A model is a formal, abstract, hypothetical description of a complex entity, system or process. Any system can be represented through a number of different models, depending on the level of abstraction required and the intention behind how the model is to be used. Examples of everyday models include climatic models as expressed in weather forecasts, models of ecosystems, economies, stock market simulations and other complex systems. While you can expect to study many different models during your career, it is important to understand the following principles about all models:

• A model is an attempt to explain the dynamics of "what is going on". It can never really explain what is really "going on".

• A Useful Model that is shared, openly discussed and deconstructed leads to a common understanding and insight.
• The most powerful and meaningful models are metaphorical and are commonly used in everyday speech – they describe a complex system in terms of analogy eg. Our company is like a busy beehive / a Roman slave galley / a Circus / a well oiled machine etc..
• Models should be evaluated for usefulness rather than truth. What is useful is not necessarily true and what is true is not necessarily useful.
• Models change over time – old models fall into disuse and new models are continuously being developed. You can expect to see many different management models and fads during your career. Remember, these will come and go.
• Every model has its own unique terminology and language. As such, models can be said to be "semantic models" in that they convey meaning.
• If a model becomes popular and sufficient numbers of people start to use it as a way of seeing the world, it will tend to impose its "truth" upon the world. Any competitive advantages conveyed by the model become eroded as more organisations adopt it.
• It is particularly useful to look at models from a number of different fields of study and to create your own models in order to help you understand.

Characteristics of good models include:

• Keep it simple - simple notation, representation of the system with few rules and symbols, no new skills should be necessary for the user to understand the model.

• Ensure the number of variables or elements in the model does not exceed 9 – Having more than 9 variables or factors in the model will tend to make it too complex and difficult to understand and convey.
• Reliability – can the model be applied on a number of different occasions and reliably reflect the behaviour of the system under investigation? In addition, can the model stand up to "shock testing" which involves improbable/borderline situations being presented to it?
• Validity – does the model reflects/measure the dynamics which it is supposed to reflect/measure? Are there other dynamics of which you might be unaware?

“The range of what we think and do / is limited by what we fail to notice / and because we fail to notice / that we fail to notice / there is little we can do / to change / until we notice / how failing to notice / shapes our thoughts and deeds” (R.D. Laing)

From: Art and Science of Change - A Resource for Management and Leadership - (ISBN-978-0-9802550-3-4) Ubuhibi Media Catalogue