When we hear about making decisions in the absence of estimates of the impact from that decision, the cost of making that decision - the opportunity cost, which is the basis of Microeconomics, or best of all the possible alternatives that might result from that decision - the opportunity costs - we need to stop and think.

Is it actually possible to make a decsion without knowing these things?

The answer is NO. But of course, the answer is also YES. Since decisions can't be made in the absence of those estimates. They are made all the time. A little joke in the extreme sports domain, which our son participates in, goes like this.

*What are the last four words uttered by our 22-year-old son backcountry skier in Crested Butte before arriving at the emergency room?*

Hey everyone watch this!

Any estimating the probability of clearing the 20-foot gap? Oh Hell No, let's go...

The decision-making process here is the same as the decision making process on projects. There are uncertainties that create risk. There are uncertainties that are irreducible and there are uncertainties that are reducible. Risk of crashing and breaking your collarbone. Risk of *crashing the project* and breaking the bank or breaking the customer relationship.

Both these uncertainty types must be addressed if the project has a chance of success, just as both uncertainty types need to be addressed if there is a chance of *landing the jump without breaking something in a very painful way*.

There are a set of environmental conditions on the project and on the slopes that are helpful and as well as a hindrance to success. Modeling those is the starting point for making the decision to *proceed*. This is the taxonomy of uncertainty that must be assessed before proceeding with the decision.

If you're 22 years old and believe you're immortal, then assessing these risks is rarely necessary. It's the *let's just try this* view of the world. After breaking both collar bones (separate occasions), crashing mountain bikes as well as crashing on skis and being transported down the mountain in a Ski Patrol Sled, feedback prevails and a more mature assessment of the outcome results.

The word *uncertainty* has a variety of meanings and has a variety of synonyms: error, information, vagueness, scatter, unknown, discord, undefined, ambiguous, probability, stochastic, distribution, confidence, and chance. These create confusion and from the confusion the opportunity to ignore them.

**To evaluate the outcomes of our decisions, we need data**

This data comes from a *model of the world *that allows us to translate our observations into information. In this model, there are two types of abstraction. Aleatory and Epistemic. Aleatory implies inherent *randomness* of the outcomes of the process subject to our decision making. Flipping a coin is modeled as an aleatory process, as is rolling dice. When flipping the coin, the *random* but observable data is the result. Since the underlying probability function for flipping the coin has no observable quantities (we can see all the processes that go into holding the coin, flipping with our fingers, the air, the rotation of the earth, etc.) but we can't model the world of coin-flipping directly. Instead, we can only observe the results from that model.

This is many times the case on projects. The underlying physical processes, which themselves may be deterministic, can't be observed. So all we get is the probability that an outcome will occur. This is a Bernoulli model of the process.

*A Bernoulli trial is an experiment outcome that can be assigned to one of two possible states (e.g., success / failure, heads / tails, yes / no). The outcomes are assigned to two values, 0 and 1. A Bernoulli process is obtained by repeating the same Bernoulli trial, where each trial is independent. If the outcome assigned to the value 1 has probability p, it can be shown that the summation of n Bernoulli trials is binomial distributed.*

The Epistemic uncertainty of the processes, both slopestyle skiing and projects, represents how precise our state of knowledge is about the *world model*. We can measure the temperature of the snow, we can measure the performance of the database server. We know the wind speed at the top of the *kicker*, we know the density of the defects on the codebase from our last quality assurance review.

The epistemic uncertainty of the process pertains to the degree of knowledge of the model and its parameters. Epistemic comes from the Greek word *episteme* (knowledge).

**We Need Aleatory and Epistemic information to make a decision**

The system we want to make a decision about has a reality that can be modeled in some way, to some degree of confidence. When it is suggested otherwise, that simply is not true. Only when *Black Swans* are introduced - the Unknown Unknowns are applied - does this model not work. In the project world, those UNK UNKs mean one of three things:

- We couldn't know - it was a surprise. We didn't understand our
*world model*. - We didn't know - we didn't have enough time or money to find out, or we were simply too lazy to find out. The
*world model*was understandable, but we didn't look hard enough. - We don't what to know -
*let's just try it and see what happens*. We know the*world model*, but don't want to acknowledge the consequences.

This last situation is best represented In the famous Rumsfeld quote about UNKUNKs he failed to read *The Histories*, by Herodotus, (484-ca. 425 BCE), who cautioned not to go into that part of the world and engage in a ground war. It turned out bad for Alexander the Great.

So if you're the sort that accepts that decisions can be made in the absence of estimating the cost and impact of that decision - you're in the last two categories.

**A Final Thought**

When it is suggested that businesses are seeking *deterministic* or *predictable* outcomes - which of course they are not, not can they since all business processes are probabilistic - those processes exist in only a few domains

Such precise processes are the antitheses of aleatory., this is the type of model most familiar to

scientists and engineers and include relationships such as E=mc^{2}, F=ma, F=((G × m_{1 }× m_{2}) / r_{2})._{ } So if you work with classical mechanics or the like, you can look for predictability. But if you work in the *real world* of projects or the business of projects - ** All The World's a Random Process** - behave accordingly.