In our consulting work we have periodically been asked to review how judgments or decisions of a particular kind are made within an organisation, and to recommend improvements.  This has taken us to some interesting places, such as the rapid lead assessment center of a national intelligence agency, and recently, meetings of coaches of an elite professional sports team.

On other occasions, we have been asked to assist a group to design and build, more or less from scratch, a process for making a particular decision or set of decisions (e.g., decisions as to what a group should consider itself to collectively believe).

Both types of activity involve thinking hard about what the current/default process is or would be, and what kind of process might work more effectively in a given real-world context, in the light of what academics in fields such as cognitive science and organisational theory have learned over the years.

This sounds a bit like engineering.  My favorite definition of the engineer is somebody who can’t help but think that there must be a better way to do this.  A more comprehensive and workmanlike definition is given by Wikipedia:

Engineering is the application of scientific, economic, social, and practical knowledge in order to invent, design, build, maintain, research, and improve structures, machines, devices, systems, materials and processes.

The activities mentioned above seem to fit this very broad concept: we were engaged to help improve or develop systems – in our case, systems for making decisions.

It is therefore tempting to describe some of what we do as decision engineering.  However this term has been in circulation for some decades now, shown in this Google n-gram:


and its current meaning or meanings might not be such a good fit with our activities.  So, I set about exploring what the term means “out there”.

As usual in such cases, there doesn’t appear to be any one official, authoritative definition.  Threads appearing in various characterizations include:

While each such thread clearly highlights something important, my view is that individually they are only part of the story, and collectively are a bit of a dog’s breakfast.  What we need, I think, is a more succinct, more abstract, and more unifying definition.  Here’s an attempt, based on Wikipedia’s definition of engineering:

Decision engineering is applying relevant knowledge to design, build, maintain, and improve systems for making decisions.

Relevant knowledge can include knowledge of at least three kinds:

  • Theoretical knowledge from any relevant field of inquiry;
  • Practical knowledge (know-how, or tacit knowledge) of the decision engineer;
  • “Local” knowledge of the particular context and challenges of decision making, contributed by people already in or familiar with the context, such as the decision makers themselves.

System is of course a very broad term, and for current purposes a system for making decisions, or decision system, is any complex part of the world causally responsible for decisions of a certain category.  Such systems may or may not include humans.  For example, decisions in a Google driverless car would be made by a complex combination of sensors, on-board computing processors, and perhaps elements outside the car such as remote servers.

However the decision processes we have worked on, which might loosely be called organisational decision processes, always involve human judgement at crucial points.  The systems responsible for such decisions include

  • People playing various roles
  • “Norms,” including procedures, guidelines, methods, standards.
  • Supporting technologies ranging from pen and paper through sophisticated computers
  • Various aspects of the environment or context of decision making.

For example, a complex organisational decision system produces the monthly interest rate decisions of the Reserve Bank of Australia, as hinted at in this paragraph from their website:

The formulation of monetary policy is the primary responsibility of the Reserve Bank Board. The Board usually meets eleven times each year, on the first Tuesday of the month except in January. Hence, the dates of meetings are well known in advance. For each meeting, the Bank’s staff prepare a detailed account of developments in the Australian and international economies, and in domestic and international financial markets. The papers contain a recommendation for the policy decision. Senior staff attend the meeting and give presentations. Monetary policy decisions by the Reserve Bank Board are communicated publicly shortly after the conclusion of the meeting.

and described in much more detail in this (surprisingly interesting) 2001 speech by the man who is now Governor of the Reserve Bank.

In most cases, decision engineering means taking an existing system and considering to how improve it.  A system can be better in various ways, including:

  • First and foremost, improving the decision hit rate, i.e. the proportion of decisions which are correct in the sense of choosing an optimal or at least satisfactory path of action;
  • More efficient in the sense of using less resources or producing decisions more quickly
  • More transparent or defensible.

Now, in order to improve a particular decision system, a decision engineer might use approaches such as:

  • Bringing standard engineering principles and techniques to bear on making decisions
  • Using more structured decision methods, including the application of decision analysis techniques
  • Basing decisions on “big data” and “data science,” such as predictive analytics

(i.e., the “threads” listed above).  However the usefulness of these approaches will depend very much on the nature of the decision challenges being addressed.  For example, if you want to improve how elite football coaches make decisions in the coaching box on game day, you almost certainly will not introduce highly structured decision methods such as decision trees.

In short, I like this more general definition of decision engineering (in four words or less, building better decision systems) because it seems to get at the essence of what decision engineers do, allowing but not requiring that highly technical, quantitative approaches might be used.  And it accommodates my instinct that much of what we do in our consulting work should indeed count as as a kind of engineering.

Whether we would be wise to publicly describe ourselves as decision engineers is however quite another question – one for marketers, not engineers.