Archive for the ‘economic indifference’ Category

Fluctuating Tails II

November 22, 2015

In my last post, “Fluctuating Tails I,” we explored the effects of a black swan on a single normal distribution. In this post, we will look at a the effects of a black swan on a multinormal distribution from the perspective of a linear regression.

Lets start off with the results of a linear regression of multidimensional data. These regressions give rise to ellipse containing the multidimensional data. This data also gives rise to many normal distributions summed into a multinormal distribution.

 Black Swan Imacts on Multivarite Distribution 1

I modified the underlying figure. The thick purple line on the distribution on the p(y) plane represents the first black swan. The thin purple line projects the black swan across the ellipse resulting from the regression. The data to the right of the black swan is lost. The perpendicular brown lines help us project the impacts on to the distribution on the p(x) plane. The black swan would change the shape of the light green ellipse, and it would change the shape of the distribution, shown in orange, on the p(x) plane.

In the next figure, we draw another black swan on the p(y) plane distribution further down the tail. We use a thin black line to represent the second black swan. This black swan has fewer impacts.

Black Swan Imacts on Multivarite Distribution 4

In neither of these figures did I project the black swan onto the p(x) plane distribution, or draw the new x’ and y’ axes as we did in the last post. I’ll do that now.

Black Swan Imacts on Multivarite Distribution 3

Here we have projected the black swan and moved the x and y axes.

Notice that the black swan is asymmetrical, so the means of the new distributions would shift. This means that any hypothesis testing done with the distributions before the black swan would have to be done again. Correlation and strength tests depend on the distance between the means of the hypotheses (distributions).

Parameter Distributions

After drawing these figures, I went looking for Levy flight parameters. I wanted to show how a black swan would affect pumps in a Levy random walk. I settled instead on a Rice distribution.

 Rice Distribution

The shades of blue in the figure are the standard deviations of sigmas from the mean. Sigma is one parameter of the Rice distribution. V is another.

Rice Distribution Parameters

Here are the PDFs and CDFs of a Rice distribution given the relevant parameter values. The blue vertical line through both of the graphs is an arbitrary black swan. Some of the distributions are hardly impacted by the black swan. A particular distribution would be selected by the value for the parameter v. The distributions would have to be redrawn after the black swan to account or the change in the ranges of the distributions. Once redrawn, the means would move if the black swan was asymmetrical. This is the case for the Rice distribution and any normal distributions involved.

If the parameters themselves were distributions, a black swan would eliminate parameter values and the distributions for those parameter values.

When we base decisions on statistical hypothesis testing, we need to deal with the impacts of black swans on those decisions.


Economic Indifference, Part III

March 25, 2009

Val Workman’s response to Monday’s post on this subject was

That Economic Indifference is the sum of product management activities is an unsupported premise

The tweet stream mentioned how P&L would come to constrain product management. Agilists feel that product management constrains them. There is a lesson in these claims. That lesson is what I’ve been exploring with these last three posts.

I could talk about how models or abstractions leave details out. Or, how metrics and their roll ups amount to one big model-view-controller, or a chain of model-view-controllers. But, that would be just another near theoretical discussion. So let’s get real for a moment. Economic indifference is a theory, as well, so I’ll happily take this down to the dirt.

On day in April, years ago, something short of two weeks from my next annual vesting–no I wasn’t the product manager back then, but I did work for some great product managers back then–we were all called to a company-wide meeting. Once we were all in the room, they let us know what it was all about.

“The trading window has been closed.” Maybe they didn’t say this, because it was already closed, since we were close to the end of the quarter anyway. I’ll just remember it this way, not wanting to talk about the technicalities.

“We missed the quarter!” If this has happened to a company you’ve worked for, well then you’ve met economic indifference personally, so it isn’t theorectical anymore.

“There will be an analyst call at 1 pm this afternoon. The details will be in an email we’ll send out after the meeting.”

There must have been some talk about staying positive, and staying on board, but no excuses were made, and no explanations offered.

So you get that email, close your office door, and dial in. The analyst call was exactly the same, and no explanation was given either. They told the analysts exactly what they told you. They asked the analysts to wait for the quarterly, so they had more time to analyze what happened.

This happened after the dot bust had started, but the dot bust wasn’t the cause.

At any rate, the bit representing the answer to the question “Did you miss your quarterly estimate?” It went from no to yes. That one bit, summed up all the efforts of everyone in the company. That one bit changed the world.

When the world was told about the impending analyst call, the institutional traders and their automation dumped the stock as fast as they could. After the call, those analysts downgraded their rating from a Buy to a Hold, as a minimum. Most of the analysts downgraded us to a Sell. Once they did this, the retail market had gotten the word.

Say whatever you want, but an organization’s performance as an safe place to put and multiply money is summed up in that one little bit, the answer to a single simple question. An investor never asks, “hey, do they have product managers?”

We know that we have much to do as a product manager, but at the end of the quarter, our P&L is our grade, our few little bits that answer a few little questions. If we miss our objective, it rolls up to corporate, and the financial markets will have their bits for their questions. Excuses will be made, but no truth will be told. It isn’t about truth. It isn’t about narrative. It isn’t about effort. It’s about missing the quarter. It’s about that one little bit.

Hopefully, you’re not a one product company, but even so, you share that ultimate roll up with your peers, all the other managers, and your superiors. In the end, everyone in the company contributes, and everyone is rewarded, or not.

But, if economic indifference was just a way to get graded, I wouldn’t have written anything about it. It’s really the means by which we do much to ensure success of our endeavours. And, it is the means by which we enable those who contribute to our success, because they do much, as well, that we are never asked about. It’s about the degrees of freedom that the roll up grants us, and the degrees of freedom that our team’s roll ups grants them.

It’s really about how we are driven, and how we drive.

Economic Indifference II

March 24, 2009

Back on my now inaccessible blog, I talked about the need for product managers to practice economic indifference. I used the mathematics of manifolds to show how decisions make at one level of a system can be independent to those made a another level. I say “can” here because the decisions made at subordinate levels need to be consistent with the constraints placed on them by those above them. But, there is a flip side. That being enablers provided by those above them. Economic indifference is one such enabler.

When I tell you to build me a car, you can build any car. I can’t come back and say, I want a truck. Nor, can I come back and say I want a red car. My specification error is my problem, not yours. I left you the freedom, and you took it. My bad. Actually, it’s my bad for now wanting something more specific than what I had specified. The granuality of my spec dictates what I will have to accept, or put in different terms the amount of economic indifference I have to accept. Or, from the perspective of the developer, the degrees of freedom I left them. And, Agilists want as many degrees of freedom that they can get.

That said, today I’ll use the mathematics of vectors, to talk about many things around product management.

The Basic Vector

The Basic Vector

A vector! What does a vector have to do with business? Well, strategy, vision, forecast, capabilities, processes, projects, competition, and ultimately economic indifference.

From Here to There

From Here to There

When we need to figure out where a shop is in the mall, or which bus to take, we look for a map, and hopefully, it has a You Are Here indicator. Then, we look for our objective. The map provides us with a convoluted path to take to get there. We could draw a vector on the map, an as the bird flies view, but it doesn’t help much on the ground. That vector would be representative of our economic indifference. The shops along the way might catch our attention, we might get sucked in, we might buy something, we might have to take that something to the car, or grab a bite to eat before we finally arrive at the pointy end of the vector, our destination. Why did I come here, we wonder. Ah, Saturday, and I’m not at work? What’s wrong with me, playing hooky. The team….

Getting There--How One Vector Summarizes a Bunch of Vectors

Getting There--How One Vector Summarizes a Bunch of Vectors

Our path around the mall is shown in red. We decompose our objective vector into a collection of vectors. That collection of vectors get us there. That collection of vectors is closer to actuals, or the reality, and a little less economically indifferent than our objective vector.

Decomposed Yet Again, More Vectors

Decomposed Yet Again, More Vectors

 Leaving our mall example behind, we have a business objective (black), and we have these capabilities, resources, and staff (red) to get us to the our goal. The goal incorporates the matter of time into the vector. We arrive at a time and place. But, one of the capabilities is just a plan. It’s realization shows up as another collection of vectors (blue.) The red vectors represent ongoing operational capabilities. The blue vectors represent a project that will put the process into operation. We don’t have that capability right now, so its a risk.

How did we determine our goal and draw the objective vector? We did our forecast.

Forecast Factors as a Collection of Vectors

Forecast Factors as a Collection of Vectors

You build a forecast with metrics that show where you’ve been over time, and where you expect to be over time. It might be a set of rates. Each forecast factor heads off in its own direction.

Vectors Collected, a Morpheme

Vectors Collected, a Morpheme

We can bring move the vectors around, so that they all start at the same point. Oddly enough, it starts to look like a morpheme, the meaning consitutents of words. Just an aside.

From Forecast Vectors to Strategy Vector

From Forecast Vectors to Strategy VectorHere I've added all the forecast vectors together to arrive at my strategy vector. I did not weight the forecast vectors. Strategy as an Organized Collection of Capabilities

Remember that the forecast variables were derived from time series data. A time series assumes that the policy basis and capabilitity basis haven’t changed. If they change, the old numbers become unreliable. When you build an estimation database, you end up creating an estimate based on averages. You are estimating your existing capabilities. The red vectors represent those capabilities. If our strategy was a real strategy, the red vectors would converge on the black vector not at the arrow, because you would divide the strategy into timeframes.
Strategies are supposed to be long-term propositions. Capabilities are created to support them. The capabilities persist for a long time. They may persist longer than the strategy that birthed them. Those capabilities improve over time. At the same time the capabilities become constraints on strategy.
Vision is not based on a forecast, or on a collection of capabilities. With a vision, you point off in a direction, and build the capabilities to get there. A product might be a strategy where it’s improvements would be linear, or in otherwords sustaining or continous. A product might be a vision where it’s improvements would be non-linear, radical, or discontinous. A vision departs strategy, and costs quite a bit more than doing the same old same old day in and day out.
Strategy (Yesterday) and Vision (Tomorrow)

Strategy (Yesterday) and Vision (Tomorrow)

Vision requires us to let go of the prior strategy and get with the new program. You might run into a vision when you get a new CEO. You will run into a vision if you ever face a market transition as described by Moore’s technology adoption lifecycle. It amounts to leaping into the new boat. The old boat is sinking. Well, maybe not yet, but its sinking is anticipated.
Transition to Vision

Transition to Vision

As you move to the new vision, you will use existing capabilities (red), reduce existing capabilities (gray), enhance existing capabilities (green), and add capabilities and processes through projects.
Moving your product to a new market, or extending your existing market would require the same kinds of efforts. Your product is a vector.
In the past few weeks, there has been a lot of discussion about fast followers, the lost of differentiation and price premiums, and commoditization. When your customers will no longer pay for additional capabilities you’ve incorporated into your product, you have been commoditized. Some customers may continue to pay, but never use those additional capabilities. In this case, your customers are overserved, and you can find yourself being attacked by a new entrant with a product based on a new technology. That entrant might not meet your performance threasholds, because they compete on other drivers. All of this involves messing with our vectors.
Commoditization forces you to find new drivers, new vectors of differentiation.
Fighting a fast follower depends on proprietary technologies or slight of hand.
Routes to a Feature

Routes to a Feature

A feature can be created quickly (red). It will be thin. It might involve adding a dialog, a button or menu option, a column or two to your database tables, and a few computations. The same feature can be create richly (blue). You do this to explore future opportunites, and to build things that don’t show up in the interface, or in the reverse engineering efforts to capture behavior. If SaaS does anything for us, it removes executable code from the hands of our competitors, so reverse engineering is about what is seen under testing at the interfaces. You might not expose the APIs for some of the depth you’ve created in your rich project. Expose just enough to make it look quick. Then, when the fast follower releases their catch up, you release the next layer to exposure.
That exploration might increase the conceptual surface area or create a much richer conceptual geography for later exploitation. Let them follow. You can keep on rolling out premium. Maybe you’ll teach them to slow down, or look deeper. “Now eating time at the lunch counter.”
You could also take a vector view of your team. You could use those n-dimensional charts in Excel to map our the abilities of your team members, their estimation factors, your influence with each of them, and your communications effectiveness with each of them. You could then add their morpheme views together to form words if you will, which add up the vectors to a team score or vector.
That might be a bit much, but much is possible with a good representation of the problem of shipping on time and hitting your P&L numbers.
Getting distance from the details grants economic indifference. The big vector doesn’t care, but the comprising vectors determine the success of the big vector.
And, for all those product managers who want the stick, instead of the hard work of building influence, one last vector veiw.
The Stick and the Goal

The Stick and the Goal

When  hit with a stick, the capability leaves with some portion of your gantt chart. Congrats! Forget the stick.

The Blog, Reading, Previous Post on Economic Indifference

March 23, 2009