to cut back on the Accounting Department.

For we PM-Types the upper and lower boundaries are not as clear, and their placements are susceptible to the subjectivity that accompanies managerial ignorance and business model pathologies. Consider the following graph:

1. This is where most Project-oriented organizations begin, since they tend to hire the engineers, scientists, programmers, etc. who perform the actual project work, with or without guidance. This level of PM capability is color coded light red, to indicate that it’s not to be considered functional.
2. Step B occurs when a senior-level manager recognizes the need for a more formal, robust PM capability, a major project overrun has occurred, or (more often, in my experience) the organization is pursuing (or actually wins) a contract for work that stipulates, if not a more advanced PM maturity, at least the capability of producing the artifacts consistent with such an ability (e.g., Gantt Charts, Cost Performance Reports [Format 1], etc.). Demand for PM talent escalates and, as it is collected, the organization begins to improve its PM aptitude. This Step level is color coded yellow, to indicate a marginal functionality.
3. This Step indicates a middling-to-good level of competence, as indicated by the generation of useful, insightful information streams showing portfolio-wide cost and schedule performance in addition to meeting external PM-centric requirements. Another, critical indication of arriving in this zone: the number of Projects coming in late or overrunning drops precipitously. Great news, right? Maybe. Recall the business service axiom, Quality, Affordability, Availability – pick any two. This level of PM capability isn’t cheap, making it a likely target for non-PM-types, whenever a discussion of how to cut costs comes up. They will be aided even by some within the organization’s PM ranks, who eschew the basics of Critical Path and Earned Value Management, and will point to the absence of Project trouble as Exhibit A behind the argument that that level of PM expertise is overkill.
4. Don't let it be forgot
   That once there was a spot
   For one brief shining moment that was known
   As Camelot[i].
5. Now, as elements of this so-called advanced PM capability begin to include more expensive but nevertheless useless techniques (cough, risk management [no initial caps], cough), the more poorly-performing PMs will tire of having their lack of performance exposed by the system, and will exert considerable effort into opting out, leading to the inversion of the trajectory at Step E. Since the organization as a whole has been performing well, a few renegades can escape notice.
6. As the trickle of Projects opting out of the Project Management Information Systems becomes a torrent (Step F), the system maturity slope plummets towards highly uneven implementation, followed closely by…

1. We’re back at Step A, awaiting a major Project catastrophe.

The reason I referred to this cycle in the title as a roller coaster has to do with the timing and magnitude of the events and PMO responses. The figure shows a nice sine wave-style curve, but that’s only for illustration. In real life, the timing of the events and amplitude of the responses are highly variable, some Steps coming in bunches, others drawn out over quarters, or even years.

This is where the gig economy comes into play. While personnel from other fields can often count on a steady-state of demand for their services, we PM types enjoy no such consistency. Depending on where the organization is in the cycle’s Steps above, demand for PM talent can either be intensely high or depressingly low, the precise type of business cycle that makes offering talent in the field a rather excellent fit for a variable supply structure. Are there strategies available to the PMO to even out the extremes of this curve? Sure, and probably the best one is…

Whoa, look at that! Out of pixel ink for this week. Tune in next week for the thrilling conclusion to The Gig Economy Comes For The PM

[i] Retrieved from https://www.allmusicals.com/lyrics/camelot/finaleultimocamelotreprise.htm on August 14, 2022, 17:40 MDT.

Before GTIM Nation accuses me of indulging in PM fan-boy-ism, allow me to point out that the supporters of virtually every management strategy or new business model never seem to be able to articulate those strategies’ or models’ upper limit of efficacy. One hundred and ten percent scalability seems to be baked into every single one of these cakes. Perhaps the most dramatic example of this comes from the world of psychology. B. F. Skinner, through his books Walden Two and Beyond Freedom and Dignity, essentially went from Pavlov observing that his dogs salivated when they heard the dinner bell all the way to asserting a basis for ordering all of human society. And, for the record, I have on several occasions pointed out that PM techniques are utterly ill-suited for Asset Management, or handling functions.

But to prove the assertion in the title, let’s engage in a couple of mental exercises. What kinds of cooperative efforts require “management”? I would argue that those would be the efforts where there are a lot of unknown parameters that have/will have bearing on the optimal decisions and strategies. For example, driving to work in the morning doesn’t require much in the area of management, since (for most of us) it’s down to a routine. Conversely, the act of driving the family across several States for a vacation does require some level of planning and on-the-spot changes to those plans, both with respect to one’s own behavior and the behavior of others – the very definition of management. What we have here is a sort of scale, where the routine and rarely-changes-from-the-baseline efforts are on one end, and the entirely novel, never-been-attempted (while requiring some level of resources) scope is on the other.

Recall one of the most common definitions of a “Project” as the creation of something novel (e.g., there’s only one Hoover Dam). Referring back to our scale, Project Management clearly belongs on the “entirely novel” side, but I want to argue something further. As the novelty of an effort moves towards the routine, so does the need for any kind of management. The manager overseeing an assembly line, where each of the workers know exactly what to do, will typically encounter malfunctioning machines, or workers not being able to show up to the facility, or upper management inflicting changes in requirements – things of that nature. The Project Manager, in addition to the types of problems encountered by her assembly line counterpart, can also expect to encounter far less foreseeable issues, often impossible to anticipate. There’s a reason why risk managers (no initial caps) seem to gravitate towards PM much more than they do to basic manufacturing. And, when they do weigh in on matters PM, they carry with them the whole other category of “unknown unknowns,” events that are utterly capricious in nature. I think the mere existence of “unknown unknowns” as a category of unforeseeable events that are still within the purview of the project’s scope is a tacit recognition of the whole lots-more-weird-stuff-happens-to-us effect.

Let’s take a look at the more routine side of this scale by posing a question about basic management science: is there a particular field or arena of business that’s home to a vast array of formulaic approaches to management? If you answered “yeah, that would be Asset Management,” go to the head of the class. The codex covering Asset Management – including Generally Accepted Accounting Principles – is so vast that universities grant graduate degrees in it. To even credibly engage in performing its basic functions requires an extremely-difficult-to-attain certification (the CPA). Almost by definition, Accountants rarely (if ever) encounter a genuinely novel situation or circumstance that has no precedent in either GAAP or tax law. Again, this codex is certainly vast, but my point is that it’s not set up to handle the unexpected. It very specifically spells out, step by step, what to do for a given situation or circumstance. Indeed, the Asset Managers’ cliched assertion that the point of all management is to “maximize shareholder wealth” points to the fact that they truly believe that every single decision made in the business world can be quantifiably reduced to an entry in a profit and loss statement which, in my mind, is clearly absurd. For those who would disagree with the previous sentence, consider the scenario where the Project Team has delivered on-time, on-budget, while overcoming a series of technical, labor, supplier, and even environmental problems unforeseen at the time of the freezing of the baseline. The customer sees this, recognizes the managerial acumen, and resolves to send all future, similar scope to this contractor. Here’s the million-dollar question: how does any of this get captured or quantified as an entry into the general ledger?

In the world of management, the rote is pretend management, and the new, never-before-tried is the real deal. One last request: remind me which end of this spectrum is dominated by PM?

One of the most fascinating games that we Game Theorists like to analyze is the Hawk/Dove game. Imagine a population of 100 generic birds. Each bird can either forage for their own food (Dove), or else aggressively attack other birds, and take their food away from them (Hawk). Which strategy will prevail? Without getting into the mathematics of the payoff grid[i], I can relay a couple of the findings from analyzing this setup:

• The payoff for the entire population is maximized when every member behaves like a Dove.
• With the introduction of just one Hawk, however, the mixed strategy will quickly conform to the Nash Equilibrium[ii], of 25% Hawk, 75% Dove.
• If the available amount of foodstuffs is insufficient to feed the entire population, selecting the Dove strategy exclusively will lead to certain death.

Okay, so we’re obviously not talking about literal birds here, but humans in an environment where strategic options are extremely limited, reduced to the binary decision to act passively or aggressively with respect to the other members of the population. Of course, in real-life human interactions within, say, a certain organization or Project Team, the decision(s) to employ a passive strategy (perform your job duties as assigned, to the best of your ability, while assisting others doing the same thing) or a more aggressive one (pursue your own interests, even at the expense of the other members of the Team, or by bending the rules) are not so Boolean. Almost everyone engages in a “mixed strategy,” composed of actions that can be objectively aggressive, or objectively passive … or somewhere in-between. It’s this in-between area that makes the relevancy of games like Hawk/Dove to real-life situations somewhat ambiguous, meaning that any take-aways from such analyses won’t approach the precision of a calculated Nash Equilibrium. Essentially, Game Theory can lend credence to some usable axioms or somewhat dependable leading indicators of organizational failure, but can’t provide the basis for making specific organizational behavior and performance decisions.

Speaking of organizational behavior and performance decisions, GTIM Nation knows of my respect for the brilliant Michael Maccoby, particularly in his book The Gamesman: The New Corporate Leaders (Simon and Schuster, 1977). In this book, Maccoby posits four basic archetypes in the workplace:

• The Craftsman cares deeply about his actual output, but not so much as to the organization around him.
• Conversely, the Company Man cares very much about the organization around him, to the point of adopting its persona.
• The Jungle Fighter gets ahead through calumny and cloak-and-dagger strategies.
• The Gamesman does not perceive his paycheck as a roof over his head, or food on the table. Rather, this archetype sees his perks and renumerations as some sort of token in an elaborate game he’s playing. Because of this, the Gamesman is both more likely to have an advanced knowledge or capability in whatever industry in which he is engaged, and more willing to take risks than the others.

Now consider what happens when we combine the Maccoby Archetypes with the usable axioms from an analysis of the Hawk/Dove Game. If we were to bin the former into the structure of the latter, I believe Craftsmen and Company Men would tend to be Doves, while Jungle Fighters and Gamesmen would be Hawks. Recall the third bullet from the Hawk/Dove analysis, that, in the absence of sufficient supplies of food, selecting the Dove strategy exclusively means that bird will perish. In PM parlance, if there aren’t enough projects for all of the Project Teams in a given industry, relying exclusively on Craftsmen and Company Men to advance your organization – or even keep it afloat – might not be the best strategy.

“But Michael!” I can hear GTIM Nation say, “In virtually every other blog where you invoke the Maccoby archetypes, you roundly condemn the Jungle Fighters! Are you reversing that now?”

Not in the least. Jungle Fighters are absolute poison, no doubt about it. It just so happens that they share the Hawk bin with the Gamesmen, and together they make up the disruptive component of the organization, or Project Team.

All of which brings us back to the question in the title… are we talking disruptive data, or disruptive people? Because an absence of disruptive data may or may not represent management information system validity, but a complete absence of disruptive people may very well presage a competitive deficiency in your Project Team or macro-organization. The trick here is to differentiate between Jungle Fighters and Gamesmen when utilizing disruptive people. The acid test? While both Jungle Fighters and Gamesmen pursue their own interests, the Jungle Fighter will do so to the detriment of the Project Team, while the Gamesmen does so in cooperation with them[iii].

Also, Jungle Fighters have more of a buzzard-like appearance.[iv]

[i] For these formulae, see Game Theory In Management, pp. 22-26.

[ii] From Investopedia,” The Nash equilibrium is a component of game theory that asserts that a player will continue with their chosen strategy while knowing their opponent's strategy as they have no incentive to change course.”

[iii] My take, not Maccoby’s.

[iv] Same as iii.

No discussion of data disruption (ProjectManagement.com’s theme for July) would be complete without addressing that most difficult to define, yet central to all management information components, relevance. Dr. Watson was often surprised by Sherlock Holmes’ lack of awareness of popular news, which Holmes would explain as his desire to not overload his mind with data that was not relevant to his ability to solve crimes, demonstrating that the issue of data disruption has been with us since (fictional) Victorian London.

There are lots of definitions of the words relevant and relevancy, but, for the sake of this discussion, I’ll use the direct ”sufficiency to infer the conclusion.” What conclusion(s)? The one(s) that can provide the basis for, in our case, informed Project Management decisions. Recall my oft-stated derivative of the Pareto Principal, that the 80^th percentile best managers with 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. In other words, the existence and availability of relevant management information easily trumps superior managerial judgement, all other things being equal. It’s the reason why junior managers, generally speaking, do not have nearly the latitude of action that their more senior counterparts do – if one is not sure of the capability of a given decision-maker, limiting their options in an environment where canned strategies haven’t been tried is always the safer strategy.

Many things can chip away at an information stream’s relevancy. Perhaps the most pernicious is the insertion of subjectivity into the data set. Throughout my career, every single time I’ve been asked to perform an analysis of the circumstances and events that led up to a surprise overrun, the presence of subjective data – namely, the so-called “bottoms-up” Estimate at Completion – has been the culprit. This method of generating an EAC involves re-estimating the project’s (or Control Account’s, or Work Package’s) remaining work, and adding this figure to the cumulative actuals. The folly of employing this method is best laid bare with a simple histogram, showing three bars per reporting period, typically stretching back 6-7 months prior to the reporting cycle that (finally!) fesses up to the overrun: one bar indicates the Budget at Completion (BAC), the second shows the Project Manager’s / bottoms-up EAC, and the third shows the calculated EAC, based on the data available at each reporting cycle. Each time I’ve performed this analysis and produced said histogram, the results have been the same. The BAC level remains level (or close to level) for the duration, barring some Baseline Change Proposal that got snuck in to try and alleviate the incoming disaster. The PM’s / bottoms-up EAC mirrors the BAC, right up until two reporting cycles prior to the obvious, where it ticks up a bit, and then jumps to the same level as the Calculated EAC has been showing all along. The level of subjectivity inherent in the PM’s EAC, or the estimators’ take on the remaining work, render that version of the EAC irrelevant to the task of alerting management of an upcoming overrun. I call this effect pernicious because, not only does it fail to perform the task of identifying and quantifying cost performance issues early on, it actually does the opposite. It hides such problems, all while a valid calculated EAC is available, if management was only canny enough to reject the bottoms-up imposter.

Lack of timeliness will also erode an information stream’s relevance. “Quickness is the essence of the war,” claimed Sun Tzu,[i] and lack of it is poison to the Management Information System. The history of warfare is filled with stories of commanders not receiving critical, tide-of-battle-changing information until it was too late. Actionable PM information has a much shorter shelf-life than, say, information coming out of the general ledger, due to the more dynamic nature of Project Management over Asset Management. A Control Account Manager turning in a travel voucher late is no big deal. That same CAM who reports the percent complete figure late can disrupt the entire project’s reporting cycle.

Finally, if we are to employ the relevancy filter to eliminate otherwise disruptive data, then the comprehensive data set must be accurate. Inaccurate (i.e., false) data rarely comes with a warning label. But make no mistake – the processing methodology that can produce relevant information without accurate data has not been invented, nor will it ever be.

“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.”

                                    -- Sherlock Holmes, The Adventure of the Copper Beeches

…and so it is with us PM-types. But the types of information streams we use are different by type, not degree, from those used by the Asset Managers, or even Strategic Managers, and the distinction will forever fall upon the filter of relevance.

[i] Retrieved from https://wealthygorilla.com/35-powerful-sun-tzu-quotes-art-war/ on July 11, 2022, 18:14 MDT.

I recently attended a conference on Predictive Analytics and Machine Learning but found it, for the most part, rather disappointing. Two of the presentations that I attended really stood out, for different reasons. The first – a truly superb one – both demonstrated an advanced knowledge in the field, and passed along usable insights. It was presented, if I’m remembering correctly, by a professor from Northeastern University. The other was just a mess. It was an attempt to attach value judgements to the results of an analysis model dealing with sentencing and parole decisions. I found the entire premise to be vacuous, for the following reasons:

• For MISs that inform public policy, no model makes decisions. They can attempt to inform decision-makers, and the decision-makers may adhere to a template, but the models, themselves, do not make these decisions.
• Data can be complete or incomplete, relevant or irrelevant, but absolutely cannot be legitimately labelled morally right or morally wrong.
• Similarly, the methodology used to process the data into usable information (alternately referred to as “the model” or “analytics,” as in “predictive analytics”) can be valid, or invalid. It cannot be considered morally right or morally wrong.
• It was clear that the presenters who had analyzed this model were doing so from an incomplete data set, and had no idea that that was their problem, much less how to pursue a remedy.

Recall Hatfield’s Incontrovertible Rule of Management # (I lost track), that all valid Management Information Systems –even those laying claim to performing “predictive analytics” – have the same high-level structure, to wit:

• Data is collected, based on some discipline or set of parameters. In PM, we tend to want to know the scope of the work, and the amount of resources and time needed to accomplish it.
• The Data is processed into usable information, usually based on some sort of methodology. For we PM-types, Earned Value and Critical Path methodologies are central to our Management Information Systems (MISs).
• The information is delivered to the decision-makers in a way that they can actually understand and make use of it. Entry-level Project Controls specialists often err when they believe that all PMs can instantly understand a Gantt Chart, or a Cost Performance Report in Format 1.

It quickly became clear to me and my associates that, while the paper presenters and organizations manning booths in the Exhibitor’s Hall were demonstrably adept at collecting data and then using that data to better identify the buying habits of specific demographics (the better to target marketing efforts), they fell quizzically silent when the topic moved away from anticipating purchasing trends. The favorite “predictive” tool of our friends, the risk managers (no initial caps), the Monte Carlo analysis technique, was never mentioned in my hearing. An old saw in the Legal Profession is “If the facts are against you, argue the law. If the law is against you, argue the facts. If the law and the facts are against you, pound the table.”[i] I think the management science derivative would be “when you don’t have the actual algorithm at the heart of you MIS down, talk about the data. When you don’t have a real shot at collecting the comprehensive data set to make your model work, talk about the algorithm. When you have neither, fake it.”

Speaking of faking it, a common but utterly invalid method for creating a Management Information System that supposedly gives advanced warning of problems is to set up some form of a common data repository, call it something like an “action item manager,” and then have it collect data without a clearly-defined discipline or structure. The resulting “system” is, essentially, a poll, a data stack surrounded by input and output nodes. The problem with a poll is that someone always has better (more recent, or more accurate) data, so that its holdings are unreliable. Add to that the fact that massive amounts of subjectivity get shoe-horned into this “system,” and you have an environment perfectly suited to the generation of an invalid MIS, passing along highly subjective data as reliable information. What could go wrong?

Which gets us back to the whole notion of data being “disruptive.” Data, by definition, can be timely or late, relevant or irrelevant, but it can’t be wrong, or inaccurate (the term “bad data” is analogous to “bad facts.”). If a particular fact is found to be disruptive, the question has to be asked, disruptive to what? Or to whom? Well, to a particular belief, and the person holding that belief. In this sense, the whole of the PMBOK Guide® could conceivably be seen as disruptive to all of those business schools still teaching the obsolete concept that the purpose of all management is to “maximize shareholder wealth,” similar to the fact that the Earth casting a round shadow on the moon during a lunar eclipse is “disruptive” data to flat-earthers. Is your model or analysis technique delivering unexpected or unreliable output? It could be disruptive data, sure. Verify the data, of course, but suspect your analysis technique.

Or you could just pound the table.

[i] Sandburg, Carl, retrieved from https://www.goodreads.com/quotes/918291-if-the-facts-are-against-you-argue-the-law-if on July 4, 2022, 17:38 MDT.