Why Estimates are Needed for Success in the Nondeterministic Universe

Some use a fallacy in the software?development business that?estimates are unnecessary to make decisions in the presence of uncertainty. Of course, this can only be true if our world is?deterministic.?

But we don't live in a deterministic world, we live in a non-deterministic world. Determinism was debunked long ago. But that determinism is a house built on sand. The originators?of determinism?didn't know it.

“There is no doubt that a man could make a machine which was capable of walking around a town for a time and of turning precisely at the corners of certain streets. And an incomparably more perfect, although still limited, mind could foresee and avoid an incomparably greater number of obstacles. And this being so, if this world were, as some think it is, only a combination of a finite number of atoms that interact following mechanical laws, it is certain that a finite mind could be sufficiently exalted as to understand and predict with certainty everything that will happen in a given period. This mind could make a ship capable of getting itself to a certain port by first giving it the route, the direction, and the requisite equipment, and it could also build a body capable of simulating a man.” — Gottfried Wilhelm Leibniz (1702)

Of course,?Leibniz didn't have the math to support his position,?which came later in the form of differential equations. But Laplace's statements are the foundation of classical mechanics.

For each?system in classical mechanics, there are equations of motions of the form:?

Which has a unique solution

Before moving on, the?phrase about a?machine capable of walking around town has come to pass. So now what about determinism?and?the machine.

Of course, the?machine in the video couldn't take a step without?falling if it didn't have a?probabilistic feedforward adaptive closed-loop control system.?Closed-loop control systems actively control the?system based on state feedback. Open-loop control systems execute?a fixed sequence of control inputs without any feedback. Here's a nice paper as an example of control systems, "A Probabilistic Approach to Mixed Open-loop and Closed-loop Control, with Application to Extreme Autonomous Driving."

Putting this to work on Projects?

In our domain, the role I usually participate?in is called?Program Planning and Controls. These planning and control activities always take place in the presence of uncertainty, which, of course, creates?risk. The presence of uncertainty?makes the system?indeterminate. And, of course, to manage in the presence of uncertainty and the resulting risks, we need specific processes and practices based on principles.?

Since all project work operates in the presence of uncertainty - reducible and irreducible - and the managers of these projects need to make decisions in the presence of these uncertainties, we need to make estimates to inform our decision-making process.

Here's an example of the probability distribution of a cost estimate for a project.

The 50th?percentile cost is $2,296,898. That means there is a 50/50 chance the project will cost more than that or less than that. That number is the median - the middle of the range. If we want to know the number with an 80% confidence, it is $2,333,153. That says there is an 80% confidence that the project cost will be $2.3M or less.

When we speak about the schedule, we can use the same terminology. In this case, there is an 80% confidence that the project will be completed?on or before?11/06/2015.?

These graphs are generated from a?Monte Carlo simulation?tool applied to a resource-loaded?Integrated Master Schedule?(IMS). If there is any doubt as to why you?MUST?create a resource-loaded schedule, please please put those doubts away. These two graphs are the source for the Joint Confidence Level, showing the overall probability distributions for both cost and schedule for the project.

This picture should convince anyone that the "joint" probability of completing?on or before?the target date (I didn't say what that was) and?at or below?that planned budget (didn't say what that was either) needs to be modeled in a way that would be considered?credible.?

Outside of a small domain of trained and experienced risk analysts, this is rarely, if ever, done. Most IT projects have some?made-up?budget and schedule. Their strategy is based almost entirely on?HOPE?with no underlying assessment of the statistical and probabilistic?drivers?of the cost and schedule, let alone the technical aspects coupled with risk.

And we wonder why IT projects come in late and over budget?

Of course, large IT? programs have the same problem - poor estimating, politically motivated estimating, naive estimating, etc. But they must produce data like this, so we know it is bogus sometimes early enough to fix it - sometimes not.

We only wish it were as simple as many would have us believe. But sadly, it is one of those?wicked problems?we must manage in the presence of uncertainty.

But no credible decision can be made in the presence of uncertainty without making an estimate of the outcome of that decision.