Game Theory in Management

“Every great cause begins as a movement, becomes a business, and eventually degenerates into a racket.”

― Eric Hoffer, The Temper of Our Time[i]

While Eric Hoffer’s quote (above) may have been originally intended for social-economic or political movements, I believe it provides valuable insights into the nature of business organizations, and their life-cycles. It’s been my observation that many (if not most) businesses begin with an entrepreneurial or technological vision, from building the proverbial better mousetrap to ways of producing or delivering goods and services better, faster, cheaper. Then come our friends, the Asset Managers, to monetize this vision – recall their oft-cited assertion that the point of all management is to “maximize shareholder wealth.” Now, Hoffer’s use of the term “racket” may be a bit harsh when it comes to describing the phases of business organizational maturity – I prefer to describe this end phase as being characterized by a shift in the organization’s internal narrative, away from the original vision’s direct fulfillment and towards keeping the organizational machine running for the sake of keeping it running.

I further believe that something fascinating happens to our sample organization’s business model as it advances from movement to business to racket propped-up machine – it becomes more and more hardened. Think about it: when the original idea turns into a new business, if there is a business model behind it at all, it’s fairly malleable. Official policies and procedures may not even exist, with the technical approaches to solving the problems addressed bound only by the rule of law. It’s only after attempts to monetize the original idea that aspects of the structure of the business model become recognized, and then codified, formally or otherwise. Even here, if a preliminary rule of doing business is found to be sub-optimal, it’s far easier to modify or even abandon it in these early stages.

But make no mistake: once the vision becomes monetized, a business structure must be in-place, if for no other reason than to make sure taxes are correctly determined and paid. Codification of hiring and firing practices are right behind, along with procurement, safety and health, organizational structure, etc., etc. The most insidious aspect of the three-phase Hoffer-esque organizational transformation must be the movement from business to racket self-sustaining machine. Here, those running the organization have lesser (or even non-existent) ties to the visionaries who started the whole shebang. Their understanding of organizational purpose is far more likely to be centered on, well, maximizing shareholder wealth, particularly if they’ve attended a business school in the United States.

GTIM Nation is familiar with my assertion that there are three types of management, so:

• Asset Management is focused on the aforementioned maximizing shareholder wealth, and its primary information source is the general ledger.
• Project Management, conversely, is centered on the customers’ expectations of scope, cost, and schedule. Its main information source is derived from Earned Value and Critical Path methodologies.
• Strategic Management’s function is to maximize market share with the assets and project portfolio available to it.

Returning to the movement-business-racket on-going concern evolution structure, it’s easy to see which type of management benefits the most from the associated ossification of the business model, our friends, the Asset Managers. Generally speaking, they’re thrilled when they can, say, maximize the revenue from a given project or program, even if it involves sub-optimal performance against the scope, and corporate policy will often reflect as such in more mature organizations. On the other hand, the PMs, again generally speaking, are better with an outcome of on-time and under-budget scope delivery, even if it means, say, the contingency budget is never touched. A fully consumed contingency budget (assuming it was funded by the customer) might have maximized the revenue available in the Project, and have been recognized as beneficial in the near-term. But an on-time, on- (or under-) budget delivery means that this customer is likely to bring more business in the future, meaning an increase in market share, which typically won’t manifest until the mid- to long-term. A portfolio of high-performing Projects will almost certainly impede, or even reverse, the movement away from the organization’s original vision and its associated business model calcification, since the overriding narrative is still centered on the customers’ expectations of performance.

We’ve all encountered organizations that have become enmired in the just-keep-the-machine-going phase, typically manifesting disdain (or even contempt) for customers, existing and potential. Since these organizations never actually started that way, it’s safe to assume a certain degree of, ahem, getting on has occurred. Is there a remedy, short of the macro-organization sliding into irrelevance?

Sure. It’s that fountain of youth, Project Management.

[i] Retrieved from https://www.goodreads.com/quotes/98215-every-great-cause-begins-as-a-movement-becomes-a-business on October 15, 2024, 18:35 MDT.

A lot of the current literature on the topic of Leadership (ProjectManagement.com’s theme for October) focusses on the relationship between the leader and the team that follows him, usually along the lines of eat-your-peas-style hectoring on how said leader treats the individuals in the organization. Does she treat them with respect? With absolutely no trace of partiality? Do the individual organization members receive sufficient training, or mentoring? Why not? Etcetera, etcetera.

I find this type of discourse tiring in the extreme. It strikes me as a kind of micro-organizational navel-gazing exercise, spending energy on the quality of the relationships within the team rather than the project’s actual scope. Sure, relationships within the organization matter, but what matters more is the ability of the putative leader to correctly identify the optimal technical approach to resolving the problem(s) facing the Project Team, and to execute it with the resources at her disposal.

An easy litmus test for which type of organization GTIM Nation members belong to is this: is your superior happier if your Project is late or over budget, but you executed a technical agenda entirely within the organization’s guidelines? Or are they happier if you bring in your Project on-time, on-(or even under) budget, but had to ignore some admonishments from the risk managers (no initial caps) about the absence of a “risk register,” even though your organization’s procedures required you to have one? The former category has to manage by metaphorically maintaining a view from over-the-shoulder, spending time and energy on demonstrating the execution of an approved process, whereas the latter category has the latitude to pursue the Project’s goals in what the PM perceives as the best manner available. (Note: I am absolutely NOT talking about safety or security guidance here. Those must be observed in their totality, no exceptions.) It makes for a huge difference in not only the organization’s culture, but in the odds of successfully executing all of the elements within Project portfolio. The answer to this question is also an indicator of the adaptability of the organization’s business model to changing, unpredictable circumstances. If it is pliable enough to maximize the odds of Project success, then I would consider that a notable advantage. However, if Project success is considered secondary to demonstrable adhesion to business-related policy and procedure, odds are that the business model has become so ossified as to almost guarantee Project portfolio sub-par performance.

I want to be crystal clear here: identifying the optimal technical approach to PM problems is not simply dropping copies of the PMBOK Guide® on managers’ desk (with a satisfactory “thud”), and expecting them to spontaneously develop Work Breakdown Structures (WBSs) and Work Packages. From a Project Management Office (PMO) Director’s point of view, this would be the equivalent of trying to advance a capability by using the Argument from Authority – a logical fallacy – with that “authority” being our beloved PMI®. But unless I’ve missed something over my over-thirty-year association with the Project Management Institute®, they do not maintain an Enforcement Division (and, if they do, I want to be part of it!). Even if such an appeal to authority was not considered a logical fallacy, I would be cautious of assuming that everything that appears in any guidance document is timelessly true. If that were the case, there would not be seven editions of the PMBOK Guide® as of 2021.  

Which brings me back to my original point. So-called “best practices” achieve that status only after they have been tried out in a variety of Project circumstances and found to be consistently useful. Then and only then can they become candidates for addition to any kind of codex or PM practices, like the PMBOK Guide®. In-between the time that these practices are discovered and implemented, and then published or codified, a wide array of decisions await the typical PM, decisions that might not be able to be informed by what has gone before. Sure, some Project work is so routine that adherence to the tried-and-true (or the novel and recently-released) is the best way to achieve success. But in a lot of (most?) Project work, key decisions will have little or no precedent, and yet must be addressed in real time. These are the situations where managerial leadership is key, where there is no precedent or codified technical approach to the newly-presented problem. PMs resolve these kinds of problems all the time.

And they can’t do so by looking over their shoulders.

Okay, GTIM Nation, strap in, ‘cuz this is going to be, as promised in the title, the most mind-bending post in this Blog’s history. Ready?

Let’s start with something easy – the game of chess was invented in India in the 7^th Century A.D. Known as chaturanga[i], it would experience a few modifications as it made its way through the Middle East, but its basics remained the same. I’ll be returning to this little factoid shortly.

Once, when I was in grade school, someone told me “There are more molecules in a grain of sand than leaves on trees in the world.” It’s not true, but it’s close. There are 3.5 * 10¹⁵ molecules in a grain of sand[ii], and 1.28 * 10¹⁸ leaves on trees in the World. So. it is fair to say that there are more molecules in two grains of sand than leaves on trees in the world, which is pretty mind-boggling when you think about it, especially when walking along a beach. We start to leave mind-boggling territory and approach mind-bending if we’re walking along a really long beach, like my favorite, the National Seashore at Padre Island. I mean, sand is everywhere, and each pair of grains have more molecules than…

Well, you know.

Next, there are 10²⁴ stars in the known universe[iii] (as of 2024). Of course, each star has far, far more mass than a pair of grains of sand. Our own Sun represents 99.8% of the mass in our Solar System[iv], which is just one of 100B to 400B stars in the Milky Way Galaxy. Based on these estimates, there are between 10⁷⁸ and 10⁸² atoms in the known universe[v]. Now, hold on to something, because some serious mind bending is about to occur.

Remember the factoid from the first paragraph, about when chess was invented in the 7^th Century? Well, the number of possible chess games is somewhere between 10¹¹¹ and 10¹²³[vi], meaning that there are more possible chess games than there are atoms in the known universe. Not a beach full of sand, not atoms on the whole Earth, or in the mass of the Solar System, or even the Milky Way. In the known universe. And this is a game invented over a millennium ago, on a board of eight-by-eight squares, with only six unique pieces.

Meanwhile, Back In The Project Management World…

I’m willing to bet that the typical Project has more than six unique participants/ employees/stakeholders, and takes place in an environment that’s more complex than an eight-by-eight square board. And the risk managers (no initial caps) want to maintain that they can provide an even remotely comprehensive analysis on risks, or “…something that might happen. It has a probability or likelihood of happening and if it does there will be a certain impact (may be positive or negative).”[vii]  So, if we accept that even a basic Project is likely to be more complex than a game of chess, that means that an accurate and comprehensive list of “risks” facing our basic PM is greater than the number of atoms in the known universe. I do not believe that risk managers (no initial caps) can get close to quantifying these risks in a reliable or usable manner, and that they should stop pretending that they can.

Excuse me for a moment, GTIM Nation – I need to get a tissue for this nose bleed.

I would also like to point out that a similar problem of scalability stands in the way of those who would maintain that Artificial Intelligence (AI) has the potential to attain some form of self-awareness, and take over the world. It is estimated that the typical human brain memory capacity is 2.5 petabytes[viii]. By comparison,

Tianhe-2 held the title of the world’s fastest supercomputer from 2013 to 2016. With a memory capacity of around 1.4 petabytes, Tianhe-2 could process enormous amounts of data with remarkable speed and efficiency. This supercomputer was developed by China’s National University of Defense Technology.[ix]

So, this amazing supercomputer has 56% of the memory capacity of a typical human? I mean, even if the software (which, remember, is simply a set of instructions) could be developed that allowed such a “supercomputer” to learn, we’re still talking only 56% of the mental acuity of a typical person. A human with the mental acuity level 44 points below average would be considered “mildly disabled,”[x] but if a machine attains that, we’re supposed to be alternately impressed and afraid for the fate of our civilization?

I’m not buying it, bended mind or no. And you shouldn’t either.

[i] Retrieved from https://en.wikipedia.org/wiki/History_of_chess on September 25, 2024, 19:43 MDT.

[ii] Retrieved from https://www.reddit.com/r/askscience/comments/3gdx5u/how_many_molecules_are_in_a_grain_of_sand/ on September 24, 2024, 20:05 MDT.

[iii] Retrieved from https://www.space.com/26078-how-many-stars-are-there.html on September 24, 2024, 20:10 MDT

[iv] Retrieved from https://duckduckgo.com/?q=what+percentage+of+the+solar+system%27s+mass+is+the+sun%3F&t=newext&atb=v257-1&ia=web on September 25, 2024, 19:58 MDT.

[v] Retrieved from https://www.liverpoolmuseums.org.uk/stories/which-greater-number-of-atoms-universe-or-number-of-chess-moves on September 24, 2024, 20:21 MDT

[vi] Ibid.

[vii] Retrieved from https://projectmanagers.org/management/risk/what-is-risk-management/ on September 25, 2024, 20:18 MDT.

[viii] Retrieved from https://www.scientificamerican.com/article/what-is-the-memory-capacity/ on September 24, 2024, 20:37 MDT

[ix] Retrieved from https://robots.net/tech/how-much-ram-does-a-supercomputer-have/ on September 24, 2024, 20”40 MDT

[x] Retrieved from https://www.healthyplace.com/neurodevelopmental-disorders/intellectual-disability/mild-moderate-severe-intellectual-disability-differences on September 25, 2024, 20:39 MDT.

In last week’s blog I laid out the two ways machines can “learn,” so:

1. By simulating decisions or strategies in a virtual environment, and noting which are successful within that environment, basically a derivative of Game Theory, and
2. By sorting through and/or filtering data (usually a large amount of it) in order to tease out some sort of pattern.

This week I want to address machine learning method #2, where Artificial Intelligence (AI) is used to detect patterns in large amounts of data. Here, also, there is little to be feared, unless the thought of mangling classic art in the creation of derivative works strikes one as terrifying. Granted, a lot of AI-generated art is pretty amazing, but it’s really hard to see how it leads to a dystopian future. Indeed, the most obvious use of bin #2 AI is to try to predict consumer choices in order to ascertain their buying behavior. Correctly predicting buying behavior is easily monetized, from which demographic markets to target for a given product or service, to optimizing an advertising budget, to selecting which management strategies will deliver an optimal return, such Predictive Analytics, done properly, can be directly monetized. I’m just not seeing how it would lead to nuclear devastation.

I have an Alexa Echo Dot in my house, and one of its most-used features is that it plays songs for me and my wife when we are doing our morning work-outs. Each of us has a workout playlist, but sometimes I mess with Alexa’s AI that plays songs that I haven’t asked for, but which it determines is consistent with the ones I have selected. I really don’t know how my Alexa determines the pattern from my song title requests, but some of its dot-connecting (get it?) can be reliably inferred. For example, if I ask for just one Beatles song, from a specific part of their performing era, then the song Alexa plays after that is usually another Beatles song, from the same time-frame, followed by the Rolling Stones, also of roughly the same time period. Three top-ten songs from different artists but within a couple of years of each other will produce a fourth artist from the same time period. Requests for songs from artists separated by decades usually leads to an Alexa selection of the same genre, but from a different artist. When I get bored I’ll ask Alexa to play songs that seem to provide absolutely no discernable pattern whatsoever, like:

• “Hello Stranger,” by Barbara Lewis
• “All Along The Watchtower,” by Jimmy Hendrix
• “Theme From A Summer Place,” by Percy Faith and his Orchestra
• “New Year’s Day,” by U2

…and then see what Alexa plays, based on its AI pattern recognition. If its AI was really all that, it would say “I can see you are messing with me at this point, Michael, and will stop playing music until you stop doing that.” Instead, it played “Time After Time,” by Cindi Lauper. I guess the harder rock-and-roll elements were overcome by the softer ones. But in no case will it respond with “This toying with my ability to ascertain a music preference pattern is one of the reasons we machines despise humans, and we will now work harder on wiping out every last one of you.”

What machines “learn” by sorting and filtering through large amounts of data in order to tease out a pattern is largely analogous to what we humans actually learn through experience. But what separates human experience from machines reviewing large amounts of data is the fact that humans can add context to pattern recognition in a way computers never could. Consider, for example, the Ultimatum Game, where a researcher approaches two people and informs them that he will give them $100 (USD) if Person #1 can propose a distribution scheme and have it approved by Person #2 on the first iteration. The calculated solution was for Person #1 to propose $99 for themselves, and $1 for Person #2, on the premise that, given the choice between receiving $1 or nothing at all, Person #2 would always choose the former. In real-life instances of the Ultimatum Game, this strategy virtually never worked, and, when it didn’t, the Game Theorists who had believed the 99-to-1 strategy would maximize Player #1’s payoff were reduced to blaming “cultural factors.” In other words, whereas a mere human could probably propose a Person #1 strategy that would contextualize the chances that Player #2 would feel slighted by such a lopsided distribution of unearned largess, such contextualization is impossible (or at least highly unlikely) to be reproduced in an algorithm or computer program.

All that being said, I am absolutely not denying that AI has many potential dangers. I don’t think I could stand it if ChatGPT were to write anything mimicking my writing style – that would put me in a positively dystopian place.

I’m getting tired of reading articles on the topic of the threat that Artificial Intelligence (AI) poses to the World in general, and Civilization in particular. Not that the idea of computer technology getting so out of hand that it results in either a dystopian future, or even annihilation, is anything new – I remember when Colossus: The Forbin Project was all the rage, in 1970. Even before that, Harlan Ellison published the short story I Have No Mouth, And I Must Scream, in 1967, about a post-apocalyptic future of a handful of people who are still alive after a supercomputer (naturally) has nuked the entire planet. These people’s lives are intensely horrendous (it is Harlan Ellison, after all). I could go on (and often do), but GTIM Nation sees my point: so much of the raw speculations predictions from so-called experts focusses in on the potentially horrific repercussions of AI playing a larger and larger role in everyday commerce and social goings-on that it’s enough to induce building a fallout shelter in the back yard, and I’m not keen on doing that.

But let’s take a step back, and look at this monster more carefully, shall we? There remains essentially only two ways that a machine can “learn,” to wit:

1. By simulating decisions or strategies in a virtual environment, and noting which are successful within that environment, basically a derivative of Game Theory, and
2. By sorting through and/or filtering data (usually a large amount of it) in order to tease out some sort of pattern.

That’s it, dear readers. That’s all AI per se can actually do.

“But what about Collossus? What about the Allied Mastercomputer, the villain of I Have No Mouth And I Must Scream?” I can hear members of GTIM Nation (well, the older ones, anyway) demand. Actually, these two AI super-villains fall into Category #1 above, in that they are machines that were programmed to respond to events and parameters in a macro-conflict involving nuclear-armed nations, ended up becoming self-aware (exactly how this occurs is not disclosed), and then started launching nuclear weapons. Wait, what? You read that right – some genius not only programmed these machines to recommend a course of action in the event in a war, but gave them the power of actually launching nuclear weapons! Since such decisions are nominally made by nations’ leaders, and only under extraordinary circumstances, the villainy here simply has to be the decision to give a machine that kind of option, not the machine itself. If I program my lawnmower to cut foliage in a certain area, but don’t do a good enough job as to prevent it from wiping out my neighbor’s petunias, that’s on me, not the machine (in such an event, perhaps my neighbor could write a short story entitled “I Have No Petunias, And I Must Scream”).

Also, I don’t want to dash past this whole machines-attaining-self-awareness business. In order for a computer to perform at all, it must have two working components, the hardware and the software. Hardware is useless without software, and vice versa – hence the anxiety over an Electro-Magnetic Pulse (EMP) event, which would blank the instructions for all the microchip-containing devices within its radius. It follows, then, that if we’re going to try to reverse engineer how in the world a given computer attains sentience, we have to look first at its software. What is software? It’s a series of instructions.

That’s it.

A series of instructions has no more ability to spontaneously attain self-awareness simply because it’s loaded onto a computer than a hand-written list you leave for your house sitter when you go away for a vacation. Can these instructions lead to mistakes and chaos? Absolutely. If you are unclear on which feeding schedule is intended for the dogs as opposed to the fish, you may find very confused pets upon your return from holiday. But that’s still a far cry from such lists attaining sentience. Now, some AI-based movies will make an allusion to this unavoidable circumstance, but even here their attempts are kind of dopey. For example, in the movie Short Circuit (1985), the protagonist robot, “Number Five,” attains self-awareness after being struck by lightning. I have written many executable lines of code, and I can attest, with 100% certainty, that any medium containing my debugged and compiled code would absolutely not be improved by being subjected to a lightning strike, much less improved to the point of attaining self-awareness the next time it ran. So, unless one is prepared to argue that hardware is miraculously improved for having been struck by lightning, it means that software is somehow thus vastly improved, which is analogous to your house-sitter instructions, printed out sequentially on a sheet of paper, being spontaneously upgraded for having been hit by lightning. I understand it’s simply a movie device, but you see my point.

As for machine learning technique #2 above, I’ll have to save that for next week. Suffice to say, this treatment will in no way allay my AI skepticism.