Objective and approach
We have built and implemented a simplified, general mathematical model of an organisation in order to simulate it on a computer and derive useful, practical conclusions.
Our objective is to define a formal language and model of an organisation, in order to observe the appearance of potential emergent system properties and their drivers. Based on that, we wish to establish a predictive, not merely descriptive, taxonomy of organisation-level behaviours that could be useful to managers. We wish to clearly define and understand the impact of key attributes of organisations that drive their behaviour, in order to provide, at a minimum, sufficient grounds to guide the intuition of real-life managers and build better tools to support decision-making.
We also want to ensure that our final, actionable conclusions and recommendations are experimentally validated over and above what a typically low N set of case studies could support.
To do this, we have built a mathematical model and implemented it in a set of tools in code that allow for simulation of organisational behaviours.
Structure of these pages
We have tried to keep the structure of these pages clear, despite the barrier of mathematical language.
- We begin by defining the core elements of the model: the Agent set, the Plant, the Reporting System and the Board; and the signal flows between them
- We then proceed to propose our key model of Agent priority setting, thus providing a definition of three key, potentially quantifiable, sets of structural variables shaping organisation behaviour: influence, judgment and incentives
- We explain how this model can be used to explore organisation dynamics under different parameters and configurations, and study the interplay of the “forces”
Side note: a delicate balance
A complex mathematical model is irrelevant if it is not both practically applicable. This requires that its key points and conclusions are, ultimately, easy to communicate.
We are very conscious of the fact that business frameworks need to be, above all, practical (Exhibit 1). Business theory, with the exception of the field of finance, tends to avoid mathematics like the plague. But, despite the necessary ultimate need for simplicity, we cannot completely ignore the powerful tools of abstraction and inference that the language of mathematics has to offer, at least as a starting point.
Exhibit 1:
If it is too complex to explain, it’s probably unhinged
Source:
It’s Always Sunny In Philadelphia (FX)
Any model of the real world is, in a way, a toy. A toy fire engine allows a child to learn about fire engines, study their parts, assess their relative size and function and use their imagination to create scenarios and stories that they then test in play. The toy also allows the child to consider the differences between their replica and a real engine every time they see a real one on the street. Similarly, our model is a simplified version of the real world that allows us to run experiments in silica and acquire useful intuition about the real world.
In these pages, we try our best to ensure that the ideas that we explore mathematically are also illustrated in clear, common-sense terms in summary commentary and through links to clarifying blog posts explaining the intuition behind the formulas.
We are conscious that we are trying to strike a very difficult balance between robustness and communicable intuition in these pages.