Decision Making in the Presence of Uncertainty

Decision theory is concerned with the problem of making decisions. Statistical decision theory is decision making in the presence of statistical knowledge, by understanding some of the uncertainties involved in the problem.

Decision theory deals with the situations where decisions have to be made in the presence of uncertainty, and its goal is to provide a rational framework for dealing with such situations. To make good choices we must calculate and manage the resulting risks from those choices. Today, we have the tools  to perform these calculations.

A few hundred years ago decision making in the presence of uncertainty and the resulting risk had only tool faith, hope, and guesswork. This is because risk is a numbers game. Before the 17^th century, our understanding of numbers did not provide us with the tools needed to make choices in the presence of uncertainty.

A good book about the history of making choices in the presence of uncertainty - risk management - is Against the Odds, The Remarkable Story of Risk, Peter Bernstein. These efforts culminated in Bernoulli's focused not on probabilistic events, but on the human beings who desire or fear certain outcomes to a greater or lesser degree.

Bernoulli showed how to create mathematical tools to allow anyone to “estimate his prospects from any risky undertaking in light of [his] specific financial circumstances.” The is the basis of Microeconomics of decision making, in which the opportunity cost of a collection of choices can be assessed by estimating both the cost of that decision and the result beneficial outcome or loss.

In 1921, Frank Knight distinguished between risk, when the probability of an outcome is possible to calculate — or is knowable — and uncertainty, when the probability of an outcome is not possible to determine — or is unknowable.

This becomes an argument that rendered insurance attractive and entrepreneurship tragic.  20 years later, John von Neumann and Oskar Morgenstern established the foundation of game theory, which deals in situations where people’s decisions are influenced by the unknowable decisions of live variables — in the gaming world, this means other people.

Decision making in the presence of uncertainty is a normal business function as well as a normal technical development process. The world is full of uncertainty.

Those seeking certainty will be woefully disappointed. Those conjecturing that decisions can't be made in the presence of uncertainty are woefully misinformed. 

Along with all this woefulness is the boneheaded notion that estimating is guessing and that decisions can actually be made in the presence of uncertainty in the absence of estimating.

Here's why. When we are faced with a decision, a choice between multiple decisions, a choice between multiple outcomes, each is probabilistic. If it were not - that is we have 100% visibility into the consequences of our decision, the cost involved in making that decision, the cost impact or benefit impact from that decision - it's no longer a decision. It's a choice to pick between several options based on something other than time, money, or benefit.

Buying an ERP system, or funding the development of a new product, or funding the consolidation of the data center in another city is a much different choice process than picking apples. These decisions have uncertainty. Uncertainty of the cost. Uncertainty of the benefits, revenue, savings, increasing in reliability and maintainability. Uncertainty in almost every variable. 

Managing in the presence of uncertainty and the resulting risk, is called business management. It's also called how adults manage projects (Tim Lister)

The Presence of Uncertainty is one of the most Significant Characteristics of Project Work

Managing in the presence of uncertainty is unavoidable. Ignoring this uncertainty is also unavoidable. It's still there even if you ignore it. Uncertainty comes in many forms

• Statistical uncertainty - Aleatory uncertainty, only margin can address this uncertainty.
• Subjective judgment - bias, anchoring, and adjustment.
• Systematic error - lack of understanding of the reference model.
• Incomplete knowledge - Epistemic Uncertainty, this lack of knowledge can be improved with effort.
• Temporal variation - instability in the observed and measured system.
• Inherent stochasticity - instability between and within collaborative system elements

So Back To the Problem at Hand

 

If decisions - credible decisions - are to be made in the presence of uncertainty, then somehow we need the information to address the sources of that uncertainty in the bulleted list above.

This information can be obtained through many means. Modeling, sampling, parametrically, past performance, reference classes. Each of these sources has in itself an inherent uncertainty. 

So in the end, it comes done to this...

 

To make a credible decision in the presence of uncertainty, we need to estimate the factors that go into that decision.

We Need To Estimate

 

There's no way out of it. We can't make a credible decision of any importance without an estimate of the impact of that decision, the cost incurred from making that decision, the potential benefits from that decision, the opportunity cost of NOT selecting an outcome from a decision.

Anyone suggesting we can make decisions in the absence of estimating needs to provide clear, concise, actionable information with examples of how this can be done in the presence of uncertainty created by he underlying statistical processes of project work and the resulting probabilistic outcomes of those processes.

 

If you have certainty, you don't need to estimate. Measure your empirical performance to date, using the Most Likely value from that performance and the variance project the future performance. Ignore risk, ignore naturally occurring (aleatory), and event-based (epistemic) uncertainties in the underlying processes and proceed to spend your customers' money by applying faith, hope, and guesswork, just like they did before the 17^th century.

 

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