Friday, June 9, 2023

Chains and funnels in risk management



What to make of chains and funnels? And, if I also stick in anchors, does it help?


What I'm actually talking about is conjunctive events, disjunctive events, and anchor bias:
  • Conjunctive events are chains of event for which every link in the chain must be a success or the chain fails. Success of the chain is the product of each link's success metric. In other words, the chain's success probability degrades geometrically (example: chain of 'n' links, each with probability 'p', has an overall probability of p*p*p* .... for 'n' p's.)
      
  • Disjunctive events are independent events, all more or less in parallel, somewhat like falling in a funnel, such that if one falls through (i.e, failure) and it's part of a system, then the system may fail as a whole. In other words, if A or B or C goes wrong, then the project goes wrong.


The general tendency to overestimate the probability of conjunctive events leads to unwarranted optimism in the evaluation of the likelihood that a plan will succeed or that a project will be completed on time. Conversely, disjunctive structures are typically encountered in the evaluation of risks. A complex system, such as a nuclear reactor or a human body, will malfunction if any of its essential components fails.
Daniel Kahneman and Amos Tversky
"Judgment Under Uncertainty: Heuristics and Biases"

Fair enough. Where does the anchor come in?

Anchoring refers to the bias introduced into our thinking or perception by suggesting a starting value (the anchor) but then not adjusting far enough from the anchor for our estimate to be correct. Now in the sales and marketing game, we see this all the time. 

Marketing sets an anchor, looking for a deal in the business case; the sales guy sets an anchor, hoping not to have to give too much away post-project. The sponsor sets an anchor top down on the project balance sheet, hoping the project manager will accept the risk; and the customer sets anchors of expectations.

But in project planning, here's the anchor bias:
  • The likely success of a conjunctive chain is always less than the success of any link
  • The likely failure of a disjunctive funnel is always greater than the failure of any element.

Conjunctive chains are products of numbers less than 1.0.
  • How many of us  would look at a 7 link chain of 90% successes in each link and realize that there's less than one 1 chance in 2 that the chain will be successful? (probability = 0.48)
Disjunctive funnels are more complex.
They are the combinations (union) of independent outcomes net of any conjunctive overlaps (All combinations of OR less all AND). In general the rules of combinations and factorials apply.
  • How many of us would look at a funnel of 7 objects, each with likely 90% success (10% failure) and realize that there's better than 1 chance in 3 that there will be 1 failure among 7 objects in the funnel? (probability = 0.37 of exactly 1 failure)*
 The fact is, in the conjunctive case we fail to adjust downward enough from 90%; in the disjunctive case we fail to adjust upward from the 10% case.  Is it any wonder that project estimates go awry?

_________________________
*This is a binomial combination of selecting exactly 1 from 7, where there are 6 conjunctive successes and 1 conjunctive failures: factorial (7 take 1) *conjunctive failure * conjunctive success




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Sunday, June 4, 2023

What are 'vanity metrics'?



Vanity Metrics: Actually, until recently, I had not heard of vanity metrics, aka VM. Now, I am writing about them! Does that make me a VM SME?...

So, some definition, as given to us by VM inventor Eric Ries, as posted at fourhourworkweek.com

The only metrics that entrepreneurs should invest energy in collecting are those that help them make decisions. Unfortunately, the majority of data available in off-the-shelf analytics packages are what I call Vanity Metrics. They might make you feel good, but they don’t offer clear guidance for what to do.

So, some examples -- as cited by Mike Cohn in an email blast about Reis' ideas:
Eric Ries first defined vanity metrics in his landmark book, The Lean Startup. Ries says vanity metrics are the ones that most startups are judged by—things like page views, number of registered users, account activations, and things like that.

So, what's wrong with this stuff? VMs are not actionable.. that's what's wrong. The no-VM crowd says that a clear cause-and-effect relationship is not discernible, and thus what action (cause) would you take to drive the metric higher (effect)? 

Well, you can't tell because there could be many cause, some indirect, that might have an effect -- or might not. The effect may be coming from somewhere else entirely. So, why waste time looking at VMs if you can't do anything about it?

Ries goes on to tell us it's all about "actionable metrics", not vanity metrics. AMs are metrics with a direct cause and effect. He gives some examples:
  • Split tests: A/B experiments produce the most actionable of all metrics, because they explicitly refute or confirm a specific hypothesis
  • Per-customer: Vanity metrics tend to take our attention away from this reality by focusing our attention on abstract groups and concepts. Instead, take a look at data that is happening on a per-customer or per-segment basis to confirm a specific hypothesis
  • Cohort and funnel analysis: The best kind of per-customer metrics to use for ongoing decision making are cohort metrics. For example, consider an ecommerce product that has a couple of key customer lifecycle events: registering for the product, signing up for the free trial, using the product, and becoming a paying customer

Now, it's time to introduce my oft cited advice: Don't confuse -- which is actually easier to write than to do -- cause-effect (causation) with correlation (somewhat coordinated movements, but not causation)
  • Causation: because you do X, I am compelled (or ordered, or mandated) to do Y; or, Y is a direct and only outcome of X. I sell one of my books (see below the books I wrote that you can buy) and the publisher sends me a dollar ninety-eight. Direct cause and effect; no ambiguity. Actionable: sell more books; get more money from the publisher.

  • Correlation: when you do X, I'll be doing Y because I feel like doing Y, but I could easily choose not to do Y, or choose to do Z. I might even do Y when you are not doing X. Thus, the correlation of Y with X is not 100%, but some lesser figure which we call the coefficient, typically "r". "r" is that part of the Y thing that is influenced consistently by X
So, what is the actionable thing to do re X if I want you to respond Y? Hard to say. Suppose "r" is only 2/3'rds. That means: 2 out of three times you'll probably respond to X with Y, but a third of the time you sit on it .... or do something else I don't care about. Bummer!

Here's my bottom line: on this blog, I watch all the VM analytics... makes me feel good, just as Ries says. But I also look at the metrics about what seems to resonate with readers, and I take action: I try to do more of the same: AM response, to be sure.

I frankly don't see the problem with having both VM and AM in the same metric system. One is nice to have and may provide some insight; one is to work on!




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Wednesday, May 31, 2023

Emergent methods


The influence of A.I. on project methodologies and process steps is yet to be defined, practised widely, and understood in the context of what most of think of when we think of project methods and processes, skills, risks, and predictable and planned outcomes .

And so, we are told to brace for emergent outcomes, not entirely predictable at the outset. And we are told to adapt our leadership and management skills to this reality (or reality in-waiting)

With that all in mind, I asked chatGPT to opine on emergent methods. Here is what it gave me back:

In the context of project management, "emergence" or "emergent methods" refers to the phenomenon where complex and unpredictable outcomes arise from the interactions and dynamics of various elements within a project. Instead of relying solely on predetermined plans and rigid processes, emergent methods embrace adaptability, flexibility, and the ability to respond to changing circumstances.

Traditionally, project management has followed a linear or sequential approach, where all tasks and dependencies are predetermined in a detailed project plan. However, this approach can be limiting when faced with uncertain and dynamic environments. Emergent methods offer an alternative perspective by acknowledging that projects are often complex, and the future is uncertain.

In emergent methods, project managers adopt a more iterative and adaptive approach, focusing on continuous learning and improvement throughout the project lifecycle. Rather than trying to predict and control every aspect of the project from the outset, emergent methods encourage exploration, experimentation, and the ability to adjust the project's direction based on real-time feedback and insights.

Key characteristics of emergent methods in project management include:

Iterative Planning: Instead of creating a rigid and detailed plan at the beginning of the project, emergent methods emphasize iterative planning. This involves developing a broad roadmap and refining it in smaller increments as the project progresses and new information becomes available.

Continuous Learning and Adaptation: Emergent methods embrace the concept of learning as you go. Project teams gather feedback and data throughout the project's execution, allowing them to adapt their strategies and make informed decisions based on evolving conditions and requirements.

Flexibility and Agility: Emergent methods prioritize flexibility and agility to respond to changing circumstances. This may involve adjusting project scope, reallocating resources, or modifying project objectives based on emerging needs or unforeseen challenges.

Empowered Teams and Collaboration: Emergent methods emphasize empowering project teams to make decisions and encouraging collaboration. By promoting open communication and fostering a culture of trust, teams can respond effectively to emerging issues and capitalize on opportunities.

Embracing Uncertainty: Unlike traditional project management approaches that attempt to eliminate uncertainty, emergent methods recognize that uncertainty is inherent in complex projects. Instead of resisting it, these methods embrace uncertainty and focus on building resilience and adaptive capacity.

Emergent Leadership: In emergent methods, project managers adopt a leadership style that is more facilitative and empowering. They create an environment where team members can contribute their expertise and collectively make decisions to address emergent challenges.

By adopting emergent methods, project managers can better navigate complex and unpredictable projects, promote innovation and creativity, and increase the chances of project success in dynamic environments. However, it's important to note that emergent methods are not suitable for all projects and may require a shift in mindset and organizational culture to be effectively implemented.







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Sunday, May 28, 2023

A creativity explosion?


There are endless headlines and debates about the downside of the A.I. revolution that is (likely) coming, some of which is upon us. Since most project people are knowledge workers or skilled trades workers, the seeming threat of A.I. job replacement is always just hanging a bit off stage-left.

But along comes an essay by Daniel Miessler which posits, in part, that we may be on a cusp of a creativity explosion which by its very nature is going to find its way into projects of all types.

Here's what Miessler has to say [lightly edited], writing in the '1st person':

What’s about to happen to knowledge workers [may be going] to be bleak. And it’s [probably] going to happen so quickly. [..........] in the last several weeks I’ve had a new thought that is blowing me away.

Let me ask you this: what percentage of people are producing creative ideas that are being seen by others and that are good enough to earn them a living? Like, on the planet.

1%? .5%? .01% I don’t know the number, but it’s extraordinarily small. We’ve got 8 billion people now. How many startups are there? How much music is there? How many Hollywoods are there? How many Taylor Swifts? How many Kendrick Lamars? How many Elons? How many Satya Nadellas?

Too few. And here’s the important question. Why? Why so few?
Part of the answer is that talent matters, and intelligence matters, and creativity matters. Sure. Agreed. But how many people have similar capabilities to these people but don’t have the time or the tools to do anything with them?

Again, I don’t know the answer to that, but I’m betting it’s vast. Not hundreds of people. Not thousands. Millions.

But they can’t go to a studio. They can’t talk to their producer friends and get a break. They don’t have an art table to work on. They don’t have a beat machine.

AI is about to change that. We’re about to remove many of the advantages that Steven Spielberg has over Takashi Noshimira, who lives in a small rural town in Japan, who is a creative genius. With these new models coming out, with the ability to create music, create video, create screenplays, create scripts, etc—we’re about to equalize the playing field massively.





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Thursday, May 25, 2023

Working in the penumbra of the rules


Process and methodology are the name of the game in projects
We all know that
Select a methodology and it undoubtedly comes with its rules, procedures, gates, decision points, you name it ....

Any worthy methodology also comes with a historical record: what works; what doesn't; compromises; deviations; and exceptions, of course.

Take in all the dots
So, that's a lot of dots: all the methodology dots and all the historical record dots. What if, when you stand back a bit and look strategically at the sum or integration of the dots, you find grey areas where principles and tactics seem to conflict? You may even draw conclusions about the process that were only apparent when you took the integrated view.

Greater than the sum:
Another way to see this: a strategic view of the dots is a not that different from the concept that the "sum is greater than than the parts". And so the "uplift" is found in the shadow of the dots.

Penumbra workings
Using, applying, or working the stuff in the shadow of the main dots is what many call "working in the penumbra", where a penumbra as we know is the shadow area behind or adjacent to something more opaque. 

Working in the penumbra is working with conflicts, competitions, and perhaps choices that we really don't want to make: personal, cultural, technical, quality, schedule, scope trades and choices that are hard and not really win/win.  

But there can be an upside: You may discover an entirely new way forward to a better outcome if only you are allowed to use some of the outcomes and conclusions which are "off the beaten path" or even on a path formerly denied.

Some even go so far as to say that new process emanates from what can be seen in the penumbra of the main process and methods. If so, you might say that penumbra emanations are tantamount to an emergent outcome.






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Friday, May 19, 2023

Grasping a big number


We don't think about numbers linearly
That is, as numbers get large, their "largeness" kind of flat-lines in our thinking, like a logarithmic scale (*)

Grasp the future
We can't grasp the future very well either when it is really far removed from the present. 
Daniel Miessler posted this bit of schedule data in a recent blog:
The difference between a million and a billion is counter-intuitively large.

As an example, a million seconds is 12 days, and a billion seconds is 

32 years.
I had to look it up to confirm. That's bonkers.

Risk management
The phenomenon of a flattening of the future possibilities affects risk perception big time. 
If you're trying to finance your project, "2 year money" is more expensive than "10 year money". And "90 day" money is more expensive than either of those. 
Why?
The "cone of uncertainty".

We can 'accurately' gauge the next few weeks, perhaps even a year is not too far off. 
The finance guys think the near-term risks are more certain and more expensive than those we can't really forecast (so we fall back on historical models)
 
Just think about how the 'cone of uncertainty' widens as we view the future, but is it wide enough? Can we really bring our mind to consider the limiting case?

A million deaths in U.S. from COVID; no one would have accepted such an outlandish forecast of the cone of uncertainty when it all began.

"They say" that one really bad mistake is tragedy, but a thousand similar mistakes is just a lot of mistakes. We just can't muster the impact of one mistake multiplied up to that of a thousand.

And so it's generally understood that we view the future more optimistically that the present (there's time to fix things), or we have a bias towards optimism, as we cannot grasp the extent of the risk possibilities, even as someone gives us a number (one billion seconds: the mind numbs; there's no grasp of reality). Climate science is a present example.

+++++++++++++++++
(*) A log scale is plot of exponents, rather than that of the base number. As an example, the base number might go 10, 100, 1000 and be plotted as an ever-extending curve; but on a log scale, the plot of exponents is 1, 2, 3, and the plot would be linear, just a straight line. So whereas a phenomenon might change by a 1000:1, in our mind's eye we might flatten that to 3:1.  





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Tuesday, May 16, 2023

Don't ask me about the worst case


Perhaps the most common question in risk management, if not in project management is this one:
"What's the worst case that can happen?"
Don't ask me that!
Why not?
Because I don't know, and I'd only be guessing if I answered.

So what questions can you answer about the future case?
  • I can tell you that a projection of the past does not forecast the future because I've made the following changes (in resources, training, tools, environment, prototypes, incentives, and .....) that nullify prior performance ......

  • I can tell you that I can foresee certain risks that can be mitigated if I can gather more knowledge about them (Such being a Bayesian approach of incrementally improving my hypothesis of the future outcomes). So, I have the following plan to gather that knowledge ......

  • I can tell you that there are random effects over which I have no control and for which there is no advancement in knowledge that will be effective. These effects could affect outcomes in the following ways ......

  • I can tell what you probably already know that the future always has a bias toward optimism (there's always time to fix it), and that there's always a tactical bias toward "availability" (one in hand is worth two in the bush ....) even if what's available is suboptimum.
Heard enough?
So go away and let me work on all that stuff!




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