SCIENCE

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Modelling Play explores ideas from systems science in an interactive performance setting. By managing a music festival through a series of challenges and disasters, the audience are introduced to a range of concepts from the science of complex adaptive systems. These concepts include:

  • How complex adaptive systems (like a human body, like an ecosystem, like a music festival) are made up of sub-systems that have their own behaviours and properties;
  • How the different parts of a system are interconnected, and how those links can often operate in surprising and unexpected ways;
  • How it’s impossible to look at one part of a system in isolation – if you want to understand a system you need to look at the whole picture, all the parts and their interacting behaviour;
  • The ways in which managing a system is all about trade-offs and compromises – how squeezing the most out of one part of the system will often involve making sacrifices somewhere else;
  • That a complex system – especially one involving groups of human beings – will often involve different stakeholders who want and value different things from the system, and you need to understand and keep in balance those different priorities if you want to keep the system flourishing;
  • The idea of a feedback loop – how some parts of the system feed into other parts which feed back again, and so on, and how those loops can sometimes get out of hand. Managing a system often involves trying to dampen feedback loops before they get out of control, and the music festival provides a couple of nice examples of this behaviour;
  • The idea of Resilience – what is it that allows systems to absorb disturbances in some cases but not others? How a system can easily absorb a whole series of shocks and then suddenly collapse – for example, capably handling a hundred hours of rainfall and then abruptly falling over on the hundred and first;
  • How complex adaptive systems take place on different scales – and how often dealing with a problem or understanding an issue is a matter of viewing it at the right scale;
  • The idea of the Tragedy of the Commons, or a common pool resource problem – what happens when a group of stakeholders are each making use of a single resource and there is a temptation on everyone to take more than their share?

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Modelling

In order to convey these concepts, the show constructs a model of a music festival. The methodology and structure of the modelling draws on a research report produced by David Finnigan as part of his creative research residency at the University College London Environment Institute in 2011. This research report is available for free download from the UCL website. The following content is extracted from it.

What is a model?

A model is a mental or formal representation of a system which is used to anticipate its future behaviour. When we store information from the past and use it to predict the behaviour of the future, we are modelling.

Modelling is a universal activity. All living creatures store information from the past and from it extract regularities. These regularities are a model of the environment which that creature uses to anticipate the future.

‘Whether it is a tree responding to shortening day length by dropping its leaves and preparing its metabolism for winter – in advance of winter – or a naked Pleistocene ape storing food in advance of winter for the same reasons, both are using models.’

As Joshua Epstein points out, ‘Anyone who ventures a projection, or imagines how a social dynamic – an epidemic, war, or migration – would unfold is running some model… when you close your eyes and imagine an epidemic spreading, or any other social dynamic, you are running some model or other. It is just an implicit model that you haven’t written down.’

Because we all use models to help imagine the future, the question is not ‘Should we use models?’ but ‘How do different models compare with each other?’

What are models for?

There are a number of reasons to construct models. Every scientist who creates a model will have a different specific goal or set of outcomes. However, most models are constructed to achieve one or more of these goals:
•    Prediction – To create predictive scenarios that allow us to prepare for the future;
•    Understanding – To illuminate the workings of the system being modelled;
•    Training – To teach skills and attitudes useful for dealing with complex systems.

Prediction
Models are frequently used to predict the future behaviour of systems. Most famously, climate and weather simulations use current meteorological data to make predictions about  climate and weather in the future. These models have become a vital part of our everyday life, and a key tool in the effort to understand the phenomenon of climate change.

Other predictive models attempt to anticipate the behaviour of the stock market, of social trends or of the spread of disease, with varying success.

Importantly, predictive models rarely allow us to anticipate an exact behaviour or event, but rather an estimation of its likely limits. Geophysical models, for instance, cannot predict accurately where and when large earthquakes will occur, but they are able to predict the geographical areas in which large earthquakes can be expected.

As Pascal Perez puts it, ‘We use models as tools to reduce uncertainty.’

Understanding
Whether or not they produce reliable predictions of the future, models can be valuable aids to understanding the ways in which a system functions.

Building a model of a system entails making assumptions about the important components of that system and the relationships between them. Checking the model’s behaviour against the behaviour of the real-world system is a good way of testing the veracity of your assumptions.

Joshua Epstein’s ‘Why Model?’ lists a variety of other ways in which modelling can improve our understanding of the systems we model: they can guide data collection, illuminate core dynamics, suggest dynamical analogies, reveal new questions, illuminate core uncertainties and challenge prevailing theories through perturbations.

Training

Facilitating communication
One issue faced by people seeking to study and respond to complex problems is that people often enter into dialogue with their opinions and attitudes already fixed. In these situations, explicit models can allow for the engagement of different parties by providing an objective platform for debate, communication and collaboration.

Provide people with useful cognitive attitudes
As well as facilitating communication between people from various backgrounds, models can also serve to educate policy-makers and the general public by promoting a scientific state of mind.

Bradbury et al. list a variety of cognitive attitudes, crucial to effective decision making, which can be identified and trained via models. These include the ability to interpret outcomes against expectations, to balance emotional responses (eg. humility, curiosity, frustration and blame-shifting), to tolerate high levels of uncertainty, acknowledge mistakes, to search for counter-evidence and to usefully self-reflect.

Assist in formulating responses to complex problems
Models can also assist in formulating responses to specific problems and crises. An appropriate model can offer crisis options in near-real time, demonstrate trade-offs, suggest efficiencies, and help keep the dialogue focused on priorities.

Models of physical processes play an important role in planning the use of  finite resources like water and primary energy.  Demographic models and projections are fundamental inputs to much government infrastructure planning.  Economic modelling has become an essential part of national and international planning in areas like tax, trade, overseas aid and development and intergenerational equity.