New perspectives through computational simulation

Although wisdom sits at the apex of the knowledge hierarchy, it is often the aspect that is least discussed. Wisdom should be the ultimate goal of any analytical endeavour. Scrutinising data lets us explore structures, spot patterns, and understand relationships. However, insight from data alone is insufficient to make wise decisions when we are confronted by new and unseen scenarios. Why is this the case? And how do we gear our analysis towards obtaining wisdom?

There are four levels on the classic knowledge hierarchy, with each a necessary condition for progress up the pyramid.

The first and most familiar level is data — the raw facts of any system. Next comes information. At this level we give meaning to our data, we understand where it comes from and how it fits together. Third comes knowledge. We begin to understand the context of our information and relate it to the real-world processes which have generated it: we move from observation and organisation of facts, to understanding.

However, merely understanding our environment is not enough. To become better decision makers, be it in our day-to-day lives, or in the boardroom, we need to turn knowledge into wisdom.

Wisdom is what lets us take our knowledge of our system and apply it to previously unseen scenarios in order to make the right call.

We train our fighter pilots in flight simulators. These accurate representations of real flight give learners room to fail cheaply and safe from danger. Having fighter pilots gain their wisdom through experience would be too costly, both in financial terms, and also in terms of human lives.

Simulators provide an easy and cost-effective way to train pilots against any number of scenarios. By changing the settings on the simulator, the pilot can train repeatedly against low-probability, high-impact scenarios — such as engine failure — that they might expect to encounter at most once in a career. Learning to fly a fighter jet is a costly domain. However, as the earlier examples earlier prove, so too is learning to run a corporate from the boardroom.

A flight simulator is simply an accurate model of the physics of a real aircraft: a recreation of thrust, lift, and aerodynamic properties. This is coupled with a visualisation layer that mimics how pilots see the world when it comes to flying.

We can think about building ‘enterprise simulators’ in the same way. Firstly we need to build accurate models of our domain — to create a virtual environment. And then we need useful visualisations to explore that environment.

Let’s explore these two steps in a bit more detail:

These models allow us to build rich and expressive models which are grounded in the micro foundations of a system. They also provide large and rich parameter spaces which we can explore to study what-if? scenarios. For a bank, we can study what happens if depositors withdraw their funds, if the central bank changes policy, or what happens if the bank were to change its pricing strategy.

These types of questions are very hard to answer for previously unobserved scenarios using traditional approaches. This is where we find need for coupling agent-based models with massive-scale simulation — to generate millions of realistic possible scenarios. This empowers decision-makers who are faced with an unpredictable future to move beyond knowledge of the past towards wise decisions.

The visualisations that boardroom executives and corporate decision-makers have to hand don’t need to be as rich and immersive as they do for fighter pilots. Most of the time, standard statistical visualisations convey the information required to make informed decisions. The goal is to couple high-fidelity simulation with a rich and informative visualisation layer.

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