When value is assymetrical

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I've written a couple of books on project value; you can see the book covers at the end of this blog.

One of my themes in these books is a version of cybernetics:

Projects are transformative of disparate inputs into something of greater value. More than a transfer function, projects fundamentally alter the collective value of resources in a cybernetics way: the value of the output is all but undiscernible from an examination of inputs

But this posting is about asymmetry. Asymmetry is a different idea than cybernetics

"Value" is highly asymmetrical in many instances, without engaging cybernetics. One example cited by Steven Pinker is this:

Your refrigerator needs repair. $500 is the estimate. You groan with despair, but you pay the bill and the refrigerator is restored. But would you take $500 in cash in lieu of refrigeration? I don't know anyone who would value $500 in cash over doing without refrigeration for a $500 repair.

Of course there is the 'availability' bias that is also value asymmetrical:

"One in hand is worth two in the bush"

And there is the time displacement asymmetry:

The time-value of money; present value is often more attractive than a larger future value. The difference between them is the discount for future risk and deferred utility.

Let's not forget there is the "utility" of value:

$5 is worth much less to a person with $100 in their pocket than it is to a person with only $10

How valuable?
So when someone asks you "how valuable is your project", your answer is ...... ?

 

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ISO 42001 AI Management Systems

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Late in 2023 ISO published ISO 42001-2023 "Information technology Artificial intelligence Management System"

To quote ISO:

ISO/IEC 42001 is an international standard that specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations.

It is designed for entities providing or utilizing AI-based products or services, ensuring responsible development and use of AI systems.

For project offices and project managers, there are some points that bear directly on project objectives:

• The standard addresses the unique challenges AI poses, which may need to be in your project's requirements deck, such as properties or functionality that addresses ethical considerations, transparency, and continuous learning. 
• For organizations and projects, the standard sets out a structured way to manage risks and opportunities associated with AI, balancing innovation with governance.

Learn More
Of course, with something like this, to learn more about this you need not go further than the ISO website (more here) for relevant PDFs and FAQs. But, of course, you can also find myriad training seminars, which for a price, will give you more detail.

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Risk on the black diamond slope

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If you snow ski, you understand the risk of a Black Diamond run: it's a moniker or label for a path that is  risk all the way, and you take it (for the challenge? the thrill? the war story bragging rights?) even though there may be a lesser risk on another way down.

So it is in projects sometimes: In my experience, a lot of projects operate more or less on the edge of risk, with no real plan beyond common sense and a bit of past experience to muddle through if things go wrong.

Problematic, as a process, but to paraphrase the late Donald Rumsfeld: 

You do the project with the resources and plan you have, not the resources or plan you want

You may want a robust risk plan, but you may not have the resources to research it and put it together.

You may not have the resources for a second opinion

You may not have the resources to maintain the plan. 

And, you may not have the resources to act upon the mitigation tactics that might be in the plan.

Oh, woe is me!

Well, you probably do what almost every other PM has done since we moved past cottage industries: You live with it and work consequences when they happen. Obviously, this approach is not in any RM textbook, briefing, or consulting pitch. But it's reality for a lot of PMs.

Too much at stake
Of course, if there is safety at stake for users and developers, as there is in many construction projects; and if there is really significant OPM invested that is 'bet the business' in scope; and if there are consequences so significant for an error moved into production that lives and livelihoods are at stake, then the RM plan has to move to the 'must have'.  

A plan with no action

And then we have this phenomenon: You actually do invest in a RM plan; you actually do train for risk avoidance; and then you actually do nothing during the project. I see this all the time in construction projects where risk avoidance is clearly known; the tools are present; and the whole thing is ignored.

Show me the math

But then of course because risk is an uncertainty, subject to the vagaries of Random Numbers and with their attendant distributions and statistics, there are these problems:

• It's easy to lie, or mislead, with 'averages' and more broadly with a raft of other statistics. See: How to Lie with Statistics (many authors) 
• Bayes is a more practical way for one-off projects to approach uncertainty than frequency-of-occurrence methods that require big data sets for valid statistics, but few PM really understand the power of Bayes. 
• Coincidence, correlation, and causation: Few understand one from the other; and for that very reason, many can be led by the few to the wrong fork in the road. Don't believe in coincidence? Then, of course, there must be a correlation or causation!

The upshot?

Risk, but no plan.

Or plan, and no action

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The people are told .....

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In the beginning, "people" are told: "It's too soon to know where we are in this project"

After the beginning, "people" are told: "It's too late to stop the project; there's too much sunk; we have to keep going"

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Sampling the data

And so the bane of big projects comes down to poor sampling technique: 

Either the early details are not predictive because the early "efficiencies" of cost per unit of value earned have too little history to be useful as a long-term predictor; or you've accepted the first idea for too long, thereby failing to update efficiency predictions until the late details are too late to pull the plug on a bad bet.

Sunk cost decisions:

It's easy to write this, and far less easy to execute, but never make a decision about the future based on the sunk cost of the past. You can't do anything about recovering the actual expenditure, but you do have free will -- politics aside -- regarding more spending or not. 

History has value

On the other had, sunk cost has a history, and if you are good at what you do, you will use that history to inform your decisions about the opportunity of the future

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New Grads for your Project

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If you recruit new grads for your project, I wonder if your experience comports with this report from "Intelligent"? The gist seems to be this:

• Headline: "1 in 6 Companies Are Hesitant To Hire Recent College Graduates"
• 75% of companies report that some or all of the recent college graduates they hired this year were unsatisfactory
• Hiring managers say recent college grads are unprepared for the workforce, can’t handle the workload, and are unprofessional

I'm thinking there may be more troubles in established firms than in more "flexible" start-ups, but here's Intelligent's observation:

“Many recent college graduates may struggle with entering the workforce for the first time as it can be a huge contrast from what they are used to throughout their education journey. They are often unprepared for a less structured environment, workplace cultural dynamics, and the expectation of autonomous work. Although they may have some theoretical knowledge from college, they often lack the practical, real-world experience and soft skills required to succeed in the work environment. These factors, combined with the expectations of seasoned workers, can create challenges for both recent grads and the companies they work for,” says Intelligent’s Chief Education and Career Development Advisor, Huy Nguyen.

Wow! I hope this not true in your industries, but it may be distressingly common.

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Quantitative Methods: It takes a number!

Numbers are a PM's best friend

Bayes Thinking Part II

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In Part I of this series, we developed the idea that Thomas Bayes was a rebel in his time, looking at probability problems in a different light, specifically from the proposition of dependencies between probabilistic events.

In Part I we posed the project situation of 'A' and 'B', where 'A' is a probabilistic event--in our example 'A' is the weather--and 'B' is another probabilistic event, the results of tests. We hypothesized that 'B' had a dependency on 'A', but not the other way 'round.

Bayes' Grid

The Figure below is a Bayes' Grid for this situation. 'A+' is good weather, and 'B+' is a good test result. 'A' is independent of 'B', but 'B' has dependencies on 'A'. The notation, 'B+ | A' means a good test result given any conditions of the weather, whereas 'B+ | A+' [shown in another figure] means a good test result given the condition of good weather. 'B+ and A+'  means a good test result when at the same time the weather is good. Note the former is a dependency and the latter is a intersection of two conditions; they are not the same.

  

The blue cells all contain probabilities; some will be from empirical observations, and others will be calculated to fill in the blanks. The dark blue cells are 'unions' of specific conditions of 'A' and 'B'. The light blue cells are probabilities of either 'A' or 'B'.

Grid Math

There are a few basic math rules that govern Bayes' Grid.

• The dark blue space [4 cells] is every condition of 'A' and 'B', so the numbers in this 'space' must sum 1.0, representing the total 'A' and 'B' union
• The light blue row just under the 'A' is every condition of 'A', so this row must sum to 1.0
• The light blue column just adjacent to 'B' is every condition of 'B' so this column must sum to 1.0
• The dark blue columns or rows must sum to their light blue counter parts

Now, we are not going to guess or rely on a hunch to fill out this grid. Only empirical observations and calculations based on those observations will be used.

Empirical Data

First, let's say the empirical observations of the weather are that 60% of the time it is good and 40% of the time it is bad. Going forward, using the empirical observations, we can say that our 'confidence' of good weather is 60%-or-less. We can begin to fill in the grid, as shown below.

In spite of the intersections of A and B shown on the grid, it's very rare for the project to observe them. More commonly, observations are made of conditional results.  Suppose we observe that given good weather, 90% of the test results are good. This is a conditional statement of the form P(B+ | A+) which is read: "probability of B+ given the condition of A+".  Now, the situation of 'B+ | A+' per se is not shown on the grid.  What is shown is 'B+ and A+'.  However, our friend Bayes gave us this equation:

P(B+ | A+) * P(A+) = P (B+ and A+)  = 0.9 * 0.6 = 0.54

Take note: B+ is not 90%; in fact, we don't know yet what B+ is.  However, we know the value of 'B+ and A+' is 0.54 because of Bayes' equation given above.

Now, since the grid has to add in every direction, we also know that the second number in the A+ column is 0.06, P(B- and A+).

However, we can go no farther until we obtain another independent emprical observation.
 

To be continued

In the next posting in this series, we will examine how the project risk manager uses the rest of the grid to estimate other conditional situations.

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