Agile Money

In a recent conversation with colleagues we were debating the merits of using story point velocity as a metric for team performance and, more specifically, how it relates to determining a team’s predictability. That is to say, how reliable the team is at completing the work they have promised to complete. At one point, the question of what is a story point came up and we hit on the idea of story points not being “points” at all. Rather, they are more like currency. This solved a number of issues for us.

First, it interrupts the all too common assumption that story points (and by extension, velocities) can be compared between teams. Experienced scrum practitioners know this isn’t true and that nothing good can come from normalizing story points and sprint velocities between teams. And yet this is something non-agile savvy management types are want to do. Thinking of a story’s effort in terms of currency carries with it the implicit assumption that one team’s “dollars” are not another team’s “rubles” or another teams “euros.” At the very least, an exchange evaluation would need to occur. Nonetheless, dollars, rubles, and euros convey an agreement of value, a store of value that serves as a reliable predictor of exchange. X number of story points will deliver Y value from the product backlog.

The second thing thinking about effort as currency accomplished was to clarify the consequences of populating the product backlog with a lot of busy work or non-value adding work tasks. By reducing the value of the story currency, the measure of the level of effort becomes inflated and the ability of the story currency to function as a store of value is diminished.

There are a host of other interesting economics derived thought experiments that can be played out with this frame around story effort. What’s the effect of supply and demand on available story currency (points)? What’s the state of the currency supply (resource availability)? Is there such a thing as counterfeit story currency? If so, what’s that look like? How might this mesh with the idea of technical or dark debt?

Try this out at your next backlog refinement session (or whenever it is you plan to size story efforts): Ask the team what you would have to pay them in order to complete the work. Choose whatever measure you wish – dollars, chickens, cookies – and use that as a basis for determining the effort needed to complete the story. You might also include in the conversation the consequences to the team – using the same measures – if they do not deliver on their promise.


Photo by Micheile Henderson on Unsplash

Root Causes

The sage business guru Willie Sutton might answer the question “Why must we work so hard at digging to finding the causes to our problems?” by observing “Because that’s where the roots are.”
Digging to find root causes is hard work. They’re are rarely obvious and there’s never just one. Occasionally, you might get lucky and trip over an obvious root cause (obvious once you’ve tripped over it.) Most often, it’ll require some unknown amount of exploration and experimentation.

Even so, I’ve watch as people work very hard to avoid the hard work needed to find root causes or fail to acknowledge them even when they are wrapped around their ankles. It’s an odd form of bikeshedding whereby the seemingly obvious major issues are ignored in favor of issues that are much easier to identify, explain, or understand.

One thing is certain, you’ll know you’ve found a root cause when one of two things happen: You implement a change meant to correct the issue and a whole lot of other things get fixed as a result or there is noisy and aggressive resistance to change.

Poor morale, for example, is often a presenting symptom mistaken for a root cause. The inexperienced (or lazy) will throw fixes at poor morale like money, happy hours, or other trinkets. These work in the very short term and have their place in a manager’s toolbox, but eventually more money becomes the new low pay and more alcohol has it’s own very steep downside.

Morale is best understood as a signal for measuring the health of the underlying system. Poor morale is a signal that a whole lot of things are going wrong and that they’ve been going wrong for an extended period of time. By leveraging a system dynamics approach, it’s relatively easy to make some educated guesses about where the root causes may be. That’s the easy part.

The hard work lies with figuring out what interventions to implement and determining how to measure whether or not the changes are having the desired effect. A positive shift in morale would certainly be one of the indicators. But since it is a lagging indicator on the scale of months, it would be important to include several other measures that are more closely associated with the selected interventions.

There are other systemic symptoms that are relatively easy to identify and track. Workforce turnover, rework, and delays in delivery of high dependency work products are just a couple of examples. Each of these would suggest a different approach needed to resolve the underlying issues and restore balance to the system dynamics behind a team or organization’s performance.

Concave, Convex, and Nonlinear Fragility

Nassim Nicholas Taleb’s book, “Antifragile,” is a wealth of information. I’ve returned to it often since first reading it several years ago. My latest revisit has been to better understand his ideas about representing the nonlinear and asymmetric aspects of fragile/antifragile in terms of “concave” and “convex.” My first read of this left me a bit confused, but I got the gist of it and moved on. Taleb is a very smart guy so I need to understand this.

The first thing I needed to sort out on this revisit was Taleb’s use of language. The fragile/antifragile comparison is variously described in his book as:

  • Concave/Convex
  • Slumped solicitor/Humped solicitor
  • Curves inward/Curves outward
  • Frown/Smile
  • Negative convexity effects/Positive convexity effects
  • Pain more than gain/Gain more than pain
  • Doesn’t “like” volatility (presumable)/”Likes” volatility

Tracking his descriptions is made a little more challenging by reversals in reference when writing of both together (concave and convex then convex and concave) and mis-matches between the text and illustrations. For example:

Nonlinearity comes in two kinds: concave (curves inward), as in the case of the king and the stone, or its opposite, convex (curves outward). And of course, mixed, with concave and convex sections. (note the order: concave / convex) Figures 10 and 11 show the following simplifications of nonlinearity: the convex and the concave resemble a smile and a frown, respectively. (note the order: convex / concave)

Figure 10 shows:

So, “convex, curves outward” is illustrated as an upward curve and “concave, curves inward” is illustrated as a downward curve. Outward is upward and inward is downward. It reads like a yoga pose instruction or a play-by-play call for a game of a Twister.

After this presentation, Taleb simplifies the ideas:

I use the term “convexity effect” for both, in order to simplify the vocabulary, saying “positive convexity effects” and “negative convexity effects.”

This was helpful. The big gain is when Taleb gets to the math and graphs what he’s talking about. Maybe the presentation to this point is helpful to non-math thinkers, but for me it was more obfuscating than illuminating. My adaptation of the graphs presented by Taleb:

With this picture, it’s easier for me to understand the non-linear relationship between a variable’s volatility and fragility vs antifragility. The rest of the chapter is easier to understand with this picture of the relationships in mind.